Survival Models and Data Analysis
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Other volumes in the Wiley Series in Probability and Mathematical Statistics: Ralph A. Bradley, J. Stuart Hunter, David G. Kendall, and Geoffrey S. Watson-- Advisory Editors The Statistical Analysis of Failure Time Data John D. Kalbfleisch & Ross L. Prentice This volume collects and unifies statistical models proposed for the analysis of failure time data in the biomedical, industrial, and engineering sciences. The focus is on regression problems with survival data, specifically estimation of regression coefficients and distributional shape in the presence of censoring. Contains specific biographical notes, historical summaries, theoretical and applied problems, numerous worked examples, and computer programs. 1980 Biostatistics Casebook Edited by Rupert G. Miller, Jr., Bradley Efron, Byron Wm. Brown, Jr., and Lincoln E. Moses This book deals with the statistical aspects of actual biomedical research problems. It provides enough of the scientific background of each problem to guide the statistical approach. Using the case study method, the book discusses many new and specially developed concepts and techniques, often applying a variety of techniques to the same detailed data set. 1980 Survival Distributions: Reliability Applications in the Biomedical Sciences Alan J. Gross & Virginia A. Clark "This book is clearly arranged [and] can be recommended to students and to those who want to become acquainted with the techniques for analysing life test data from the practical standpoint."--Technometrics Describes nonparametric and parametric techniques used to achieve more reliable survival distributions in biomedical applications. Introduces commonly used survival distributions and covers applications of clinical life tables. Includes mathematical and graphical techniques for accurately selecting appropriate survival distributions to fit survival data, models for analyzing growth in reliability for clinical trials and industrial applications, a complete methodology for comparing two treatment groups when length of survival is the comparison criterion, and new help for choosing, in advance of clinical trial, the number of patients required for an adequate sample. 1975 von Elandt-Johnson, Regina C;Johnson, Norman L;
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