A01=Avishek Nag
accelerated failure time
Accelerated Life Model
Aft
Aft Model
Author_Avishek Nag
Baseline Hazard
Baseline Survival Function
Brier Score
Category=PBT
Category=UMX
Category=UN
Censoring
Covariate Weights
Cox PH Model
Cox regression analysis
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eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
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Exponential Distribution
Fit Cox PH Model
Fitted Weibull Model
Gumbel Distribution
Hazard Function
Hazard Function Plot
Indian ISBN
Interval Censoring
Kaplan Meier estimator
Km Curve
Log Rank Test
Low AIC Score
maximum likelihood estimation
Models with Covariates
Non-Parametric Models
Parametric Models
parametric survival models
Proportional Hazard Model
proportional hazards model
Python
Python lifelines survival analysis
Survival Analysis
Survival Distribution
Survival Function
Survival Probability
Train Dataset
True Survival Time
Weibull Aft Model
Weibull Distribution
Product details
- ISBN 9781032148267
- Weight: 400g
- Dimensions: 156 x 234mm
- Publication Date: 17 Dec 2021
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
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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
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- MLE of the survival function
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- Common probability distributions and their analysis
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- Analysis of exponential distribution as a survival function
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- Analysis of Weibull distribution as a survival function
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- 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
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- Fitting KM estimator with an example dataset, Python code and plotting curves
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- 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
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- Weibull-AFT model and derivation of parameters by MLE
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- Proportional Hazard (PH) model
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- Cox-PH model and Breslow’s method
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- Significance of covariates
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- Selection of covariates
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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.
Avishek Nag has a Masters of Technology Degree in data analytics and machine learning from Birla Institute of Technology and Science, Pilani, India. He has more than 15 years of experience in Software Development and Architecting Systems. He also has professional experience in data science and machine learning, Java, Python, Big Data, including Spark and MongoDB. He has worked at VMWare, Cisco, Mobile Iron, and Computer Science Corporation (now called DXC). He is also the author of the book Pragmatic Machine Learning with Python, which is recommended in the ACM Education Digital Library.
