Shuangge Ma – författare

Using real data sets throughout, Survival Analysis in Medicine and Genetics introduces the latest methods for analyzing high-dimensional survival data. It provides thorough coverage of recent statistical developments in the medical and genetics fields. The text mainly addresses special concerns of the survival model. After covering the fundamentals, it discusses interval censoring, nonparametric and semiparametric hazard regression, multivariate survival data analysis, the sub-distribution method for competing risks data, the cure rate model, and Bayesian inference methods. The authors then focus on time-dependent diagnostic medicine and high-dimensional genetic data analysis. Many of the methods are illustrated with clinical examples.Emphasizing the applications of survival analysis techniques in genetics, this book presents a statistical framework for burgeoning research in this area and offers a set of established approaches for statistical analysis. It reveals a new way of looking at how predictors are associated with censored survival time and extracts novel statistical genetic methods for censored survival time outcome from the vast amount of research results in genomics.

Michael R. Kosorok has made significant contributions to biostatistics, precision medicine, machine learning and artificial intelligence, shaping the future of statistical methodology and biomedical research. Empirical Processes and Statistical Reinforcement Learning: A Festschrift in Honor of Michael R. Kosorok centres around his remarkable achievements.The book encompasses topics such as empirical processes, semiparametric inference, causal inference, reinforcement learning, artificial intelligence, and precision medicine. With contributions from leading experts in the field, it highlights Michael R. Kosorok’s pivotal role in advancing statistical methodology for cancer research and treatment regimes.This Festschrift serves both as a reference for researchers and a resource for PhD-level education in biostatistics and biomedical research.Key Features:Informs the frontiers of methodological developments and their biomedical applications.Explains empirical processes and semiparametric inference, including minimax optimality and target localization in distributed systems.Provides in-depth insights into causal inference and reinforcement learning with topics like fair representation learning, synthetic control models, and causal reinforcement learning with unmeasured confounders.Showcases advancements in precision medicine, including individualized treatment rules, outcome-weighted learning, and applications in sports analytics.Includes contributions on statistical and machine learning methods for clinical decision-making and early detection.