Modeling in Medical Decision Making: A Bayesian Approach

Medical decision making has evolved in recent years, as more complex problems are being faced and addressed based on increasingly large amounts of data. In parallel, advances in computing power have led to a host of new and powerful statistical tools to support decision making. Simulation–based Bayesian methods are especially promising, as they provide a unified framework for data collection, inference, and decision making. In addition, these methods are simple to implement and can help to address the most pressing practical and ethical concerns arising in medical decision making. Provides an overview of the necessary methodological background, including Bayesian inference, Monte Carlo simulation, and utility theory.Driven by three real applications, presented as extensively detailed case studies.Case studies include simplified versions of the analysis, to approach complex modelling in stages.Features coverage of meta–analysis, decision analysis, and comprehensive decision modeling.Accessible to readers with only a basic statistical knowledge.Primarily aimed at students and practitioners of biostatistics, the book will also appeal to those working in statistics, medical informatics, evidence–based medicine, health economics, health service research and health policy.

Spis treści:
Preface.
PART I: METHODS.
1. Inference.
Summary.
Medical Diagnosis.
Genetic Counseling.
Estimating sensitivity and specificity.
Chronic disease modeling.
2. Decision making.
Summary.
Foundations of expected utility theory.
Measuring the value of avoiding a major stroke.
Decision making in health care.
Cost–effectiveness analyses in the μ SPPM.
Statistical decision problems.
3. Simulation.
Summary.
Inference via simulation.
Prediction and expected utility via simulation.
Sensitivity analysis via simulation.
Searching for stra tegies via simulation.
Part II: CASE STUDIES.
4. Meta–analysis.
Summary.
Meta–analysis.
Bayesian meta–analysis.
Tamoxifen in early breast cancer.
Combined studies with continuous and dichotomous responses.
Migraine headache.
5. Decision trees.
Summary.
Axillary lymph node dissection in early breast cancer.
A simple decision tree
A more complete decision tree for ALND
6. Chronic disease modeling.
Summary.
Model overview.
Natural history model.
Modeling the effects of screening.
Comparing screening schedules.
Model critique.
Optimizing screening schedule.
References
Index.

Okładka tylna:
Medical decision making has evolved in recent years, as more complex problems are being faced and addressed based on increasingly large amounts of data. In parallel, advances in computing power have led to a host of new and powerful statistical tools to support decision making. Simulation–based Bayesian methods are especially promising, as they provide a unified framework for data collection, inference, and decision making. In addition, these methods are simple to implement and can help to address the most pressing practical and ethical concerns arising in medical decision making. Provides an overview of the necessary methodological background, including Bayesian inference, Monte Carlo simulation, and utility theory.Driven by three real applications, presented as extensively detailed case studies.Case studies include simplified versions of the analysis, to approach complex modelling in stages.Features coverage of meta–analysis, decision analysis, and comprehensive decision modeling.Accessible to readers with only a basic statistical knowledge.Primarily aimed at students and practitioners of biostatistics, the book will also appeal to those working in statistics, medical informatics, evidence–base d medicine, health economics, health service research and health policy.