Interval-Censored Time-to-Event Data

<p><strong>Interval-Censored Time-to-Event Data: Methods and Applications</strong> collects the most recent techniques models and computational tools for interval-censored time-to-event data. Top biostatisticians from academia biopharmaceutical industries and government agencies discuss how these advances are impacting clinical trials and biomedical research.</p><p></p><p>Divided into three parts the book begins with an overview of interval-censored data modeling including nonparametric estimation survival functions regression analysis multivariate data analysis competing risks analysis and other models for interval-censored data. The next part presents interval-censored methods for current status data Bayesian semiparametric regression analysis of interval-censored data with monotone splines Bayesian inferential models for interval-censored data an estimator for identifying causal effect of treatment and consistent variance estimation for interval-censored data. In the final part the contributors use Monte Carlo simulation to assess biases in progression-free survival analysis as well as correct bias in interval-censored time-to-event 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.</p><p></p><p>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 interval-censored time-to-event data. It shows how up-to-date statistical methods are used in biopharmaceutical and public health applications.</p>