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Course

Survival Analysis in Python

AdvancedSkill Level

4.7+

Updated 06/2024

Use survival analysis to work with time-to-event data and predict survival time.

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PythonProbability & Statistics4 hr16 videos48 Exercises3,850 XP5,739Statement of Accomplishment

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Course Description

How long does it take for flu symptoms to show after exposure? And what if you don't know when people caught the virus? Do salary and work-life balance influence the speed of employee turnover? Lots of real-life challenges require survival analysis to robustly estimate the time until an event to help us draw insights from time-to-event distributions. This course introduces you to the basic concepts of survival analysis. Through hands-on practice, you’ll learn how to compute, visualize, interpret, and compare survival curves using Kaplan-Meier, Weibull, and Cox PH models. By the end of this course, you’ll be able to model survival distributions, build pretty plots of survival curves, and even predict survival durations.

Prerequisites

Introduction to Regression with statsmodels in Python Hypothesis Testing in Python

1

Introduction to Survival Analysis

What problems does survival analysis solve, and what is censorship? You’ll answer these questions as you explore survival analysis data, build survival curves, and make basic estimations of survival time.

What is survival analysis?

What problems does survival analysis solve?

Choose the right data for survival analysis

Why use survival analysis?

Identify the censorship type

Preprocess censored data

First look at censored data

Your first survival curve!

Draw a survival curve

Long live democracy!

2

Survival Curve Estimation

In this chapter, you’ll learn how the Kaplan-Meier model works and how to fit, visualize, and interpret it. You’ll then apply this model to explore how categorical variables affect survival and learn how to supplement your analysis using hypothesis testing methods like the log-rank test.

Fitting a Kaplan-Meier estimator

How to fit a Kaplan-Meier estimator?

Heart disease patient survival

Visualizing your Kaplan-Meier model

Plotting the survival curve

Patient soreness treatment

Applying survival analysis to groups

Senators' terms in office

Comparing patient soreness treatments

The log-rank test

Appropriate data for log-rank test

Plotting and comparing survival curves

Log-rank test

3

The Weibull Model

Discover how to model time-to-event data with parametric models. Learn how to use the Weibull model and the Weibull AFT model and what different purposes they serve. Use survival regression to make inferences about how covariates affect the survival function and learn how to select the best survival model for your data.

Fitting the Weibull model

Model prison data with Weibull

Compare Weibull model parameters

Weibull model with covariates

Analyze heart patients characteristics

Explore gender-LVDD interaction

Visualization and prediction with Weibull model

How do prior arrests impact re-arrest rate?

Predict re-arrest rate

Other distributions and model selection

How good is the fit?

Choose a parametric model

4

The Cox PH Model

Another chapter, another model! In this final chapter, you'll learn about the proportional hazards assumption and the role it plays in fitting and interpreting the Cox Proportional Hazards model. You’ll also learn how to predict new subjects' survival times using the Cox Proportional Hazards model.

Fitting the Cox Proportional Hazards model

Model prison data with Cox PH

Custom Cox PH model

Interpreting the Cox PH model

Cox PH model survival time

Plot covariate effects on survival

The proportional hazards assumption

Test the PH assumption with KM curves

Test the PH assumption automatically

Predicting with the Cox PH model

Employee churn study

Predict before they leave!

Congratulations!

Survival Analysis in Python

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Tung

2 months ago

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Lawal

2 months ago

i enjoyed the course but would appreciate more healthcare related context

Jason

2 months ago

Aditya Krishna

2 months ago

Todor

2 months ago

Michael

2 months ago

Jason

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FAQs

What survival analysis models does this course teach?

You will learn to fit and interpret Kaplan-Meier, Weibull, Weibull AFT, and Cox Proportional Hazards models, covering both nonparametric and parametric approaches.

What is censoring and does the course explain it?

Censoring occurs when the event of interest has not yet happened for some subjects by the study end. Chapter 1 introduces censoring as a fundamental concept and shows how to handle it.

What real-world scenarios are explored in the exercises?

You will analyze flu symptom onset times and employee turnover data, examining how factors like salary and work-life balance affect time-to-event outcomes.

What Python prerequisites do I need for this advanced course?

You need pandas, seaborn, statsmodels regression, hypothesis testing, sampling, and intermediate Python. This is an advanced statistics course with substantial prerequisites.

Can I make predictions with the models learned in this course?

Yes. The final chapter teaches you how to predict survival durations for new subjects using the Cox Proportional Hazards model.

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