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# Survival Analysis in Python

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

Comience El Curso Gratis
4 horas16 vídeos48 ejercicios
4436 aprendicesDeclaración de cumplimiento

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## Descripción del curso

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.
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1. 1

### Introduction to Survival Analysis

Gratuito

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.

Reproducir Capítulo Ahora
What is survival analysis?
50 xp
What problems does survival analysis solve?
100 xp
Choose the right data for survival analysis
50 xp
Why use survival analysis?
50 xp
Identify the censorship type
50 xp
Preprocess censored data
100 xp
First look at censored data
100 xp
50 xp
Draw a survival curve
100 xp
Long live democracy!
100 xp
2. 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.

3. 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.

4. 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.

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conjuntos de datos

Echocardiogram dataEmployee attrition dataRegimes dataPrison recidivism data

Shae Wang

Senior Data Scientist at Ripple

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