Survival Analysis Techniques For Censored And Truncated Data 2nd Edition
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Survival Analysis
Author: John P. Klein
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-06-29
Applied statisticians in many fields must frequently analyze time to event data. While the statistical tools presented in this book are applicable to data from medicine, biology, public health, epidemiology, engineering, economics, and demography, the focus here is on applications of the techniques to biology and medicine. The analysis of survival experiments is complicated by issues of censoring, where an individual's life length is known to occur only in a certain period of time, and by truncation, where individuals enter the study only if they survive a sufficient length of time or individuals are included in the study only if the event has occurred by a given date. The use of counting process methodology has allowed for substantial advances in the statistical theory to account for censoring and truncation in survival experiments. This book makes these complex methods more accessible to applied researchers without an advanced mathematical background. The authors present the essence of these techniques, as well as classical techniques not based on counting processes, and apply them to data. Practical suggestions for implementing the various methods are set off in a series of Practical Notes at the end of each section. Technical details of the derivation of the techniques are sketched in a series of Technical Notes. This book will be useful for investigators who need to analyze censored or truncated life time data, and as a textbook for a graduate course in survival analysis. The prerequisite is a standard course in statistical methodology. "This book...offers an excellent course in survival analysis for
Survival Analysis
Author: David G. Kleinbaum
language: en
Publisher: Springer Science & Business Media
Release Date: 2005
This text on survival analysis provides a straightforward and easy-to-follow introduction to the main concepts and techniques of the subject. It is based on numerous courses given by the author to students and researchers in the health sciences and is written with such readers in mind. Throughout, there is an emphasis on presenting each new topic motivated with real examples of a survival analysis investigation, and then presenting thorough analyses of real data sets. Each chapter concludes with practice exercises to help readers reinforce their understanding of the concepts covered in the chapter.
Concise Biostatistical Principles and Concepts, 2nd Edition
Author: Laurens Holmes, Jr
language: en
Publisher: Laurens Holmes, Jr
Release Date: 2025-03-18
Concise Biostatistical Principles and Concepts - Statistical Reality in Evidence Discovery Clinical medicine or surgery continues to make advances through evidence that is judged to be objectively drawn from the care of individual patients. The natural observation of individuals remains the basis for our researchable questions’ formulation and the subsequent hypothesis testing. Evidence-based medicine or surgery depends on how critical we are in evaluating evidence in order to inform our practice. These evaluations no matter how objective are never absolute but probabilistic, as we will never know with absolute certainty how to treat future patients who were not a part of our study. Despite the obstacles facing us today in an attempt to provide an objective evaluation of our patients, since all our decisions are based on a judgment of some evidence, we have progressed from expert opinion to the body of evidence from randomized controlled clinical trials, as well as cohort investigations, prospective and retrospective. The conduct of clinical trials though termed the “gold standard”, which yields more reliable and valid evidence from the data relative to non-experimental or observational designs, depends on how well it is designed and conducted prior to outcomes data collection, analysis, results, interpretation, and dissemination. The designs and the techniques used to draw statistical inferences are often beyond the average clinician’s understanding. A text that brings hypothesis formulation, analysis, and how to interpret the results of the findings is long overdue and highly anticipated. Statistical modeling which is fundamentally a journey from sample to the application of findings is essential to evidence discovery. The four past decades have experienced modern advances in statistical modeling and evidence discovery in biomedical, clinical, and population-based research. With these advances come the challenges in accurate model stipulation and application of models in scientific evidence discovery. While the application of novel statistical techniques to our data is necessary and fundamental to research, the selection of a sample and sampling method that reflects the representativeness of that sample to the targeted population is even more important. Since one of the rationale behind research conduct is to generate new knowledge and apply it to improve life situations including the improvement of patient and population health, sampling, sample size, and power estimations remain the basis for such inference. With the essential relevance of sample and sampling technique to how we come to make sense of data, the design of the study transcends statistical technique, since no statistical tool no matter how sophisticated can correct the errors of sampling. This text is written to highlight the importance of appropriate design prior to analysis by placing emphasis on subject selection and probability sample, randomization process when applicable prior to the selection of the analytic tool. In addition, it stresses the importance of biological and clinical significance in the interpretation of study findings. The basis for statistical inference, implying the quantification of random error is a random sample. When studies are conducted without random samples as often encountered in clinical and biomedical research, it is meaningless to report the findings with p value. However, in the absence of a random sample, the p-value can be applied to designs that utilize consecutive samples, and disease registries, since these samples reflect the population of interest, and hence representative sample, justifying inference and generalization. Essential to the selection of test statistics is the understanding of the scale of the measurement of the variables, especially the response, outcome or dependent variable, type of sample (independent or correlated), hypothesis, and normality assumption. In terms of the selection of statistical tests, this text is based on the scale of measurement (binary), type of sample (single, independent), and relationship (linear). For example, if the scale of measurement of the outcome variable is binary, repeated measure, and normality is not assumed, the repeated measure logistic regression model remains a feasible model for evidence discovery in using the independent variables to predict the repeated outcome. This book presents a simplified approach to evidence discovery by recommending the graphic illustration of data and normality test for continuous (ratio/interval scale) data prior to statistical test selection. Unlike current text in biostatistics, the approach taken to present these materials is very simple. First, this text uses applied statistics by illustrating what, when, where, and why a test is appropriate. Where a text violates the normality assumption, readers are presented with a non-parametric alternative. The rationale for the test is explained with a limited mathematical formula and is intended in order to stress the applied nature of biostatistics. Attempts have been made in this book to present the most commonly used statistical model in biomedical or clinical research. We believe since no book is complete to have covered the basics that will facilitate the understanding of scientific evidence discovery. We hope this book remains a useful guide, which is our intention in bridging the gap between theoretical statistical models and reality in the statistical modeling of biomedical and clinical research data. As researchers we all make mistakes and we believe we have learned from our mistakes during the past three decades hence the need to examine flaws and apply reality in the statistical modeling of biomedical and research data. We hope this text results in increased reliability in the conduct, analysis