Data collected on the time to an event-such as the death of a patient in a medical study-is known as survival data. The methods for analyzing survival data can also be used to analyze data on the time to events such as the recurrence of a disease or relief from symptoms. Modelling Survival Data in Medical Research begins with an introduction to survival analysis and a description of four studies in which survival data was obtained. These and other data sets are then used to illustrate the techniques presented in the following chapters, including the Cox and Weibull proportional hazards models; accelerated failure time models; models with time-dependent variables; interval-censored survival data; model checking; and use of statistical packages. Designed for statisticians in the pharmaceutical industry and medical research institutes, and for numerate scientists and clinicians analyzing their own data sets, this book also meets the need for an intermediate text which emphasizes the application of the methodology to survival data arising from medical studies.

Rese�a del editor:

In the course of medical research, data on the time to the occurrence of a partic� of a patient, are frequently encountered. Such data ular event, such as the death are generically referred to as survival data. However, the event of interest need not necessarily be death, but could, for example, be the end of aperiod spent in remission from a disease, relief from symptoms, or the recurrence of a particular condition. Although there are a number of books devoted to the analysis of sur� vival data, this book is designed to meet the need for an intermediate text which emphasises the application of the methodology to survival data arising from med� ical studies, which shows how widely-available computer software can be used in survival analysis, and which will appeal to statisticians engaged in medical re� search. This book is based on a course on the analysis of survival data from clinical trials which has been given annually by the Statistical Services Centre ofthe Department of Applied Statistics, University of Reading, since 1986. Although it is written primarily for those working as statisticians in the pharmaceutical industry and in medical research institutes, much of the text should be accessible to numerate scientists and clinicians working alongside statisticians on the analysis of their own data sets. This book could also be used as a text to accompany undergraduate and postgraduate courses on survival analysis in universities and other institutes of higher education.

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