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

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  Parametric models with coverage of

  
  


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  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

  
  






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  Non-parametric models including

  
  


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  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

  
  






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  Models with covariates explaining

  
  


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  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.