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.
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