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IntervalCensored TimetoEvent Data: Methods and Applications collects the most recent techniques, models, and computational tools for intervalcensored timetoevent data. Top biostatisticians from academia, biopharmaceutical industries, and government agencies discuss how these advances are impacting clinical trials and biomedical research.Divided into three parts, the book begins with an overview of intervalcensored data modeling, including nonparametric estimation, survival functions, regression analysis, multivariate data analysis, competing risks analysis, and other models for intervalcensored data. The next part presents intervalcensored methods for current status data, Bayesian semiparametric regression analysis of intervalcensored data with monotone splines, Bayesian inferential models for intervalcensored data, an estimator for identifying causal effect of treatment, and consistent variance estimation for intervalcensored data. In the final part, the contributors use Monte Carlo simulation to assess biases in progressionfree survival analysis as well as correct bias in intervalcensored timetoevent applications. They also present adaptive decision making methods to optimize the rapid treatment of stroke, explore practical issues in using weighted logrank tests, and describe how to use two R packages.A practical guide for biomedical researchers, clinicians, biostatisticians, and graduate students in biostatistics, this volume covers the latest developments in the analysis and modeling of intervalcensored timetoevent data. It shows how uptodate statistical methods are used in biopharmaceutical and public health applications.
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