Statistical Methods for Survival Data Analysis - Third Edition potx

[...]... the methods have been widened from their historical use in cancer and reliability research to business, criminology, epidemiology, and social and behavioral sciences The third edition of Statistical Methods for Survival Data Analysis is intended to provide a comprehensive introduction of the most commonly used methods for analyzing survival data It begins with basic definitions and interpretations of survival. .. There are many excellent books on clinical trials We therefore have deleted the two chapters on the subject that were in the second edition Instead, we have included discussions of more statistical methods for survival data analysis A brief summary of the improvements made for the third edition is given below 1 Two additional distributions, the log-logistic distribution and a generalized gamma distribution,... PRELIMINARIES This book is for biomedical researchers, epidemiologists, consulting statisticians, students taking a first course on survival data analysis, and others interested in survival time study It deals with statistical methods for analyzing survival data derived from laboratory studies of animals, clinical and epidemiologic studies of humans, and other appropriate applications Survival time can be... Polychotomous Outcomes 377 14.1 Univariate Analysis, 378 14.2 Logistic and Conditional Logistic Regression Models for Dichotomous Responses, 385 14.3 Models for Polychotomous Outcomes, 413 Bibliographical Remarks, 425 Exercises, 425 Appendix A Newton Raphson Method 428 Appendix B Statistical Tables 433 References 488 Index 511 Preface Statistical methods for survival data analysis have continued to flourish... death Therefore, survival time can be tumor-free time, the time from the start of treatment to response, length of remission, and time to death Survival data can include survival time, response to a given treatment, and patient characteristics related to response, survival, and the development of a disease The study of survival data has focused on predicting the probability of response, survival, or... exercise section for the reader to analyze These data are referred to in the various chapters In Part II (Chapters 4 and 5) we introduce some of the most widely used nonparametric methods for estimating and comparing survival distributions Chapter 4 deals with the nonparametric methods for estimating the three survival functions: the Kaplan and Meier product-limit (PL) estimate and the life-table technique... appropriate statistical model for survival time has been constructed and its parameters estimated, its information can help predict survival, develop optimal treatment regimens, plan future clinical or laboratory studies, and so on The graphical technique is a simple informal way to select a statistical model and estimate its parameters When a statistical distribution is found to fit the data well, the... parametric techniques can be substituted for by their nonparametric competitors In fact, a large percentage of survival studies in clinical or epidemiological journals are analyzed by nonparametric methods Researchers not interested in survival     7 model fitting should read the chapters and sections on nonparametric methods Computer programs for survival data analysis are available in several... 2.1a, represents low survival rate or short survival time A gradual or flat survival curve such as in Figure 2.1b represents high survival rate or longer survival The survivorship function or the survival curve is used to find the 50th percentile (the median) and other percentiles (e.g., 25th and 75th) of survival time and to compare survival distributions of two or more groups The median survival times in... linear interpolation For BCG patients the median survival time was about 18.2 months The median survival time for the C parvum group cannot be calculated since 15 of the 19 patients were still alive Most computer programs give not only S(t) but also the standard error of S(t), and the 7 5-,   5 0-, and 25-percentile points Figure 3.1 plots the estimated survival function S(t) for patients receiving .