Decision-Making & Problem Solving, Probability & Statistics, Public Health, Diagnosis, Research, Statistics 

Statistics in Practice 

Medical decision making has evolved in recent years, as morecomplex problems are being faced and addressed based onincreasingly large amounts of data. In parallel, advances incomputing have led to a host of new and powerful statistical toolsto support decision making. Simulation-based Bayesian methods areespecially promising, as they provide a unified framework for datacollection, inference, and decision making. In addition, thesemethods are simple to interpret, and can help to address the mostpressing practical and ethical concerns arising in medical decisionmaking.* Provides an overview of the necessary methodological background,including Bayesian inference, Monte Carlo simulation, and utilitytheory.* Driven by three real applications, presented as extensivelydetailed case studies.* Case studies include simplified versions of the analysis, toapproach complex modelling in stages.* Features coverage of meta-analysis, decision analysis, andcomprehensive decision modeling.* Accessible to readers with only a basic statisticalknowledge.Primarily aimed at students and practitioners of biostatistics, thebook will also appeal to those working in statistics, medicalinformatics, evidence-based medicine, health economics, healthservices research, and health policy. - from Amzon