May 1, 2024
3 minute read
Survival analysis is a branch of statistics that deals with the analysis of time-to-event data. It is used to study the occurrence of events that happen over time, such as the time to failure of a machine, the time to recovery from an illness, or the time to death. Survival analysis can be used to estimate the probability of an event occurring at a given time, as well as the median and mean times to event.
Types of Survival Analysis
There are two main types of survival analysis: parametric and non-parametric. Parametric survival analysis assumes that the data follows a particular distribution, such as the exponential distribution or the Weibull distribution. Non-parametric survival analysis does not make any assumptions about the distribution of the data.
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Find a path to becoming a Survival Analysis. Learn more at:
OpenCourser.com/topic/c0ovs1/survival
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Survival Analysis.
This handbook provides a comprehensive overview of survival analysis, covering both the theoretical foundations and practical applications of the techniques. It is an excellent resource for researchers who want to learn more about this topic.
On survival analysis offers comprehensive coverage of survival models in a format specifically designed for R users. The author has expertise in this subject matter, being the original developer of the R survival package.
Provides a comprehensive overview of statistical methods for survival data analysis. It valuable resource for researchers who want to learn more about this topic.
Provides a comprehensive overview of accelerated failure time models. It covers both the theoretical foundations and practical applications of the techniques. It valuable resource for researchers who want to learn more about this topic.
Provides a practical guide to survival analysis in public health. It covers the essential topics, and it includes many examples and exercises.
Provides a comprehensive overview of survival analysis, covering both the theoretical foundations and practical applications of the techniques. It is well-written and easy to follow, making it an excellent choice for both beginners and more experienced researchers.
Provides an introduction to survival analysis using SAS. It covers the essential topics, and it includes many examples and exercises.
Develops survival analysis methods from probability models for lifetime data and discusses applications to engineering, science, and business. The author is an expert in this subject with more than 40 years of experience.
Provides a comprehensive overview of Bayesian survival analysis. It covers both the theoretical foundations and practical applications of the techniques. It valuable resource for researchers who want to learn more about this topic.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/c0ovs1/survival
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