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.