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Full Description
Too often in biostatistical research and clinical trials, a knowledge gap exists between developed statistical methods and the applications of these methods. Filling this gap, Clinical Trial Data Analysis Using R provides a thorough presentation of biostatistical analyses of clinical trial data and shows step by step how to implement the statistical methods using R. The book's practical, detailed approach draws on the authors' 30 years of real-world experience in biostatistical research and clinical development. Each chapter presents examples of clinical trials based on the authors' actual experiences in clinical drug development. Various biostatistical methods for analyzing the data are then identified. The authors develop analysis code step by step using appropriate R packages and functions. This approach enables readers to gain an understanding of the analysis methods and R implementation so that they can use R to analyze their own clinical trial data.With step-by-step illustrations of R implementations, this book shows how to easily use R to simulate and analyze data from a clinical trial. It describes numerous up-to-date statistical methods and offers sound guidance on the processes involved in clinical trials.
Contents
Introduction to R What Is R? Steps on Installing R and Updating R Packages R for Clinical Trials A Simple Simulated Clinical Trial Concluding Remarks Overview of Clinical Trials Introduction Phases of Clinical Trials and Objectives The Clinical Development Plan Biostatistical Aspects of a ProtocolTreatment Comparisons in Clinical Trials Data from Clinical Trials Statistical Models for Treatment ComparisonsData Analysis in R Treatment Comparisons in Clinical Trials with Covariates Data from Clinical Trials Statistical Models Incorporating Covariates Data Analysis in RAnalysis of Clinical Trials with Time-to-Event Endpoints Clinical Trials with Time-to-Event Data Statistical ModelsStatistical Methods for Right-Censored Data Statistical Methods for Interval-Censored Data Step-by-Step Implementations in RAnalysis of Data from Longitudinal Clinical Trials Clinical Trials Statistical ModelsAnalysis of Data from Longitudinal Clinical TrialsSample Size Determination and Power Calculation in Clinical Trials Prerequisites for Sample Size Determination Comparison of Two Treatment Groups with Continuous EndpointsTwo Binomial Proportions Time-to-Event Endpoint Design of Group Sequential Trials Longitudinal Trials Relative Changes and Coefficient of Variation: An ExtraMeta-Analysis of Clinical Trials Data from Clinical Trials Statistical Models for Meta-AnalysisMeta-Analysis of Data in RBayesian Analysis Methods in Clinical TrialsBayesian ModelsR Packages in Bayesian Modeling MCMC Simulations Bayesian Data AnalysisAnalysis of Bioequivalence Clinical Trials Data from Bioequivalence Clinical Trials Bioequivalence Clinical Trial Endpoints Statistical Methods to Analyze BioequivalenceStep-by-Step Implementation in RAnalysis of Adverse Events in Clinical Trials Adverse Event Data from a Clinical Trial Statistical MethodsStep-by-Step Implementation in RAnalysis of DNA Microarrays in Clinical Trials DNA MicroarrayBreast Cancer DataBibliographyIndexConcluding Remarks appear at the end of each chapter.
