Dynamic Prediction in Clinical Survival Analysis

ISBN-13: 9781439835333 / Angielski / Twarda / 2011 / 250 str.

Hans Van Houwelingen; Hein Putter

Opis

There is a huge amount of literature on statistical models for the prediction of survival after diagnosis of a wide range of diseases like cancer, cardiovascular disease, and chronic kidney disease. Current practice is to use prediction models based on the Cox proportional hazards model and to present those as static models for remaining lifetime after diagnosis or treatment. In contrast, Dynamic Prediction in Clinical Survival Analysis focuses on dynamic models for the remaining lifetime at later points in time, for instance using landmark models. Designed to be useful to applied statisticians and clinical epidemiologists, each chapter in the book has a practical focus on the issues of working with real life data. Chapters conclude with additional material either on the interpretation of the models, alternative models, or theoretical background. The book consists of four parts:

• Part I deals with prognostic models for survival data using (clinical) information available at baseline, based on the Cox model
• Part II is about prognostic models for survival data using (clinical) information available at baseline, when the proportional hazards assumption of the Cox model is violated
• Part III is dedicated to the use of time-dependent information in dynamic prediction
• Part IV explores dynamic prediction models for survival data using genomic data

Dynamic Prediction in Clinical Survival Analysis summarizes cutting-edge research on the dynamic use of predictive models with traditional and new approaches. Aimed at applied statisticians who actively analyze clinical data in collaboration with clinicians, the analyses of the different data sets throughout the book demonstrate how predictive models can be obtained from proper data sets.

Recenzje

"It offers several original viewpoints that make it a worthwhile addition to the literature. … For the researcher wishing to gain knowledge of survival analysis beyond that of standard introductions, this is an excellent book. It contains a lot of very useful procedures and demonstrates them in practical applications on real data from the authors’ own experience. The datasets are described in Appendix A and most of the data are available from the package dynpred (Appendix B), which also contains suitable software. On the book’s website one may find R code for each chapter in the book; this is a highly useful feature. Output and plots are also available, which makes the book useful for teaching purposes."
—Odd O. Aalen, Journal of the American Statistical Association, September 2014, Vol. 109

Spis treści

Prognostic models for survival data using (clinical) information available at baseline, based on the Cox model: The special nature of survival data. Cox regression model. Measuring the predictive value of a Cox model. Calibration and revision of Cox models. Prognostic models for survival data using (clinical) information available at baseline, when the proportional hazards assumption of the Cox model is violated: Mechanisms explaining violation of the Cox model. Non-proportional hazards models. Dealing with non-proportional hazards. Dynamic prognostic models for survival data using time-dependent information: Dynamic predictions using biomarkers. Dynamic prediction in multi-state models. Dynamic prediction in chronic disease. Dynamic prognostic models for survival data using genomic data: Penalized Cox models. Dynamic prediction based on genomic data. Appendices: Data sets. Software and website. References. Index.