Survival Analysis with Python

Survival analysis uses statistics to calculate time to failure. Survival Analysis with Python takes a fresh look at this complex subject by explaining how to use the Python programming language to perform this type of analysis. As the subject itself is very mathematical and full of expressions and formulations, the book provides detailed explanations and examines practical implications. The book begins with an overview of the concepts underpinning statistical survival analysis. It then delves into



Parametric models with coverage of

Concept of maximum likelihood estimate (MLE) of a probability distribution parameter

MLE of the survival function

Common probability distributions and their analysis

Analysis of exponential distribution as a survival function

Analysis of Weibull distribution as a survival function

Derivation of Gumbel distribution as a survival function from Weibull

Non-parametric models including

Kaplan–Meier (KM) estimator, a derivation of expression using MLE

Fitting KM estimator with an example dataset, Python code and plotting curves

Greenwood’s formula and its derivation

Models with covariates explaining

The concept of time shift and the accelerated failure time (AFT) model

Weibull-AFT model and derivation of parameters by MLE

Proportional Hazard (PH) model

Cox-PH model and Breslow’s method

Significance of covariates

Selection of covariates The Python lifelines library is used for coding examples. By mapping theory to practical examples featuring datasets, this book is a hands-on tutorial as well as a handy reference.