Eric Laber – författare
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2 produkter
2 produkter
2 842 kr
Skickas inom 10-15 vardagar
The statistical study and development of analytic methodology for individualization of treatments is no longer in its infancy. Many methods of study design, estimation, and inference exist, and the tools available to the analyst are ever growing. This handbook introduces the foundations of modern statistical approaches to precision medicine, bridging key ideas to active lines of current research in precision medicine.The contributions in this handbook vary in their level of assumed statistical knowledge; all contributions are accessible to a wide readership of statisticians and computer scientists including graduate students and new researchers in the area. Many contributions, particularly those that are more comprehensive reviews, are suitable for epidemiologists and clinical researchers with some statistical training. The handbook is split into three sections: Study Design for Precision Medicine, Estimation of Optimal Treatment Strategies, and Precision Medicine in High Dimensions.The first focuses on designed experiments, in many instances, building and extending on the notion of sequential multiple assignment randomized trials. Dose finding and simulation-based designs using agent-based modelling are also featured. The second section contains both introductory contributions and more advanced methods, suitable for estimating optimal adaptive treatment strategies from a variety of data sources including non-experimental (observational) studies. The final section turns to estimation in the many-covariate setting, providing approaches suitable to the challenges posed by electronic health records, wearable devices, or any other settings where the number of possible variables (whether confounders, tailoring variables, or other) is high. Together, these three sections bring together some of the foremost leaders in the field of precision medicine, offering new insights and ideas as this field moves towards its third decade.
Empirical Processes and Statistical Reinforcement Learning
A Festschrift in Honor of Michael R. Kosorok
Inbunden, Engelska, 2026
1 895 kr
Kommande
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.