{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing survival curves\n",
    "\n",
    "## Introduction\n",
    "\n",
    "Survival analysis is more than just tracking mortality; it's about understanding the time until any defined event occurs, be it disease progression, mechanical failure, or even customer churn. In this chapter we delve into the powerful tools and techniques used to compare survival experiences, empowering us to discern crucial patterns and differences in time-to-event data.\n",
    "\n",
    "From creating insightful visualizations of survival patterns with Kaplan-Meier curves to rigorously testing for differences using the log-rank test, and further exploring the complexities of survival data through both semi-parametric Cox PH models and parametric Weibull regression, the [lifelines library in Python](https://lifelines.readthedocs.io/en/stable/index.html) provides an accessible and powerful platform for conducting comprehensive survival analyses."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prednisolone case study\n",
    "\n",
    "We'll apply the Kaplan-Meier method to a real-world dataset. Inspired by an example from [Intuitive Biostatistics 4th Edition book by Harvey Motulsky](https://global.oup.com/ushe/product/intuitive-biostatistics-9780190643560), we reanalyze the data from a prospective controlled trial of prednisolone therapy in hepatitis B surface antigen negative chronic active hepatitis, as discussed by [Altman and Bland](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1113717/). These patients were randomized into two distinct groups; one received treatment with *prednisolone* and the other received a placebo.\n",
    "\n",
    "This example will serve as the bedrock for our exploration of advanced survival analysis techniques, including hazard ratio estimation and more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lifelines version: 0.29.0\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import lifelines\n",
    "\n",
    "print('lifelines version:', lifelines.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first prepare the data by ensuring durations and censoring indicators are in the correct format. We simply enter the table from the original article manually into a DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame({\n",
    "    'T': [  # Time in months\n",
    "        2,3,4,7,10,22,28,29,32,37,40,41,54,61,63,71,127,140,146,158,167,182,           # control\n",
    "        2,6,12,54,56,68,89,96,96,125,128,131,140,141,143,145,146,148,162,168,173,181,  # PRED\n",
    "    ],\n",
    "    'E': [\n",
    "        1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,   # 1:death\n",
    "        1,1,1,1,0,1,1,1,1,0,0,0,0,0,1,0,1,0,0,1,0,0,   # 0:censored\n",
    "    ],\n",
    "    'PRED': [False]*22 + [True]*22,  # encode the treatment group as True or False\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T</th>\n",
       "      <th>E</th>\n",
       "      <th>PRED</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    T  E   PRED\n",
       "0   2  1  False\n",
       "1   3  1  False\n",
       "2   4  1  False\n",
       "3   7  1  False\n",
       "4  10  1  False"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling dates in survival analysis\n",
    "\n",
    "In real-world scenarios, survival data often involves dates rather than numeric durations. To accommodate this, we'll demonstrate how to convert dates into a format compatible with lifelines.\n",
    "\n",
    "First, we ensure our dataset includes a censoring indicator. Here, we assume that non-null values in the 'Death' column represent events (deaths), while nulls indicate censoring:\n",
    "\n",
    "```python\n",
    "from datetime import datetime\n",
    "import numpy as np\n",
    "\n",
    "data['Censored'] = data['Death'].notnull().astype(np.int)\n",
    "```\n",
    "\n",
    "Next, we calculate durations in days from a reference date ('D0') for both events and censored observations:\n",
    "\n",
    "```python\n",
    "data['Days'] = np.where(\n",
    "    data['Censored'] == 1,\n",
    "    data['Death'] - data['D0'],\n",
    "    datetime.now() - data['D0'])\n",
    "```\n",
    "\n",
    "For scenarios where we start with pure date strings, lifelines offers a convenient helper function:\n",
    "```python\n",
    "from lifelines.utils import datetimes_to_durations\n",
    "\n",
    "start_date= ['2013-10-10 0:00:00', '2013-10-09', '2013-10-10']\n",
    "end_date = ['2013-10-13', '2013-10-10', None]\n",
    "\n",
    "T, E = datetimes_to_durations(\n",
    "    start_date,\n",
    "    end_date, \n",
    "    fill_date='2013-10-15') \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kaplan-Meier analysis\n",
    "\n",
    "In the following code, we're performing a Kaplan-Meier survival analysis to visualize and compare the survival experiences of the two patient groups. We use the `KaplanMeierFitter` from the lifelines library to fit survival curves for each group and plot them on the same graph. We also customize the plot's appearance for clarity and visual appeal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lifelines import KaplanMeierFitter\n",
    "\n",
    "kmf_ctrl = KaplanMeierFitter(label='placebo (KM)')\n",
    "kmf_pred = KaplanMeierFitter(label='prednisolone (KM)')\n",
    "\n",
    "# Define censor styles once for reuse\n",
    "censor_styles = {'marker': 2, 'ms': 6}\n",
    "\n",
    "# Filter data directly within the .fit() call\n",
    "# Fit and plot for prednisolone group\n",
    "kmf_pred.fit(\n",
    "    durations=data.loc[data['PRED'], 'T'],\n",
    "    event_observed=data.loc[data['PRED'], 'E'],\n",
    "    # label='prednisolone (KM)',\n",
    ")\n",
    "ax = kmf_pred.plot_survival_function(\n",
    "    show_censors=True,\n",
    "    lw=2,\n",
    "    ci_alpha=.1,\n",
    "    censor_styles=censor_styles)\n",
    "\n",
    "# Extract median survival times of the prednisolone group\n",
    "median_survival_pred = kmf_pred.median_survival_time_\n",
    "\n",
    "\n",
    "# Fit and plot for control placebo group on the same axes\n",
    "kmf_ctrl.fit(data[~data['PRED']]['T'], data[~data['PRED']]['E'])\n",
    "kmf_ctrl.plot_survival_function(\n",
    "    ax=ax,\n",
    "    show_censors=True,\n",
    "    lw=2,\n",
    "    ci_alpha=.1,\n",
    "    censor_styles=censor_styles)\n",
    "\n",
    "# Extract median survival times of the placebo group\n",
    "median_survival_ctrl = kmf_ctrl.median_survival_time_\n",
    "\n",
    "# Customize plot appearance\n",
    "plt.xticks(fontsize=11)\n",
    "plt.xlabel('Months of follow-up', fontdict={'size': 12})\n",
    "plt.yticks(fontsize=11)\n",
    "plt.ylabel('Proportion survival', fontdict={'size': 12})\n",
    "plt.title(\n",
    "    'Kaplan-Meier analysis',\n",
    "    fontdict={'size': 14, 'weight': 'bold'})\n",
    "\n",
    "plt.tight_layout();\n",
    "# plt.savefig('survival.svg');\n",
    "# It's also possible to generate a Plotly plot using the .mpl_to_plotly()\n",
    "# method, see https://plot.ly/ipython-notebooks/survival-analysis-r-vs-python/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Kaplan-Meier curves visually depict the survival experiences of the two groups. Both curves initially start at 100%, indicating that all patients are alive at the beginning of the study. However, they diverge notably over time. The separation between the curves, particularly during the initial months, strongly suggests that the treatment had a positive impact on survival."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Beyond the curves\n",
    "\n",
    "The Kaplan-Meier curves paint a visual story, but the `KaplanMeierFitter` object holds a treasure trove of insights beyond the plot. Let's delve deeper into its capabilities, extracting key metrics and making predictions to enhance our understanding of the survival patterns.\n",
    "\n",
    "#### Median survival\n",
    "\n",
    "The median survival time, the point at which 50% of the subjects have experienced the event (in this case, death), is a commonly used summary statistic in survival analysis. We can calculate the ratio of median survival times between the two groups to quantify the treatment's effect. Additionally, under the assumption of proportional hazards, we can estimate a 95% confidence interval for this ratio, providing a measure of uncertainty around our estimate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median survival time for prednisolone group: 146.0\n",
      "Median survival time for control group: 40.0\n"
     ]
    }
   ],
   "source": [
    "print(f\"Median survival time for prednisolone group: {median_survival_pred}\")\n",
    "print(f\"Median survival time for control group: {median_survival_ctrl}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Survival predictions\n",
    "\n",
    "While median survival time is valuable, other statistics like mean time until death or five-year survival might not always be as informative in survival analysis. This is because these statistics can be heavily influenced by censoring, where some patients are lost to follow-up or the study ends before they experience the event.\n",
    "\n",
    "Nevertheless, we can easily predict the survival probability at specific time points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Survival prediction at month 60:\n",
      "Group placebo (KM):\t0.4\n",
      "Group prednisolone (KM):0.8\n"
     ]
    }
   ],
   "source": [
    "month=5*12  # 5-year survival\n",
    "print(f\"Survival prediction at month {month}:\")\n",
    "print(f\"Group {kmf_ctrl._label}:\\t{kmf_ctrl.predict(month):.1f}\")\n",
    "print(f\"Group {kmf_pred._label}:{kmf_pred.predict(month):.1f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Survival function\n",
    "\n",
    "Provides the estimated probability of surviving beyond any given time point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          prednisolone (KM)\n",
      "timeline                   \n",
      "0.0                1.000000\n",
      "2.0                0.954545\n",
      "6.0                0.909091\n",
      "12.0               0.863636\n",
      "54.0               0.818182\n"
     ]
    }
   ],
   "source": [
    "print(kmf_pred.survival_function_.head())  # Series returned"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Non-parametric log-rank test\n",
    "\n",
    "### A little bit of theory\n",
    "\n",
    "In survival analysis, **non-parametric** tests shine when we make no assumptions about the underlying shape of the survival curves. They offer flexibility, especially when the data doesn't neatly fit into predefined distributions. In contrast, **parametric** tests rely on specific distributional assumptions (e.g., Weibull, exponential) to model the survival data.\n",
    "\n",
    "The log-rank test, also known as the **Mantel-Cox test**, evaluates the null hypothesis that there's *no difference in survival between two or more groups*: $S_A(t) = S_B(t) = \\dots $. It essentially compares the observed number of events (e.g., deaths) in each group at each distinct event time to the number of events we would expect if there were no difference in survival between the groups. It then combines these comparisons across all event times to assess the overall evidence against the null hypothesis of equal survival. It's step-by-step breakdown can be summarized as follows:\n",
    "\n",
    "1. Pooling the data: the log-rank test starts by pooling all the event times from all groups, creating a combined timeline of events.\n",
    "2. Calculating expected events: at each event time on this combined timeline, the test calculates the expected number of events for each group. This is done under the null hypothesis that the survival curves are the same for all groups. The expected number of events for a group at a particular time point is based on:\n",
    "   - The total number of individuals at risk (i.e., still alive and uncensored) at that time point across all groups.\n",
    "   - The proportion of individuals at risk in that specific group at that time point.\n",
    "   - The total number of events observed at that time point across all groups.\n",
    "3. Comparing observed and expected: the test then compares the observed number of events in each group at each event time to the expected number of events. It quantifies the discrepancies between observed and expected events across all time points using a test statistic (often a **chi-squared statistic**).\n",
    "4. Calculating the P value: the test statistic is used to calculate a P value, which represents the probability of observing a difference in survival as extreme as (or more extreme than) the one actually observed, if the null hypothesis were true (i.e., if there were no real difference in survival between the groups).\n",
    "5. Interpreting the P value. The P value answers this question:\n",
    "    > If the null hypothesis is true (i.e., there's no real difference in survival between the groups), what is the probability of observing a difference in survival as extreme as (or more extreme than) the one we actually observed, just by random chance?\n",
    "   - A small P value (typically < 0.05) suggests that the observed difference in survival is unlikely to have occurred by chance alone, leading to the rejection of the null hypothesis. We conclude that there's a statistically significant difference in survival between the groups.\n",
    "   - A large P value suggests that the observed difference could be due to chance, and we fail to reject the null hypothesis. We don't have enough evidence to conclude a significant difference in survival.\n",
    "\n",
    "### Using log-rank test in Python\n",
    "\n",
    "With lifelines, performing the log-rank test is straightforward using `logrank_test`. For multiple groups, consider `pairwise_logrank_test()` to perform all pairwise comparisons between groups, or `multivariate_logrank_test()` to test the overall hypothesis that all groups have the same survival."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   test_statistic         p  -log2(p)\n",
      "0        4.659901  0.030875  5.017419\n"
     ]
    }
   ],
   "source": [
    "from lifelines.statistics import logrank_test\n",
    "\n",
    "# Perform the log-rank test\n",
    "results = logrank_test(\n",
    "    durations_A=data.loc[data['PRED'], 'T'],\n",
    "    durations_B=data.loc[~data['PRED'],'T'],\n",
    "    event_observed_A=data.loc[data['PRED'], 'E'],\n",
    "    event_observed_B=data.loc[~data['PRED'],'E'],\n",
    "    alpha=.95)\n",
    "\n",
    "# Print the summary of the results\n",
    "print(results.summary)\n",
    "\n",
    "# results.print_summary()\n",
    "# results.p_value\n",
    "# results.test_statistic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the current example, the two-tailed P value calculated using the log-rank test is 0.031. This means that if the treatment were truly ineffective, there's only a 3.1% chance that we would have randomly selected patients whose survival experiences differed as much (or more) as they did in this study.\n",
    "\n",
    "### Mantel-Haenszel method\n",
    "\n",
    "Both log-rank (Mantel-Cox) and Manter-Haenszel are non-parametric methods used to compare survival curves between groups. They are very similar in their approach and often yield similar results. [The main difference lies in how they handle ties](https://www.graphpad.com/support/faq/how-do-the-three-methods-compare-to-survival-curves-log-rank-mantel-haenszel-gehan-wilcoxon-differ/) (multiple events occurring at the same time point). The Mantel-Haenszel test makes a slight adjustment for ties, while the log-rank test doesn't. In practice, the difference between the two tests is often negligible, especially with large sample sizes or few ties.\n",
    "\n",
    "### Gehan-Breslow-Wilcoxon method\n",
    "\n",
    "The [Gehan-Breslow-Wilcoxon test](https://www.graphpad.com/guides/prism/latest/statistics/stat_analysis_choices_for_survival.htm) emphasizes differences in survival occurring early in the study, which can be insightful in certain scenarios. However, this emphasis can be misleading if a large proportion of patients are censored early on (i.e., lost to follow-up or the study ends before their event occurs).\n",
    "\n",
    "In contrast, the log-rank test treats all time points equally, making it more statistically powerful when the assumption of proportional hazards holds true. If the Kaplan-Meier curves for the two groups cross, this suggests non-proportional hazards, indicating that the relative risk changes over time. In such cases, alternative tests like the [Peto-Peto Prentice, Tarone-Ware, or Fleming-Harrington tests](https://lifelines.readthedocs.io/en/latest/lifelines.statistics.html) might be more appropriate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>t_0</th>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>null_distribution</th>\n",
       "      <td>chi squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degrees_of_freedom</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_name</th>\n",
       "      <td>Wilcoxon_test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_statistic</th>\n",
       "      <th>p</th>\n",
       "      <th>-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.54</td>\n",
       "      <td>0.01</td>\n",
       "      <td>6.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/latex": [
       "\\begin{tabular}{lrrr}\n",
       " & test_statistic & p & -log2(p) \\\\\n",
       "0 & 6.54 & 0.01 & 6.57 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.StatisticalResult: Wilcoxon_test>\n",
       "               t_0 = -1\n",
       " null_distribution = chi squared\n",
       "degrees_of_freedom = 1\n",
       "             alpha = 0.95\n",
       "         test_name = Wilcoxon_test\n",
       "\n",
       "---\n",
       " test_statistic    p  -log2(p)\n",
       "           6.54 0.01      6.57"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using the Gehan-Breslow-Wilcoxon method\n",
    "results_wilcoxon = logrank_test(\n",
    "    durations_A=data.loc[data['PRED'], 'T'],\n",
    "    durations_B=data.loc[~data['PRED'], 'T'],\n",
    "    event_observed_A=data.loc[data['PRED'], 'E'],\n",
    "    event_observed_B=data.loc[~data['PRED'], 'E'],\n",
    "    alpha=.95,\n",
    "    weightings='wilcoxon')\n",
    "\n",
    "results_wilcoxon.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hazard ratio for quantifying relative risk\n",
    "\n",
    "While Kaplan-Meier curves offer a powerful visual representation of survival experiences, they don't directly reveal the underlying dynamics of risk over time. To gain insights into the instantaneous risk of an event at any given moment, we turn to the **hazard function**, denoted by h(t).\n",
    "\n",
    "In survival analysis, the **hazard** represents the instantaneous **risk** of experiencing the event of interest (e.g., death) at a given time, given that the individual has survived up to that point. Think of it as the \"slope\" of the survival curve - a steeper slope indicates a higher hazard, meaning events are occurring more rapidly.\n",
    "\n",
    "The **hazard ratio (HR)** compares the hazards of two groups, providing a measure of relative risk. If the HR is 2.0, it signifies that the rate of events in one group is twice the rate in the other group at any given time point. In simpler terms, individuals in the first group are experiencing the event at twice the speed of those in the second group.\n",
    "\n",
    "The interpretation of the hazard ratio hinges on the **assumption of proportional hazards**. This assumption states that the *hazard ratio remains constant over time*, regardless of their covariate values. Mathematically: $h_A(t) / h_B(t) = c$, where $h_A(t)$ and $h_B(t)$ are the hazard functions of the treatment groups, and $c$ is a constant.\n",
    "\n",
    "In other words, the relative risk between the two groups stays the same throughout the follow-up period. If this assumption holds, we can confidently say that, for example, patients in one treatment group are consistently experiencing the event at half the rate of patients in the other group, regardless of how much time has passed.\n",
    "\n",
    "However, the proportional hazards assumption isn't always met in real-world scenarios. Consider the comparison between surgery and medical therapy. Surgery often carries a higher initial risk, but the risk may decrease over time as patients recover. In contrast, medical therapy might have a lower initial risk, but the risk could increase as the disease progresses. In such cases, the hazard ratio wouldn't be constant, violating the proportional hazards assumption."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unveiling hazards with the Nelson-Aalen estimator\n",
    "\n",
    "#### The mathematics behind the model\n",
    "\n",
    "We can't simply transform the Kaplan-Meier estimate to obtain the hazard function because statistical intricacies prevent such a straightforward conversion. But we have a robust *non-parametric* tool at our disposal: the Nelson-Aalen estimator. This estimator allows us to estimate the **cumulative hazard function**, H(t), which represents the total accumulated risk of experiencing the event up to time $t$.\n",
    "\n",
    "Mathematically, the cumulative hazard is defined as the integral of the hazard function: $H(t) = \\int_{0}^t \\lambda(z)dz$, where $\\lambda(z)$ represents the hazard function at time $z$.\n",
    "\n",
    "The Nelson-Aalen estimator provides a practical way to estimate $H(t)$ from observed data: $\\hat{H}(t) = \\sum_{t_i \\le t} \\frac{d_i}{n_i}$. In this formula:\n",
    "\n",
    "- $\\hat{H}(t)$ is the estimated cumulative hazard at time $t$.\n",
    "- The summation is over all event times ($t_i$) that are less than or equal to $t$.\n",
    "- $d_i$ is the number of events (e.g., deaths) observed at time $t_i$\n",
    "- $n_i$ is the number of individuals at risk (i.e., still alive and uncensored) just before time $t_i$\n",
    "\n",
    "#### Using the `NelsonAalenFitter`\n",
    "\n",
    "Let's generate a plot showing the cumulative hazard estimates for both the treatment 'PRED' and control groups, allowing us to visualize and compare the accumulation of risk over time between the two."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lifelines import NelsonAalenFitter\n",
    "\n",
    "naf_pred = NelsonAalenFitter()\n",
    "naf_ctrl = NelsonAalenFitter()\n",
    "\n",
    "# Fit Nelson-Aalen estimators for each group\n",
    "naf_pred.fit(\n",
    "    data[data['PRED']]['T'],\n",
    "    event_observed=data[data['PRED']]['E'],\n",
    "    label='prednisolone')\n",
    "naf_ctrl.fit(\n",
    "    data[~data['PRED']]['T'],\n",
    "    event_observed=data[~data['PRED']]['E'],\n",
    "    label='placebo')\n",
    "\n",
    "# Plot cumulative hazard functions\n",
    "ax = plt.subplot(111)\n",
    "naf_pred.plot(ax=ax, color='red')\n",
    "naf_ctrl.plot(ax=ax, color='blue')\n",
    "\n",
    "# Customize plot appearance\n",
    "plt.xlabel('Time (months)')\n",
    "plt.ylabel('Cumulative hazard')\n",
    "plt.title('Nelson-Aalen cumulative hazard estimates');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also access the cumulative hazard data through the `cumulative_hazard_` attribute of the `NelsonAalenFitter` objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cumulative Hazard Data for PRED group:\n",
      "\n",
      "          prednisolone\n",
      "timeline              \n",
      "0.0           0.000000\n",
      "2.0           0.045455\n",
      "6.0           0.093074\n",
      "12.0          0.143074\n",
      "54.0          0.195705\n"
     ]
    }
   ],
   "source": [
    "# Print cumulative hazard data (top 5) for PRED group\n",
    "print(\"\\nCumulative Hazard Data for PRED group:\\n\")\n",
    "print(naf_pred.cumulative_hazard_.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Connection to Kaplan-Meier survival\n",
    "\n",
    "There is a fundamental mathematical relationship between Kaplan-Meier survival curves and Nelson-Aalen cumulative hazard estimates, which can be thought of as a form of symmetry or duality: $S(t) = \\exp[-H(t)]$, where\n",
    "- $S(t)$ is the survival function at time $t$ (estimated by the Kaplan-Meier method)\n",
    "- $H(t)$ is the cumulative hazard function at time $t$ (estimated by the Nelson-Aalen method)\n",
    "\n",
    "This equation shows that the survival probability at any time point is directly related to the cumulative hazard up to that point. This relationship has other implications:\n",
    "- Complementary information: the Kaplan-Meier curve and the Nelson-Aalen plot provide complementary perspectives on the same underlying survival data.\n",
    "- Shape correspondence:\n",
    "  - A decreasing Kaplan-Meier curve (indicating decreasing survival probability) corresponds to an increasing Nelson-Aalen curve (indicating accumulating hazard).\n",
    "  - The steeper the drop in the Kaplan-Meier curve, the steeper the rise in the Nelson-Aalen curve, reflecting a higher hazard rate at that time.\n",
    "- Transformation: we can mathematically transform one into the other. If we have the Kaplan-Meier estimate, we can calculate the cumulative hazard as $H(t) = -\\log[S(t)]$. Conversely, if we have the Nelson-Aalen estimate, we can calculate the survival function as $S(t) = \\exp[-H(t)]$.\n",
    "- If we plot the Kaplan-Meier curve and the Nelson-Aalen curve on the same graph (with appropriate scaling of the y-axes), we'll often observe a *visual symmetry* or mirror-image relationship between the two. This visual correspondence reflects the underlying mathematical connection between survival probability and cumulative hazard."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The link with hazard ratio\n",
    "\n",
    "While the Nelson-Aalen estimator directly estimates the cumulative hazard function, it doesn't provide a *hazard ratio* in the same way that a Cox proportional hazards model does. However, it's still connected to the concept of hazard ratios in a few key ways:\n",
    "\n",
    "1. Visual assessment of proportional hazards\n",
    "   - The Nelson-Aalen plots for two or more groups can visually suggest whether the proportional hazards assumption might hold. If the curves are *roughly parallel* (i.e., maintain a relatively constant vertical distance), it hints at proportional hazards. Conversely, if the curves cross or diverge significantly, it suggests non-proportional hazards.\n",
    "   - This visual assessment can guide us in deciding whether a Cox model (which assumes proportional hazards) is appropriate for further analysis.\n",
    "2. Estimating hazard ratios (with caution)\n",
    "   - Although not directly, we can crudely estimate hazard ratios from Nelson-Aalen plots by comparing the slopes of the cumulative hazard curves at different time points. A steeper slope indicates a higher hazard rate.\n",
    "   - However, this approach is less precise and less reliable than the hazard ratios obtained from a Cox model, especially when the proportional hazards assumption is violated.\n",
    "3. Basis for Cox model diagnostics\n",
    "   - The Nelson-Aalen estimator plays a crucial role in diagnosing the proportional hazards assumption in Cox models.\n",
    "   - One common diagnostic plot involves plotting the log of the Nelson-Aalen cumulative hazard estimates against the log of survival time for each group. If the proportional hazards assumption holds, these plots should be roughly parallel."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cox proportional hazards model\n",
    "\n",
    "While the Weibull model (as discussed later) is a popular parametric choice for survival analysis, sometimes its distributional assumptions might not be met or are difficult to verify. In such cases, researchers often turn to the **Cox proportional hazards (Cox PH) model**, a *semi-parametric approach* that offers flexibility while still allowing for the inclusion of **covariates**.\n",
    "\n",
    "The Cox model expresses the hazard function as $h(t|x) = h_0(t) * \\exp[(x - \\overline{x})' \\beta]$, where:\n",
    "- $h(t|x)$ is the hazard at time $t$ for an individual with covariate values $x$.\n",
    "- $h_0(t)$ is the baseline hazard, representing the hazard when all covariates are at their average values ($\\overline{x}$).\n",
    "- $x$ is the vector of covariate values for the individual.\n",
    "- $(x - \\overline{x})'$ is the transpose (row vector) of $(x - \\overline{x})$, which explicitly shows the deviation of the individual's covariate values from the average values, and then multiplies this deviation with the coefficient vector.\n",
    "- $\\beta$ is the vector of coefficients, quantifying the impact of each covariate on the hazard.\n",
    "\n",
    "The hazard function, $h(t)$, describes the instantaneous risk of the event occurring at time $t$, given that the individual has survived up to that point. Mathematically, it's related to the survival function, $S(t)$, as follows $h(t) = - \\frac{d}{dt} \\log S(t)$.\n",
    "\n",
    "A simplified representation of the Cox model is: $h(t)=h_0(t) \\times \\exp(b_1 x_1 + b_2 x_2 + ... + b_p x_p)$, where:\n",
    "- $t$ represents the survival time\n",
    "- $h(t)$ is the hazard function. *The $t$ in $h(t)$ reminds us that the hazard may vary over time.*\n",
    "- $x_1, x_2, \\dots, x_p$ are the covariates.\n",
    "- $b_1, b_2, \\dots, b_p$ are the coefficients representing the effect of each covariate on the hazard.\n",
    "- $h_0$ is called the *baseline hazard*. It corresponds to the value of the hazard if all the $x_i$ are equal to zero, with $\\exp(0) = 1$.\n",
    "\n",
    "The coefficients ($b_i$) indicate how each covariate influences the hazard, e.g., a positive coefficient implies that an increase in the covariate value is associated with an increased hazard (and thus a decreased survival probability).\n",
    "\n",
    "We can therefore appreciate the advantages of the Cox Model:\n",
    "- Semi-parametric: it doesn't require assumptions about the shape of the baseline hazard function.\n",
    "- Handles covariates: it allows for the inclusion of multiple covariates (both categorical and continuous) to adjust for their effects on survival.\n",
    "- Provides hazard ratios: it directly estimates hazard ratios, which are easily interpretable measures of relative risk.\n",
    "\n",
    "The expression $h(t|x) = b_0(t) \\exp[\\sum_{i=1}^n (b_i * x_i)]$ is another way to represent the Cox proportional hazards model, although it has a few subtle differences and potential points of confusion compared to the more standard notation. It uses $\\sum_{i=1}^n (b_i * x_i)$ which directly calculates the linear combination of covariates and their coefficients without explicitly showing the deviation from the average."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Interpreting the Cox PH model\n",
    "\n",
    "The summary of the Cox proportional hazards model typically includes the following key components for each covariate:\n",
    "- **coef**: this represents the estimated coefficient ($\\beta$) for the covariate in the Cox model.\n",
    "- **exp(coef)**: this is the exponentiated coefficient, which represents the **hazard ratio** associated with a one-unit increase in the covariate, holding all other covariates constant.\n",
    "  - If 'exp(coef)' > 1: a one-unit increase in the covariate is associated with an increased hazard (i.e., a higher risk of the event occurring) by a factor of 'exp(coef)' compared to the baseline hazard.\n",
    "  - If 'exp(coef)' < 1: a one-unit increase in the covariate is associated with a decreased hazard (i.e., a lower risk of the event occurring) by a factor of '1/exp(coef)' compared to the baseline hazard.\n",
    "- **p-value**: this indicates the statistical significance of the covariate's effect on the hazard. A small P value (typically < 0.05) suggests that the covariate is a significant predictor of survival."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.CoxPHFitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration col</th>\n",
       "      <td>'T'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event col</th>\n",
       "      <td>'E'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baseline estimation</th>\n",
       "      <td>breslow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of observations</th>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events observed</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partial log-likelihood</th>\n",
       "      <td>-86.620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2024-11-17 15:38:33 UTC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th style=\"min-width: 12px;\"></th>\n",
       "      <th style=\"min-width: 12px;\">coef</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">se(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">coef lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">coef upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">cmp to</th>\n",
       "      <th style=\"min-width: 12px;\">z</th>\n",
       "      <th style=\"min-width: 12px;\">p</th>\n",
       "      <th style=\"min-width: 12px;\">-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PRED</th>\n",
       "      <td>-0.831</td>\n",
       "      <td>0.436</td>\n",
       "      <td>0.397</td>\n",
       "      <td>-1.609</td>\n",
       "      <td>-0.052</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.949</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-2.091</td>\n",
       "      <td>0.037</td>\n",
       "      <td>4.773</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><br><div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Concordance</th>\n",
       "      <td>0.633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partial AIC</th>\n",
       "      <td>175.239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood ratio test</th>\n",
       "      <td>4.471 on 1 df</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-log2(p) of ll-ratio test</th>\n",
       "      <td>4.859</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "\\begin{tabular}{lrrrrrrrrrrr}\n",
       " & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\n",
       "covariate &  &  &  &  &  &  &  &  &  &  &  \\\\\n",
       "PRED & -0.831 & 0.436 & 0.397 & -1.609 & -0.052 & 0.200 & 0.949 & 0.000 & -2.091 & 0.037 & 4.773 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.CoxPHFitter: fitted with 44 total observations, 17 right-censored observations>\n",
       "             duration col = 'T'\n",
       "                event col = 'E'\n",
       "      baseline estimation = breslow\n",
       "   number of observations = 44\n",
       "number of events observed = 27\n",
       "   partial log-likelihood = -86.620\n",
       "         time fit was run = 2024-11-17 15:38:33 UTC\n",
       "\n",
       "---\n",
       "            coef exp(coef)  se(coef)  coef lower 95%  coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n",
       "covariate                                                                                                   \n",
       "PRED      -0.831     0.436     0.397          -1.609          -0.052               0.200               0.949\n",
       "\n",
       "           cmp to      z     p  -log2(p)\n",
       "covariate                               \n",
       "PRED        0.000 -2.091 0.037     4.773\n",
       "---\n",
       "Concordance = 0.633\n",
       "Partial AIC = 175.239\n",
       "log-likelihood ratio test = 4.471 on 1 df\n",
       "-log2(p) of ll-ratio test = 4.859"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lifelines import CoxPHFitter\n",
    "\n",
    "# Fit the CoxPHFitter\n",
    "cph = CoxPHFitter()\n",
    "cph.fit(df=data, duration_col=\"T\", event_col=\"E\", formula='PRED')\n",
    "\n",
    "# Print the summary to see the coefficient and its significance\n",
    "cph.print_summary(decimals=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, the hazard ratio equals 0.436, with a 95% confidence interval ranging from 0.200 to 0.949. This indicates that, at any given time, individuals in the treated group ('PRED' = True) have a hazard rate that is approximately 43.6% of the hazard rate in the control/reference group ('PRED' = False), assuming the proportional hazards assumption holds. In simpler terms, this suggests that treated patients are experiencing death at a significantly lower rate than control patients throughout the follow-up period.\n",
    "\n",
    "Moreover, the 95% confidence interval for the HR does not include 1.0. This indicates that we can be 95% confident that the true hazard ratio in the population is not 1.0, i.e., the hazards are not equal between the two groups. This provides evidence for a statistically significant difference in survival experiences between the treated and control groups.\n",
    "If the two survival curves were truly identical, the hazard ratio would indeed be 1.0. The fact that our confidence interval excludes 1.0 strengthens our conclusion that the treatment likely has a real effect on survival."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Assessing the proportional hazards assumption\n",
    "\n",
    "To ensure the validity of our model and the accuracy of these interpretations, it's essential to check whether the proportional hazards assumption is reasonably met. The lifelines library provides a convenient method, [`check_assumptions()`](https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html), to assist in this assessment. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "   Bootstrapping lowess lines. May take a moment...\n",
      "\n",
      "Proportional hazard assumption looks okay.\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cph.check_assumptions(\n",
    "    training_df=data,\n",
    "    p_value_threshold=.05,  # default 0.01\n",
    "    show_plots=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizing the impact of covariate on survival\n",
    "\n",
    "The Cox PH model allows us to estimate the effect of each covariate on the hazard rate. But how does this translate into actual changes in survival probabilities? The `plot_partial_effects_on_outcome` function provides a visual answer.\n",
    "\n",
    "This function generates a plot showing the predicted survival curves for different values of a specific covariate ('PRED' in our case), while holding all other covariates (if any) constant at their average (or other specified) values. In essence, it lets us see how changing the value of 'PRED' would impact the survival experience, assuming everything else remains the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the effect of the predictor on survival curves\n",
    "cph.plot_partial_effects_on_outcome(\n",
    "    covariates='PRED',\n",
    "    values=[False, True],\n",
    "    plot_baseline=True,\n",
    ")\n",
    "plt.title(\"Effect of 'PRED' on survival\")\n",
    "plt.xlabel('Time (months)')\n",
    "plt.ylabel('Survival probability');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the context of a Cox PH plot, the **baseline curve** represents the estimated survival function when all covariates in the model are set to their baseline or reference levels. The specific baseline level for each covariate depends on how it is coded in the model:\n",
    "- Continuous covariates: the baseline level is typically the *mean or median* value of the covariate in the dataset.\n",
    "- Categorical covariates: the baseline level is usually the *reference category* (e.g., the \"control\" group or the most common category).\n",
    "\n",
    "_It's important to note that in the current version of lifelines, when dealing with categorical covariates, the reference or control group used for establishing the baseline is determined by the **first category encountered in the dataset** when the model is fit. In our case, since the first patient in the dataset has 'PRED = False', the baseline hazard and survival functions represent the untreated or control group. If 'PRED' were encoded numerically as 0 and 1, then 0 (representing the absence of prednisone treatment) would naturally become the baseline. Similarly, if the very first patient in the dataset had been assigned 'PRED = True', then the baseline would correspond to the prednisone-treated group._\n",
    "\n",
    "This enhanced explanation provides a more accurate and nuanced understanding of how lifelines handles baseline selection for categorical covariates, which is crucial for interpreting the model results correctly. \n",
    "\n",
    "_Of note, in the current version of lifelines, the reference/control group chosen for the baseline is the first category of the dataset, i.e. in our example, 'PRED = False'. If the very first patient of the dataset was set as 'PRED = True', then the baseline would be the prednisone group._\n",
    "\n",
    "Essentially, the baseline curve shows the survival experience we would expect for an \"average\" individual or an individual belonging to the reference categories of all categorical covariates, assuming the proportional hazards assumption holds.\n",
    "\n",
    "Why do the curves from both groups have the same shape? This is a direct consequence of the proportional hazards assumption, which is the core principle underlying the Cox PH model. The proportional hazards assumption states that the hazard ratio between any two individuals remains constant over time, regardless of their covariate values.\n",
    "\n",
    "Mathematically, this means that the hazard function for one group is simply a *constant multiple of the hazard function for another group*. When plotting the survival curves, this constant proportionality translates into curves that have the same basic shape but are vertically shifted relative to each other. The vertical shift reflects the difference in hazard (and thus survival probability) between the groups due to their different covariate values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Weibull parametric model\n",
    "\n",
    "Having explored non-parametric and semi-parametric approaches, let's now turn our attention to a fully **parametric** model for survival analysis. The Weibull model is a popular parametric model in survival analysis due to its flexibility in capturing various hazard patterns.\n",
    "\n",
    "The Weibull distribution, [originally applied to model particle size distribution](https://en.wikipedia.org/wiki/Weibull_distribution), is a continuous probability distribution that models time-to-event data. Its probability density function is: $f(x; \\lambda, \\rho) = \\frac{k}{\\lambda}(\\frac{x}{\\lambda})^{\\rho-1} \\exp{-(x/\\lambda)^k}, x \\ge 0, \\lambda > 0, \\rho > 0$, where $\\lambda$ determines the scale and $\\rho$ the shape of the distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 700x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import weibull_min\n",
    "\n",
    "# Create a range of values for x\n",
    "x = np.arange(0, 5, 0.01)\n",
    "\n",
    "# Create the 1x2 subplot grid\n",
    "fig, axes = plt.subplots(1, 2, figsize=(7, 3))\n",
    "\n",
    "# Left subplot: varying lambda with rho = 2\n",
    "for scale in [1, 2, 3]:\n",
    "    pdf = weibull_min.pdf(x, 2, scale=scale)\n",
    "    axes[0].plot(x, pdf, lw=2, label=f'λ={scale}')\n",
    "\n",
    "axes[0].set_title('Weibull PDF (ρ =2)')\n",
    "axes[0].set_xlabel('x')\n",
    "axes[0].set_ylabel('Probability density')\n",
    "axes[0].legend()\n",
    "\n",
    "# Right subplot: varying rho with lambda = 1\n",
    "for rho in [0.5, 1, 2]:\n",
    "    pdf = weibull_min.pdf(x, rho, scale=1)\n",
    "    axes[1].plot(x, pdf, lw=2, label=f'ρ={rho}')\n",
    "\n",
    "axes[1].set_title('Weibull PDF (λ=1)')\n",
    "axes[1].set_xlabel('x')\n",
    "axes[1].set_ylabel('Probability Density')\n",
    "axes[1].legend()\n",
    "\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Weibull distribution provides a parameterized form for the survival function: $S(t)=\\exp[-(t / \\lambda)^\\rho]$, where:\n",
    "- $S(t)$: the survival function, representing the probability of surviving beyond time $t$.\n",
    "- $\\lambda$ (lambda): the *scale* parameter, which influences the time scale of the survival curve:\n",
    "  - It has a direct and intuitive interpretation: it represents the time at which 63.2% of the population has experienced the event of interest (e.g., death).\n",
    "  - A larger $\\lambda$ indicates a longer time until 63.2% of the population experiences the event, suggesting a slower event rate overall.\n",
    "- $\\rho$ (rho): the *shape* parameter, which controls the shape of the hazard function and, consequently, the survival curve:\n",
    "  - It governs the shape of the hazard function and determines whether the hazard rate is increasing, decreasing, or constant over time.\n",
    "  - $\\rho < 1$: the hazard rate decreases over time (*decelerating hazard*). This might be suitable for modeling situations where the risk of the event diminishes as time progresses (e.g., infant mortality).\n",
    "  - $\\rho = 1$: the hazard rate is *constant* over time. This is equivalent to the exponential distribution, often used when the risk of the event remains stable.\n",
    "  - $\\rho > 1$: the hazard rate increases over time (*accelerating hazard*). This could be applicable when the risk of the event escalates with time (e.g., the failure of aging machinery)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Weibull model also provides explicit expressions for the hazard function and cumulative hazard function:\n",
    "- Cumulative Hazard Function: $H(t) = (t / \\lambda)^ρ$\n",
    "- Hazard Function: $h(t) = (\\rho / \\lambda) * (t / \\lambda)^{\\rho - 1}$\n",
    "\n",
    "The Weibull model has many advantages:\n",
    "- Flexibility: can model various hazard shapes (increasing, decreasing, or constant).\n",
    "- Parametric: provides estimates of parameters ($\\lambda$ and $\\rho$) that can be interpreted directly.\n",
    "- Handles covariates: allows for the inclusion of covariates to adjust for their effects on survival.\n",
    "\n",
    "We can consider opting for the Weibull model in scenarios where we have prior knowledge or evidence hinting at a particular hazard shape - be it increasing, decreasing, or constant. Additionally, it proves valuable when we seek parameters with straightforward interpretations, such as the time by which 63.2% of the population is expected to experience the event. Finally, the Weibull model emerges as a suitable alternative when the proportional hazards assumption, fundamental to the Cox PH model, appears to be violated in the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Application to the prednisolone data\n",
    "\n",
    "Now, let's apply the Weibull model to our prednisolone dataset. We'll fit separate Weibull models to the placebo and treatment groups, then visually compare their survival functions against the corresponding Kaplan-Meier estimates for a comprehensive understanding of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lifelines import WeibullFitter\n",
    "\n",
    "# Fit Weibull models for each group\n",
    "wb_ctrl = WeibullFitter()\n",
    "wb_pred = WeibullFitter()\n",
    "\n",
    "wb_ctrl.fit(\n",
    "    durations=data[~data['PRED']]['T'],\n",
    "    event_observed=data[~data['PRED']]['E'],\n",
    "    label='placebo (Weibull)')\n",
    "wb_pred.fit(\n",
    "    data[data['PRED']]['T'],\n",
    "    data[data['PRED']]['E'],\n",
    "    label='prednisolone (Weibull)')\n",
    "\n",
    "# Plot survival functions\n",
    "ax = wb_ctrl.plot_survival_function()\n",
    "wb_pred.plot_survival_function(ax=ax)\n",
    "\n",
    "# Overlay Kaplan-Meier curves on the same plot\n",
    "kmf_ctrl.plot(ax=ax, ci_show=False, color='steelblue', ls='--', alpha=.75)  # Dashed line for KM\n",
    "kmf_pred.plot(ax=ax, ci_show=False, color='darkorange', ls='--', alpha=.75)\n",
    "\n",
    "plt.title(\"Survival functions: Weibull Fit vs. Kaplan-Meier\")\n",
    "plt.xlabel(\"Time (months)\")\n",
    "plt.ylim((0, 1))\n",
    "plt.ylabel(\"Fraction survival\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Goodness-of-fit\n",
    "\n",
    "These interpretations are based on the assumption that the Weibull model provides a good fit to the data for both groups. It's always advisable to assess the model fit and consider alternative models if necessary.\n",
    "\n",
    "The visual comparison with the Kaplan-Meier plots is straightforward and intuitive as we can see how well the Weibull model captures the empirical survival patterns in the data. If the Weibull curves closely follow the Kaplan-Meier curves, it suggests a good fit. Large deviations between the curves indicate potential areas where the Weibull model might not be capturing the underlying survival patterns adequately.\n",
    "\n",
    "We can leverage the log-likelihood and AIC values provided in the Weibull model summary table to gain some insights into the goodness-of-fit, but with certain considerations and limitations. In general, a higher log-likelihood value indicates a better fit of the model to the data, but the log-likelihood alone doesn't tell if the model is a \"good\" fit in an absolute sense. It's more useful for relative comparisons between models, e.g., when testing other parametric models as it will be discussed at the end of this chapter.\n",
    "\n",
    "While AIC and visual comparisons of survival curves offer valuable insights into model fit, we can further assess the suitability of the Weibull model using a Quantile-Quantile (QQ) plot. The QQ plot compares the quantiles of the observed data to the quantiles we would expect if the data truly followed a Weibull distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lifelines.plotting import qq_plot\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(6, 3))\n",
    "\n",
    "# QQ plot for placebo group\n",
    "qq_plot(wb_ctrl, ax=axes[0])\n",
    "axes[0].set_title(\"QQ plot for Weibull fit (placebo)\")\n",
    "\n",
    "# QQ plot for PRED Group\n",
    "qq_plot(wb_pred, ax=axes[1])\n",
    "axes[1].set_title(\"QQ plot for Weibull fit (PRED)\")\n",
    "\n",
    "# Adjust layout and display the plot\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameters estimates\n",
    "\n",
    "We can print the model summaries to see the estimated parameters and their confidence intervals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model summary for the prednisone group\n",
      "--------------------------------------\n",
      "<lifelines.WeibullFitter:\"prednisolone (Weibull)\", fitted with 22 total observations, 11 right-censored observations>\n",
      "   number of observations = 22\n",
      "number of events observed = 11\n",
      "           log-likelihood = -70.27\n",
      "               hypothesis = lambda_ != 1, rho_ != 1\n",
      "\n",
      "---\n",
      "          coef  se(coef)  coef lower 95%  coef upper 95%\n",
      "lambda_ 224.72     79.37           69.16          380.27\n",
      "rho_      0.95      0.27            0.43            1.48\n",
      "\n",
      "         cmp to     z      p  -log2(p)\n",
      "lambda_    1.00  2.82 <0.005      7.70\n",
      "rho_       1.00 -0.17   0.87      0.21\n",
      "---\n",
      "AIC = 144.54\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(s:=\"Model summary for the prednisone group\")\n",
    "print(\"-\"*len(s))\n",
    "wb_pred.print_summary(style='ascii')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall context:\n",
    "- The table shows the results of fitting a Weibull survival model to your data, specifically for the group that received prednisone.\n",
    "- We have 22 observations in total, out of which 11 experienced the event of interest (presumably death or some other failure event), while the remaining 11 were right-censored (meaning they didn't experience the event during the observation period).\n",
    "\n",
    "Model fit statistics:\n",
    "- **log-likelihood = -70.27**: his is a measure of how well the Weibull model fits the data. Higher values (less negative) indicate a better fit.\n",
    "- **AIC = 144.54**: *Akaike Information Criterion*, another measure of model fit, penalizing for model complexity. Lower AIC values are generally preferred.\n",
    "\n",
    "Hypothesis test:\n",
    "- **hypothesis = lambda_ != 1, rho_ != 1**: the model is testing whether the estimated parameters (lambda and rho) are significantly different from 1. This is a common baseline to check if the Weibull model provides a better fit than the simpler exponential model (where rho = 1).\n",
    "\n",
    "Parameter estimates:\n",
    "- **lambda_**\n",
    "  - **coef = 224.72**: the estimated scale parameter ($\\lambda$) is 224.72. This means that 63.2% of the individuals in the prednisone group are expected to experience the event by approximately 224.72 months, or 18.7 years.\n",
    "  - **se(coef) = 79.37**: the standard error of the lambda estimate, indicating the uncertainty around this estimate.\n",
    "  - **coef lower 95% = 69.16, coef upper 95% = 380.27**: the 95% confidence interval for the lambda coefficient.\n",
    "  - **cmp to 1.00, z = 2.82, p <0.005, -log2(p) = 7.70**: these statistics test the null hypothesis that lambda is equal to 1. The very small P value (<0.005) strongly suggests that lambda is significantly different from 1, indicating that the Weibull model is a better fit than the exponential model for this group.\n",
    "- **rho_**\n",
    "  - **coef = 0.95**: the estimated shape parameter ($\\rho$) is 0.95. This is slightly less than 1, suggesting a hazard rate that decreases slightly over time for the prednisone group.\n",
    "  - **se(coef) = 0.27**: the standard error of the rho estimate.\n",
    "  - **coef lower 95% = 0.43, coef upper 95% = 1.48**: the 95% confidence interval for the rho coefficient. Note that this interval includes 1.\n",
    "  - **cmp to 1.00, z = -0.17, p = 0.87, -log2(p) = 0.21**: these statistics test the null hypothesis that rho is equal to 1. The large P value (0.87) indicates that we don't have strong evidence to reject this null hypothesis. This suggests that the hazard rate might be close to constant, and an exponential model could also be a reasonable fit for this group, although the Weibull model with a slightly decreasing hazard might be slightly better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model summary for the placebo group\n",
      "-----------------------------------\n",
      "<lifelines.WeibullFitter:\"placebo (Weibull)\", fitted with 22 total observations, 6 right-censored observations>\n",
      "   number of observations = 22\n",
      "number of events observed = 16\n",
      "           log-likelihood = -86.43\n",
      "               hypothesis = lambda_ != 1, rho_ != 1\n",
      "\n",
      "---\n",
      "         coef  se(coef)  coef lower 95%  coef upper 95%\n",
      "lambda_ 88.79     30.92           28.20          149.39\n",
      "rho_     0.72      0.15            0.42            1.02\n",
      "\n",
      "         cmp to     z      p  -log2(p)\n",
      "lambda_    1.00  2.84 <0.005      7.79\n",
      "rho_       1.00 -1.83   0.07      3.90\n",
      "---\n",
      "AIC = 176.87\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(s := \"Model summary for the placebo group\")\n",
    "print(\"-\"*len(s))\n",
    "wb_ctrl.print_summary(style='ascii')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here in the placebo group, the λ value is significantly lower than that of the prednisone group (88.79 vs. 224.72). This indicates that the median survival time (time at which 63.2% of the population experiences the event) is considerably shorter for the placebo group compared to the prednisone group.\n",
    "\n",
    "The ρ value for the placebo group is 0.72, which is less than 1. This suggests a decreasing hazard rate over time for the control group. The risk of the event is higher initially but decreases as time progresses. The value of ρ for the prednisone group is 0.95, which is close to 1. This suggests a hazard rate that is nearly constant over time for the prednisone group. The risk of the event remains relatively stable throughout the follow-up period."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weibull regression\n",
    "\n",
    "When exploring the influence of one or more continuous variables (covariates) on survival, we turn to the **Accelerated Failure Time (AFT)** model. The AFT model, in the context of Weibull regression, provides a framework to understand how covariates accelerate or decelerate the time to an event, i.e., $Y_i = f(X_i, \\beta)$. The core concept of the AFT model can be summarized as: $S_A(t) = S_B(t \\times \\lambda)$, where:\n",
    "- $S_A(t)$ and $S_B(t)$ are the survival functions for two individuals or groups with different covariate values. $S_B(t)$ is accelerating (speeding up) or decelerating (slowing down) along $S_A(t)$ by a factor of $\\lambda$.\n",
    "- $\\lambda$ (lambda) is the acceleration factor, which depends on the covariates and their coefficients.\n",
    "  - A $\\lambda > 1$ indicates that the event is accelerated (occurs sooner) for the individual or group with those covariate values compared to the baseline.\n",
    "  - A $\\lambda < 1$ indicates that the event is decelerated (occurs later).\n",
    "\n",
    "In the Weibull AFT model, the acceleration factor $\\lambda$ is modeled as: $\\lambda(x) = \\exp(\\beta_0 + \\beta_1 x_1 + \\dots + \\beta_n x_n)$, where:\n",
    "- $x_1, x_2, \\dots, x_n$  are the covariate values.\n",
    "- $\\beta_0, \\beta_1, \\dots, \\beta_n$ are the coefficients, quantifying the impact of each covariate on the acceleration factor.\n",
    "\n",
    "Optionally, the shape parameter ρ can also be modeled as a function of covariates: $\\rho(y) = \\exp(\\alpha_0 + \\alpha_1 y_1 + \\dots + \\alpha_n y_n)$, where:\n",
    "- $y_1, y_2, \\dots, y_n$ are additional covariate values (if applicable).\n",
    "- $\\alpha_0, \\alpha_1, \\dots, \\alpha_n$ are the coefficients for these covariates.\n",
    "\n",
    "The final Weibull survival function with covariates becomes $S(t; x, y) = \\exp[-(t / \\lambda(x))^{\\rho(y)}]$, and the cumulative hazard function $H(t; x, y) = (t / \\lambda(x))^{\\rho(y)}$.\n",
    "\n",
    "How do we interpret the Weibull AFT model? A positive $\\beta_i$ indicates that an increase in the corresponding covariate $x_i$ is associated with an increase in the acceleration factor $\\lambda$, leading to a faster time to the event (decreased survival time). A negative $\\beta_i$ indicates the opposite: an increase in $x_i$ is associated with a decrease in $\\lambda$, leading to a slower time to the event (increased survival time).\n",
    "\n",
    "Note that, when there are no covariates, or all covariates are at their reference levels (typically 0 for continuous covariates and the reference category for categorical covariates), the Weibull model reduces to its basic form with parameters $\\lambda_0$ and $\\rho_0$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.WeibullAFTFitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration col</th>\n",
       "      <td>'T'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event col</th>\n",
       "      <td>'E'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of observations</th>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events observed</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood</th>\n",
       "      <td>-157.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2024-11-17 15:47:13 UTC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th style=\"min-width: 12px;\"></th>\n",
       "      <th style=\"min-width: 12px;\"></th>\n",
       "      <th style=\"min-width: 12px;\">coef</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">se(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">coef lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">coef upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">cmp to</th>\n",
       "      <th style=\"min-width: 12px;\">z</th>\n",
       "      <th style=\"min-width: 12px;\">p</th>\n",
       "      <th style=\"min-width: 12px;\">-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">lambda_</th>\n",
       "      <th>PRED</th>\n",
       "      <td>1.05</td>\n",
       "      <td>2.87</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.06</td>\n",
       "      <td>2.05</td>\n",
       "      <td>1.06</td>\n",
       "      <td>7.79</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.07</td>\n",
       "      <td>0.04</td>\n",
       "      <td>4.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>4.48</td>\n",
       "      <td>88.34</td>\n",
       "      <td>0.32</td>\n",
       "      <td>3.86</td>\n",
       "      <td>5.10</td>\n",
       "      <td>47.47</td>\n",
       "      <td>164.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.14</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>148.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rho_</th>\n",
       "      <th>Intercept</th>\n",
       "      <td>-0.24</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.17</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.40</td>\n",
       "      <td>0.16</td>\n",
       "      <td>2.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><br><div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Concordance</th>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AIC</th>\n",
       "      <td>320.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood ratio test</th>\n",
       "      <td>4.54 on 1 df</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-log2(p) of ll-ratio test</th>\n",
       "      <td>4.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "\\begin{tabular}{llrrrrrrrrrrr}\n",
       " &  & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\n",
       "param & covariate &  &  &  &  &  &  &  &  &  &  &  \\\\\n",
       "\\multirow[c]{2}{*}{lambda_} & PRED & 1.05 & 2.87 & 0.51 & 0.06 & 2.05 & 1.06 & 7.79 & 0.00 & 2.07 & 0.04 & 4.70 \\\\\n",
       " & Intercept & 4.48 & 88.34 & 0.32 & 3.86 & 5.10 & 47.47 & 164.38 & 0.00 & 14.14 & 0.00 & 148.43 \\\\\n",
       "rho_ & Intercept & -0.24 & 0.79 & 0.17 & -0.57 & 0.09 & 0.57 & 1.10 & 0.00 & -1.40 & 0.16 & 2.64 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.WeibullAFTFitter: fitted with 44 total observations, 17 right-censored observations>\n",
       "             duration col = 'T'\n",
       "                event col = 'E'\n",
       "   number of observations = 44\n",
       "number of events observed = 27\n",
       "           log-likelihood = -157.02\n",
       "         time fit was run = 2024-11-17 15:47:13 UTC\n",
       "\n",
       "---\n",
       "                   coef exp(coef)  se(coef)  coef lower 95%  coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n",
       "param   covariate                                                                                                  \n",
       "lambda_ PRED       1.05      2.87      0.51            0.06            2.05                1.06                7.79\n",
       "        Intercept  4.48     88.34      0.32            3.86            5.10               47.47              164.38\n",
       "rho_    Intercept -0.24      0.79      0.17           -0.57            0.09                0.57                1.10\n",
       "\n",
       "                   cmp to     z      p  -log2(p)\n",
       "param   covariate                               \n",
       "lambda_ PRED         0.00  2.07   0.04      4.70\n",
       "        Intercept    0.00 14.14 <0.005    148.43\n",
       "rho_    Intercept    0.00 -1.40   0.16      2.64\n",
       "---\n",
       "Concordance = 0.63\n",
       "AIC = 320.03\n",
       "log-likelihood ratio test = 4.54 on 1 df\n",
       "-log2(p) of ll-ratio test = 4.92"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lifelines import WeibullAFTFitter\n",
    "\n",
    "# Fit the Weibull AFT model\n",
    "aft = WeibullAFTFitter()\n",
    "aft.fit(\n",
    "    df=data,\n",
    "    duration_col='T',\n",
    "    event_col='E',\n",
    "    # formula='PRED'\n",
    ")\n",
    "\n",
    "# Print the model summary\n",
    "aft.print_summary(decimals=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results suggest that the treatment ('PRED') is associated with a significant increase in survival time. The positive coefficient for 'PRED' indicates that individuals in the treatment group (PRED = True) experience a deceleration in the time to the event compared to the control group (PRED = False). In other words, the treatment is associated with a longer survival time. The P value is less than 0.05, indicating that the effect of the treatment on survival time is statistically significant.\n",
    "\n",
    "The intercept for λ represents the log of the baseline scale parameter ($\\lambda_0$) in the Weibull AFT model. To get the actual baseline scale parameter, we need to exponentiate this value: $\\lambda_0 = exp(4.48) \\approx 88.34$. This means that, in the absence of any effect from the 'PRED' covariate (i.e., when PRED = False, which is often considered the baseline or reference group in this context), 63.2% of the individuals are expected to experience the event by approximately 88.34 months.\n",
    "\n",
    "Finally, the intercept for ρ represents the log of the baseline shape parameter ($\\rho_0$) in the Weibull AFT model. To get the actual baseline shape parameter, we need to exponentiate this value: $\\rho_0 = exp(-0.24) \\approx 0.79$. This indicates that the baseline hazard function (the hazard function when PRED = False) is decreasing over time. A shape parameter less than 1 implies a decreasing hazard rate, meaning the risk of the event is higher initially but diminishes as time progresses."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization and prediction with Weibull model\n",
    "\n",
    "Having established the theoretical foundations of the Weibull AFT model, let's demonstrate how the Weibull AFT model can be leveraged to gain valuable insights into real-world survival data. We can visualize the impact of covariates on survival and make predictions using a more complex dataset, showcasing the model's versatility and power.\n",
    "\n",
    "This data set is originally from [Rossi et al. (1980)](https://rdrr.io/cran/carData/man/Rossi.html), and is used as an example in [Allison (1995)](https://www.oreilly.com/library/view/survival-analysis-using/9781555442798/). The data pertain to 432 convicts who were released from Maryland state prisons in the 1970s and who were followed up for one year after release. Half the released convicts were assigned at random to an experimental treatment in which they were given financial aid; half did not receive aid.\n",
    "\n",
    "Note that the visualization and prediction techniques described in this section are applicable to both the Weibull AFT and Cox PH models, allowing us to gain a deeper understanding of the relationships between covariates and survival outcomes, regardless of the specific model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>week</th>\n",
       "      <th>arrest</th>\n",
       "      <th>fin</th>\n",
       "      <th>age</th>\n",
       "      <th>race</th>\n",
       "      <th>wexp</th>\n",
       "      <th>mar</th>\n",
       "      <th>paro</th>\n",
       "      <th>prio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   week  arrest  fin  age  race  wexp  mar  paro  prio\n",
       "0    20       1    0   27     1     0    0     1     3\n",
       "1    17       1    0   18     1     0    0     1     8\n",
       "2    25       1    0   19     0     1    0     1    13\n",
       "3    52       0    1   23     1     1    1     1     1\n",
       "4    52       0    0   19     0     1    0     1     3"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lifelines.datasets import load_rossi\n",
    "\n",
    "rossi = load_rossi()\n",
    "rossi.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use formula to analyze linear models with interaction terms, e.g., $\\beta_1 \\text{fin} + \\beta_2 \\text{wexp} + \\beta_3 \\text{age} +\\beta_4 \\text{prio} + \\beta_5 \\text{age}.\\text{prio}$, as in the example below. Note that it's also possible to [model the $\\rho$ parameter](https://lifelines.readthedocs.io/en/latest/Survival%20Regression.html#modeling-ancillary-parameters)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.WeibullAFTFitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration col</th>\n",
       "      <td>'week'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event col</th>\n",
       "      <td>'arrest'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of observations</th>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events observed</th>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood</th>\n",
       "      <td>-680.563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2024-11-17 15:47:56 UTC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th style=\"min-width: 12px;\"></th>\n",
       "      <th style=\"min-width: 12px;\"></th>\n",
       "      <th style=\"min-width: 12px;\">coef</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">se(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">coef lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">coef upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">cmp to</th>\n",
       "      <th style=\"min-width: 12px;\">z</th>\n",
       "      <th style=\"min-width: 12px;\">p</th>\n",
       "      <th style=\"min-width: 12px;\">-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">lambda_</th>\n",
       "      <th>Intercept</th>\n",
       "      <td>4.308</td>\n",
       "      <td>74.261</td>\n",
       "      <td>0.501</td>\n",
       "      <td>3.325</td>\n",
       "      <td>5.290</td>\n",
       "      <td>27.805</td>\n",
       "      <td>198.336</td>\n",
       "      <td>0.000</td>\n",
       "      <td>8.594</td>\n",
       "      <td>&lt;0.0005</td>\n",
       "      <td>56.727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fin</th>\n",
       "      <td>0.236</td>\n",
       "      <td>1.266</td>\n",
       "      <td>0.137</td>\n",
       "      <td>-0.033</td>\n",
       "      <td>0.505</td>\n",
       "      <td>0.968</td>\n",
       "      <td>1.657</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.722</td>\n",
       "      <td>0.085</td>\n",
       "      <td>3.556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wexp</th>\n",
       "      <td>0.169</td>\n",
       "      <td>1.184</td>\n",
       "      <td>0.150</td>\n",
       "      <td>-0.125</td>\n",
       "      <td>0.463</td>\n",
       "      <td>0.883</td>\n",
       "      <td>1.589</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.128</td>\n",
       "      <td>0.259</td>\n",
       "      <td>1.947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.020</td>\n",
       "      <td>1.020</td>\n",
       "      <td>0.022</td>\n",
       "      <td>-0.023</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.977</td>\n",
       "      <td>1.065</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.916</td>\n",
       "      <td>0.359</td>\n",
       "      <td>1.476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prio</th>\n",
       "      <td>-0.220</td>\n",
       "      <td>0.802</td>\n",
       "      <td>0.122</td>\n",
       "      <td>-0.460</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.631</td>\n",
       "      <td>1.020</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-1.801</td>\n",
       "      <td>0.072</td>\n",
       "      <td>3.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age:prio</th>\n",
       "      <td>0.007</td>\n",
       "      <td>1.007</td>\n",
       "      <td>0.005</td>\n",
       "      <td>-0.004</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.996</td>\n",
       "      <td>1.018</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.282</td>\n",
       "      <td>0.200</td>\n",
       "      <td>2.323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rho_</th>\n",
       "      <th>Intercept</th>\n",
       "      <td>0.341</td>\n",
       "      <td>1.406</td>\n",
       "      <td>0.089</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.515</td>\n",
       "      <td>1.181</td>\n",
       "      <td>1.674</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.833</td>\n",
       "      <td>&lt;0.0005</td>\n",
       "      <td>12.949</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><br><div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Concordance</th>\n",
       "      <td>0.640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AIC</th>\n",
       "      <td>1375.126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood ratio test</th>\n",
       "      <td>32.123 on 5 df</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-log2(p) of ll-ratio test</th>\n",
       "      <td>17.441</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "\\begin{tabular}{llrrrrrrrrrrr}\n",
       " &  & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\n",
       "param & covariate &  &  &  &  &  &  &  &  &  &  &  \\\\\n",
       "\\multirow[c]{6}{*}{lambda_} & Intercept & 4.308 & 74.261 & 0.501 & 3.325 & 5.290 & 27.805 & 198.336 & 0.000 & 8.594 & 0.000 & 56.727 \\\\\n",
       " & fin & 0.236 & 1.266 & 0.137 & -0.033 & 0.505 & 0.968 & 1.657 & 0.000 & 1.722 & 0.085 & 3.556 \\\\\n",
       " & wexp & 0.169 & 1.184 & 0.150 & -0.125 & 0.463 & 0.883 & 1.589 & 0.000 & 1.128 & 0.259 & 1.947 \\\\\n",
       " & age & 0.020 & 1.020 & 0.022 & -0.023 & 0.063 & 0.977 & 1.065 & 0.000 & 0.916 & 0.359 & 1.476 \\\\\n",
       " & prio & -0.220 & 0.802 & 0.122 & -0.460 & 0.020 & 0.631 & 1.020 & 0.000 & -1.801 & 0.072 & 3.801 \\\\\n",
       " & age:prio & 0.007 & 1.007 & 0.005 & -0.004 & 0.018 & 0.996 & 1.018 & 0.000 & 1.282 & 0.200 & 2.323 \\\\\n",
       "rho_ & Intercept & 0.341 & 1.406 & 0.089 & 0.167 & 0.515 & 1.181 & 1.674 & 0.000 & 3.833 & 0.000 & 12.949 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.WeibullAFTFitter: fitted with 432 total observations, 318 right-censored observations>\n",
       "             duration col = 'week'\n",
       "                event col = 'arrest'\n",
       "   number of observations = 432\n",
       "number of events observed = 114\n",
       "           log-likelihood = -680.563\n",
       "         time fit was run = 2024-11-17 15:47:56 UTC\n",
       "\n",
       "---\n",
       "                    coef exp(coef)  se(coef)  coef lower 95%  coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n",
       "param   covariate                                                                                                   \n",
       "lambda_ Intercept  4.308    74.261     0.501           3.325           5.290              27.805             198.336\n",
       "        fin        0.236     1.266     0.137          -0.033           0.505               0.968               1.657\n",
       "        wexp       0.169     1.184     0.150          -0.125           0.463               0.883               1.589\n",
       "        age        0.020     1.020     0.022          -0.023           0.063               0.977               1.065\n",
       "        prio      -0.220     0.802     0.122          -0.460           0.020               0.631               1.020\n",
       "        age:prio   0.007     1.007     0.005          -0.004           0.018               0.996               1.018\n",
       "rho_    Intercept  0.341     1.406     0.089           0.167           0.515               1.181               1.674\n",
       "\n",
       "                   cmp to      z       p  -log2(p)\n",
       "param   covariate                                 \n",
       "lambda_ Intercept   0.000  8.594 <0.0005    56.727\n",
       "        fin         0.000  1.722   0.085     3.556\n",
       "        wexp        0.000  1.128   0.259     1.947\n",
       "        age         0.000  0.916   0.359     1.476\n",
       "        prio        0.000 -1.801   0.072     3.801\n",
       "        age:prio    0.000  1.282   0.200     2.323\n",
       "rho_    Intercept   0.000  3.833 <0.0005    12.949\n",
       "---\n",
       "Concordance = 0.640\n",
       "AIC = 1375.126\n",
       "log-likelihood ratio test = 32.123 on 5 df\n",
       "-log2(p) of ll-ratio test = 17.441"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aft_rossi = WeibullAFTFitter()\n",
    "aft_rossi.fit(\n",
    "    df=rossi,\n",
    "    duration_col='week',\n",
    "    event_col='arrest',\n",
    "    formula=\"fin + wexp + age * prio\"\n",
    ")\n",
    "\n",
    "aft_rossi.print_summary(3)  # access the results using aft.summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can view all covariate estimates and their 95% confidence intervals in a forest plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aft_rossi.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also observe the influence a variable, e.g., 'prio', in the model by plotting the outcome (i.e. survival) of changing the variable. By comparing the positions and shapes of these curves, we can visually assess how changes in this covariate affect the predicted survival time. In a Weibull AFT model, a negative coefficient for a covariate (like 'prio') indicates that an increase in that covariate is associated with a decrease in the acceleration factor λ. This implies that higher values of 'prio' should lead to a slower time to the event (i.e., increased survival time), and we would expect the survival curve to shift to the right."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aft_rossi.plot_partial_effects_on_outcome(\n",
    "    covariates='prio',\n",
    "    values=range(0, 16, 3),\n",
    "    cmap='coolwarm'\n",
    ")\n",
    "\n",
    "plt.title(\"Effect of 'prio' on survival (Weibull AFT)\")\n",
    "plt.xlabel('Time (weeks)')\n",
    "plt.ylabel('Survival probability');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the current example, we observe that increasing 'prio' shifts the survival curve to the left, which typically indicates a faster time to the event (decreased survival time). This may be due to interaction effects. If the Weibull AFT model includes interaction terms involving 'prio', the interpretation of the main effect of 'prio' becomes more complex, and the effect of 'prio' on survival might depend on the values of other covariates involved in the interaction. In such cases, it's essential to examine the `plot_partial_effects_on_outcome` for different combinations of covariate values to understand the full picture."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a list of tuples representing combinations of 'prio' and 'age' values\n",
    "values_grid = [(p, a) for p in range(0, 16, 3) for a in range(20, 41, 20)]\n",
    "\n",
    "# Plot the partial effects on outcome for the combined covariates\n",
    "aft_rossi.plot_partial_effects_on_outcome(\n",
    "    covariates=['prio', 'age'], values=values_grid, cmap='Paired_r')\n",
    "\n",
    "# Enhance the plot with labels and title\n",
    "plt.title(\"Effect of 'prio:age' interaction on survival (Weibull AFT)\")\n",
    "plt.xlabel('Time (weeks)')\n",
    "plt.ylabel('Survival probability')\n",
    "\n",
    "plt.legend([f\"prio={p}, age={a}\" for p, a in values_grid], fontsize=8);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can obtain survival predictions for one or more new individuals from the fitted Weibull AFT model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted median survival time:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3    128.088796\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict median survival time for a specific combination of covariates\n",
    "# Note: don't feed `predict_median` with a Series but with a true DataFrame!\n",
    "median_survival = aft_rossi.predict_median(rossi.iloc[[3]])\n",
    "\n",
    "print(\"Predicted median survival time:\")\n",
    "median_survival"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predict survival function for a new combination of covariates\n",
    "# If a numpy array, columns must be in the same order as the training data, i.e.,\n",
    "# ['fin', 'age', 'race', 'wexp', 'mar', 'paro', 'prio']\n",
    "\n",
    "new_individual = pd.DataFrame(\n",
    "    data={\n",
    "        'fin': [.5, .5, .5],\n",
    "        'age': [20, 30, 40],\n",
    "        'race': [1, 1, 1],\n",
    "        'wexp': [0, 0, 0],\n",
    "        'mar': [0, 0, 0],\n",
    "        'paro': [1, 1, 1],\n",
    "        'prio': [10, 10, 10]\n",
    "    },\n",
    "    index=[20, 30, 40],\n",
    ")\n",
    "\n",
    "survival_function = aft_rossi.predict_survival_function(new_individual)\n",
    "\n",
    "# Plot the predicted survival function\n",
    "survival_function.plot(cmap='viridis')\n",
    "plt.title(f\"Predicted survival function for varying 'age' only\")\n",
    "plt.xlabel('Time (weeks)')\n",
    "plt.ylabel('Survival probability')\n",
    "plt.legend(title=\"age\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Alternative parametric models\n",
    "\n",
    "While the Weibull model offers flexibility in capturing different hazard shapes, it's not the only parametric option available:\n",
    "- Exponential model: assumes a constant hazard rate over time. It's a special case of the Weibull model with the shape parameter (rho) equal to 1.\n",
    "- Log-Normal model: assumes that the logarithm of the survival times follows a normal distribution. It can be useful when the hazard rate initially increases and then decreases.\n",
    "- Log-logistic model: assumes that the logarithm of the odds of survival follows a logistic distribution. It can also model non-monotonic hazard rates.\n",
    "- Generalized gamma model: a more general model that encompasses the Weibull, exponential, and log-normal models as special cases. It offers additional flexibility but can be more complex to interpret\n",
    "\n",
    "The [Akaike information criterion (AIC)](https://en.wikipedia.org/wiki/Akaike_information_criterion) is a useful metric for comparing the relative quality of different statistical models fit to the same data. It estimates the relative amount of information lost by a given model and penalizes large number of estimated parameters. Therefore, the less information a model loses, the higher the quality of that model, and the fewer parameters (less complex) a model is, the higher the quality of that model. It balances the model's goodness-of-fit (measured by the log-likelihood) with its complexity (measured by the number of parameters)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AIC values for different models fitting 'PRED' data:\n",
      "\n",
      "WeibullFitter \t 144.54091487803493\n",
      "ExponentialFitter \t 142.56870858109932\n",
      "LogNormalFitter \t 146.80469733430616\n",
      "LogLogisticFitter \t 145.8274267786253\n",
      "GeneralizedGammaFitter \t 145.1972001057433\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "from lifelines import WeibullFitter, ExponentialFitter, LogNormalFitter, \\\n",
    "    LogLogisticFitter, GeneralizedGammaFitter\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Instantiate each fitter\n",
    "wb = WeibullFitter()\n",
    "exp = ExponentialFitter()\n",
    "lognorm = LogNormalFitter()\n",
    "loglogistic = LogLogisticFitter()\n",
    "gamma = GeneralizedGammaFitter()\n",
    "\n",
    "# Fit to data and display the AIC\n",
    "print(\"AIC values for different models fitting 'PRED' data:\\n\")\n",
    "for model in [wb, exp, lognorm, loglogistic, gamma]:\n",
    "    model.fit(durations=data[data['PRED']]['T'],\n",
    "              event_observed=data[data['PRED']]['E'])\n",
    "    print(model.__class__.__name__, '\\t', model.AIC_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While numerical metrics like AIC provide valuable guidance in model selection, it's always beneficial to complement them with visual inspection. Plotting the fitted survival curves from different parametric models alongside the non-parametric Kaplan-Meier curve offers a quick and intuitive way to gauge the quality of the fit. This visual comparison allows us to see how well each model captures the empirical survival patterns in the data and identify potential areas where a model might be oversimplifying or underfitting the complexities of the survival experience."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot for PRED Group\n",
    "ax = kmf_pred.plot(\n",
    "    style='--',\n",
    "    lw=2,\n",
    "    ci_show=False,\n",
    "    color='darkgrey',\n",
    "    alpha=.75,\n",
    "    label='Kaplan-Meier')\n",
    "\n",
    "for model, color in zip(\n",
    "    [wb, exp, lognorm, loglogistic, gamma],\n",
    "    ['yellowgreen', 'firebrick', 'slategray', 'chocolate', 'steelblue']):\n",
    "    model.plot_survival_function(\n",
    "        ax=ax,\n",
    "        ci_show=False,\n",
    "        color=color,\n",
    "        label=model.__class__.__name__.removesuffix('Fitter'),\n",
    "    )\n",
    "\n",
    "ax.set_title(\"Survival functions for 'PRED=True' group\") # type: ignore\n",
    "ax.set_xlabel('Time (weeks)') # type: ignore\n",
    "ax.set_ylabel('Survival probability') # type: ignore\n",
    "ax.legend() # type: ignore\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given a set of candidate models for the data, the one with the minimum AIC value is the preferred model. To streamline the process of comparing different parametric models and selecting the best-fitting one based on the AIC criterion, we can leverage the convenient `find_best_parametric_model` function provided by lifelines. This function automates the fitting and comparison of multiple parametric models, ensuring consistency in the model selection process.\n",
    "\n",
    "Let's use this function to identify the best-fitting parametric model for both the 'PRED' and placebo groups in our prednisolone dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parametric Model for placebo group:\n",
      "<lifelines.LogNormalFitter:\"LogNormal_estimate\", fitted with 22 total observations, 6 right-censored observations>\n",
      "AIC: 174.54\n",
      "\n",
      "\n",
      "Best Parametric Model for 'PRED' group:\n",
      "<lifelines.PiecewiseExponentialFitter:\"PiecewiseExponentialFitter: 1 breakpoint\", fitted with 22 total observations, 11 right-censored observations>\n",
      "AIC: 142.35\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from lifelines.utils import find_best_parametric_model\n",
    "\n",
    "# Find the best parametric model for the placebo group\n",
    "best_model_ctrl, best_aic_ctrl = find_best_parametric_model(\n",
    "    event_times=data[~data['PRED']]['T'],\n",
    "    event_observed=data[~data['PRED']]['E'],\n",
    "    scoring_method=\"AIC\"\n",
    ")\n",
    "\n",
    "# Print the best model and its AIC for the placebo group\n",
    "print(\"Best Parametric Model for placebo group:\")\n",
    "print(best_model_ctrl)\n",
    "print(f\"AIC: {best_aic_ctrl:.2f}\\n\")\n",
    "\n",
    "# Find the best parametric model for the 'PRED' group\n",
    "best_model_pred, best_aic_pred = find_best_parametric_model(\n",
    "    event_times=data[data['PRED']]['T'],\n",
    "    event_observed=data[data['PRED']]['E'],\n",
    "    scoring_method=\"AIC\"\n",
    ")\n",
    "\n",
    "# Print the best model and its AIC for the 'PRED' group\n",
    "print(\"\\nBest Parametric Model for 'PRED' group:\")\n",
    "print(best_model_pred)\n",
    "print(f\"AIC: {best_aic_pred:.2f}\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our exploration of parametric survival models has revealed the richness and depth of this field. Even within the realm of parametric models, there exists a wide array of options beyond the commonly used Weibull, exponential, and log-normal distributions. The lifelines library, with its extensive collection of fitters, provides a powerful toolkit for delving deeper into this fascinating domain.\n",
    "\n",
    "The emergence of the `PiecewiseExponentialFitter` as the best-fitting model for the 'PRED' group underscores the importance of considering a diverse set of models and letting the data guide the selection process. While we've only scratched the surface in this chapter, the lifelines library offers many more advanced models and techniques for those seeking to master the intricacies of survival analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "In this chapter, we embarked on a comprehensive exploration of survival analysis, harnessing the capabilities of the lifelines library in Python. We commenced by elucidating the fundamental concepts of survival data and its visual representation through **Kaplan-Meier** curves. The **log-rank test** was then employed to rigorously assess the statistical significance of observed differences in survival between groups. \n",
    "\n",
    "We further ventured into the realm of **hazard ratios**, quantifying the relative risks associated with distinct groups, and examined the **Nelson-Aalen estimator**, providing an alternative perspective on the accumulation of risk over time. The chapter subsequently focused on the power of **parametric models**, with a particular emphasis on the **Cox proportional hazards model** and the flexible **Weibull model**. These models allowed us to identify and quantify the impact of **covariates** on survival, enhancing our understanding of the complex interplay between variables and outcomes. The Weibull model's adaptability in accommodating various hazard shapes proved particularly valuable when the proportional hazards assumption of the Cox model was potentially violated.\n",
    "\n",
    "Throughout the chapter, we emphasized the synergy between visualization and prediction. Techniques like `plot_partial_effects_on_outcome` were employed to graphically illustrate the influence of covariates on survival, while functions such as `predict_median` enabled the extraction of concrete predictions from our models. The critical aspect of model selection was also addressed, utilizing the **AIC criterion** and visual inspections, including **QQ plots**, to guide informed decision-making.\n",
    "\n",
    "In conclusion, this chapter has equipped readers with the theoretical foundations and practical tools necessary to navigate the complexities of survival analysis. The lifelines library, with its extensive suite of functionalities, empowers researchers and practitioners to unveil the intricate dynamics of time-to-event data across diverse fields. We encourage readers to apply these techniques to their own datasets, fostering a deeper understanding of survival phenomena and contributing to the advancement of knowledge in this critical domain."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cheat sheet\n",
    "\n",
    "### Kaplan-Meier\n",
    "\n",
    "```python\n",
    "# convert datetimes to durations\n",
    "from lifelines.utils import datetimes_to_durations\n",
    "T, E = datetimes_to_durations(\n",
    "    start_date,  # list/array of datatime values\n",
    "    end_date,    # list/array of datatime values\n",
    "    fill_date='2013-10-15')  # value used for NaN\n",
    "\n",
    "# Fit and plot Kaplan-Meier curves\n",
    "from lifelines import KaplanMeierFitter\n",
    "kmf_pred = KaplanMeierFitter(label='prednisolone (KM)')\n",
    "kmf_pred.fit(\n",
    "    durations=data.loc[data['PRED'], 'T'],\n",
    "    event_observed=data.loc[data['PRED'], 'E'],\n",
    "    #label='prednisolone (KM)'\n",
    ")\n",
    "\n",
    "ax = kmf_pred.plot_survival_function(\n",
    "    show_censors=True,\n",
    "    lw=2,\n",
    "    ci_alpha=.1,\n",
    "    censor_styles=None)  # style as dictionary\n",
    "# Fit other groups and plot on the same axis\n",
    "\n",
    "# Extract median survival times\n",
    "median_survival_pred = kmf_pred.median_survival_time_\n",
    "\n",
    "# Survival prediction\n",
    "months=5*12  # i.e., 5-year survival time\n",
    "kmf_pred.predict(months)\n",
    "```\n",
    "\n",
    "### Non-parametric log-rank\n",
    "\n",
    "```python\n",
    "from lifelines.statistics import logrank_test\n",
    "results = logrank_test(\n",
    "    durations_A=data.loc[data['PRED'], 'T'],\n",
    "    durations_B=data.loc[~data['PRED'],'T'],\n",
    "    event_observed_A=data.loc[data['PRED'], 'E'],\n",
    "    event_observed_B=data.loc[~data['PRED'],'E'],\n",
    "    alpha=.95)\n",
    "\n",
    "results.print_summary()\n",
    "# results.summary, results.p_value, results.test_statistic\n",
    "```\n",
    "\n",
    "### Nelson-Aalen cumulative hazard\n",
    "\n",
    "```python\n",
    "from lifelines import NelsonAalenFitter\n",
    "\n",
    "naf_pred = NelsonAalenFitter()\n",
    "naf_pred.fit(\n",
    "    data[data['PRED']]['T'],\n",
    "    event_observed=data[data['PRED']]['E'],\n",
    "    label='prednisolone')\n",
    "\n",
    "naf_pred.plot(color='red')\n",
    "ax = naf_pred.plot(color='red')\n",
    "```\n",
    "\n",
    "### Semi-parametric Cox PH\n",
    "\n",
    "```python\n",
    "from lifelines import CoxPHFitter\n",
    "\n",
    "# Fit the CoxPHFitter\n",
    "cph = CoxPHFitter()\n",
    "cph.fit(\n",
    "    df=data,\n",
    "    duration_col=\"T\",\n",
    "    event_col=\"E\",\n",
    "    # formula='PRED'\n",
    ")\n",
    "\n",
    "# Print the summary to see the coefficient and its significance\n",
    "cph.print_summary(decimals=3)\n",
    "\n",
    "# Assessing the proportional hazards assumption a posteriori\n",
    "cph.check_assumptions(\n",
    "    training_df=data,\n",
    "    p_value_threshold=.05,  # default 0.01\n",
    "    show_plots=True)\n",
    "\n",
    "# Visualizing the impact of covariate\n",
    "cph.plot_partial_effects_on_outcome(\n",
    "    covariates='PRED',\n",
    "    values=[False, True],\n",
    "    plot_baseline=True)\n",
    "```\n",
    "\n",
    "### Parametric Weibull\n",
    "\n",
    "#### Fitting and visualization\n",
    "\n",
    "```python\n",
    "from lifelines import WeibullFitter\n",
    "\n",
    "wb_pred = WeibullFitter()\n",
    "wb_pred.fit(\n",
    "    data[data['PRED']]['T'],\n",
    "    data[data['PRED']]['E'],\n",
    "    label='prednisolone (Weibull)')\n",
    "\n",
    "wb_pred.print_summary(style='ascii')\n",
    "\n",
    "ax = wb_pred.plot_survival_function()\n",
    "```\n",
    "\n",
    "#### Goodness-of-fit (QQ plot)\n",
    "\n",
    "```python\n",
    "from lifelines.plotting import qq_plot\n",
    "qq_plot(wb_pred)\n",
    "```\n",
    "\n",
    "#### AFT regression\n",
    "\n",
    "```python\n",
    "from lifelines import WeibullAFTFitter\n",
    "\n",
    "aft_rossi = WeibullAFTFitter()\n",
    "\n",
    "aft_rossi.fit(\n",
    "    df=rossi,\n",
    "    duration_col='week',\n",
    "    event_col='arrest',\n",
    "    formula=\"fin + wexp + age * prio\",\n",
    ")\n",
    "\n",
    "aft_rossi.print_summary(3)\n",
    "\n",
    "# Plot covariate and 95% CI forest plot\n",
    "aft_rossi.plot()\n",
    "\n",
    "# Plot effect of covariate on survival\n",
    "aft_rossi.plot_partial_effects_on_outcome(\n",
    "    covariates='prio',\n",
    "    values=range(0, 16, 3),\n",
    "    cmap='coolwarm')\n",
    "\n",
    "# Prediction of median survival\n",
    "aft_rossi.predict_median(rossi.iloc[[3]])  # don't feed predict_median with a Series but with a true DataFrame!\n",
    "\n",
    "# Prediction of survival function for a new individual\n",
    "# Enter values for the new individual(s)\n",
    "new_individual = pd.DataFrame()  # Could also be an np.array\n",
    "survival_function = aft_rossi.predict_survival_function(new_individual)\n",
    "survival_function.plot()\n",
    "```\n",
    "\n",
    "### Alternative parametric models\n",
    "\n",
    "```python\n",
    "from lifelines import WeibullFitter, ExponentialFitter, LogNormalFitter, \\\n",
    "    LogLogisticFitter, GeneralizedGammaFitter\n",
    "\n",
    "# Instantiate each fitter\n",
    "wb = WeibullFitter()\n",
    "exp = ExponentialFitter()\n",
    "lognorm = LogNormalFitter()\n",
    "loglogistic = LogLogisticFitter()\n",
    "gamma = GeneralizedGammaFitter()\n",
    "\n",
    "# Fit to data and display the AIC\n",
    "print(\"AIC values for different models fitting 'PRED' data:\\n\")\n",
    "for model in [wb, exp, lognorm, loglogistic, gamma]:\n",
    "    model.fit(\n",
    "        durations=data[data['PRED']]['T'],\n",
    "        event_observed=data[data['PRED']]['E'])\n",
    "    print(model.__class__.__name__, '\\t', model.AIC_)\n",
    "\n",
    "# Plot each plot on the KMF curve\n",
    "ax = kmf_pred.plot()\n",
    "for model, color in zip(\n",
    "    [wb, exp, lognorm, loglogistic, gamma],\n",
    "    ['yellowgreen', 'firebrick', 'slategray', 'chocolate', 'steelblue']):\n",
    "\n",
    "    model.plot_survival_function(\n",
    "        ax=ax,\n",
    "        ci_show=False,\n",
    "        color=color,\n",
    "        label=model.__class__.__name__.removesuffix('Fitter'))\n",
    "\n",
    "# Find the best parametric model\n",
    "from lifelines.utils import find_best_parametric_model\n",
    "\n",
    "# Find the best parametric model for the 'PRED' group\n",
    "best_model_pred, best_aic_pred = find_best_parametric_model(\n",
    "    event_times=data[data['PRED']]['T'],\n",
    "    event_observed=data[data['PRED']]['E'],\n",
    "    scoring_method=\"AIC\")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Session information\n",
    "\n",
    "The output below details all packages and version necessary to reproduce the results in this report."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.12.7\n",
      "-------------\n",
      "numpy: 1.26.4\n",
      "pandas: 2.2.2\n",
      "lifelines: 0.29.0\n",
      "matplotlib: 3.9.2\n",
      "seaborn: 0.13.2\n",
      "scipy: 1.14.1\n"
     ]
    }
   ],
   "source": [
    "!python --version\n",
    "print(\"-------------\")\n",
    "\n",
    "from importlib.metadata import version\n",
    "\n",
    "# List of packages we want to check the version\n",
    "packages = ['numpy', 'pandas', 'lifelines', 'matplotlib', 'seaborn', 'scipy']\n",
    "\n",
    "# Initialize an empty list to store the versions\n",
    "versions = []\n",
    "\n",
    "# Loop over the packages\n",
    "for package in packages:\n",
    "    try:\n",
    "        # Get the version of the package\n",
    "        package_version = version(package)\n",
    "        # Append the version to the list\n",
    "        versions.append(package_version)\n",
    "    except Exception:  # Use a more general exception for broader compatibility\n",
    "        versions.append('Not installed')\n",
    "\n",
    "# Print the versions\n",
    "for package, version in zip(packages, versions):\n",
    "    print(f'{package}: {version}')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}