Main description:

Modelling Survival Data in Medical Research describes the modelling approach to the analysis of survival data using a wide range of examples from biomedical research.

Well known for its nontechnical style, this third edition contains new chapters on frailty models and their applications, competing risks, non-proportional hazards, and dependent censoring. It also describes techniques for modelling the occurrence of multiple events and event history analysis. Earlier chapters are now expanded to include new material on a number of topics, including measures of predictive ability and flexible parametric models. Many new data sets and examples are included to illustrate how these techniques are used in modelling survival data.

Bibliographic notes and suggestions for further reading are provided at the end of each chapter. Additional data sets to obtain a fuller appreciation of the methodology, or to be used as student exercises, are provided in the appendix. All data sets used in this book are also available in electronic format online.

This book is an invaluable resource for statisticians in the pharmaceutical industry, professionals in medical research institutes, scientists and clinicians who are analyzing their own data, and students taking undergraduate or postgraduate courses in survival analysis.

Contents:

Survival Analysis. Some Non-Parametric Procedures. The Cox Regression Model. Model Checking in the Cox Regression Model. Parametric Proportional Hazards Models. Accelerated Failure Time and Other Parametric Models. Model Checking In Parametric Models. Time-Dependent Variables. Interval-Censored Survival Data. Frailty Models. Non-Proportional Hazards and Institutional Comparisons. Competing Risks. Multiple Events and Event History Modelling. Dependent Censoring. Sample Size Requirements for a Survival Study. Appendix A: Maximum Likelihood Estimation. Appendix B: Additional Data Sets. Bibliography. Index of Examples. Index.