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Applied Survival Analysis: Regression Modeling of Time to Event Data, Second Edition

Comment from the Stata technical group

Applied Survival Analysis: Regression Modeling of Time to Event Data, Second Edition, by David W. Hosmer Jr., Stanley Lemeshow, and Susanne May, is an ideal choice for a semester-long course in survival analysis for health professionals. The authors provide a good overview of regression models for time-to-event data, giving the most depth to the Cox proportional hazards model.

Unlike similar texts, Applied Survival Analysis is not overly abstract or mathematical in its introduction of the concepts of survival analysis, but it instead relies on a model-building approach. As such, this text is most useful to those who are experienced in using regression models in nonsurvival settings, such as Gaussian or logistic regression. The text builds upon the reader's prior experience by showing how the usual techniques of regression and model building apply to survival data.

The text illustrates most of its analyses in Stata, and material added since the first edition mirrors Stata's growth in survival analysis, for example, the new material on multivariable fractional polynomials and frailty models.

The authors cover such topics as censoring; descriptive methods such as Kaplan–Meier; the Cox model (including estimation, model building, diagnostics, and extensions); parametric regression models (an introductory chapter); and some advanced topics, such as recurrent event models and competing risks.

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