Using mathematics to make better decisions around Standards of Care, Clinical Guidelines, and Health Strategies Mathematics of Medical Decisions provides a unified mathematical framework for analyzing the unique challenges of medical choices, from individual patient care decisions to population-level healthcare policies. The text begins by establishing a formal foundation for the concepts of probability and utility, going on to describe essential tools such as decision trees, Bayesian analysis, and utility theory. It progresses to Markov models, screening policies, and prospect theory, amongst other advanced topics. The author demonstrates how mathematical reasoning can guide diagnostic, therapeutic, and policy decisions in medicine - enabling readers to evaluate trade-offs, quantify uncertainty, and design rational approaches to patient care decisions. Combining theoretical rigor with practical application, Mathematics of Medical Decisions: Provides mathematical foundations for designing and evaluating predictive models used in diagnostic and treatment decisionsPlaces special emphasis on how underlying assumptions can be tested to validate an analysisIntegrates survival modeling and cost considerations into comprehensive decision frameworksHighlights the interplay between normative and descriptive theories of medical treatment and diagnosis decision makingAddresses quantitative modeling and clinical reasoning to support both patient-specific and policy-level medical decisionsIncludes a wealth of clear examples linking abstract mathematical principles to real-world scenarios, including health policy design Ideal for medical students, health economics students, and those in biomedical engineering or applied mathematics, Mathematics of Medical Decisions is a key resource for graduate and advanced undergraduate curricula in medical and quantitative sciences. It is particularly well suited for courses in medical decision analysis, health policy modeling, and biostatistics.
Michael C. Higgins is Adjunct Professor in the Division of Computational Medicine at Stanford School of Medicine. His research focuses on quantitative methods for clinical decision analysis and the development of mathematical models to improve medical outcomes.
ForewordPrefaceAbout the Companion Website1 Introduction - Medical decisions2 Probabilistic Analysis - Quantifying uncertainty3 Decision Trees - Structuring decision problems4 Sensitivity Analysis - Analyzing the analysis5 Determining Probabilities - Empirical and subjective methods6 Survival Models - Representing the uncertainty about the length of life7 Utility Fundamentals - Risk attitudes and quantifying preferences8 Parametric Utility Models - Simplifying the representation of risk attitudes9 Multidimensional Utility Models - Adjusting for the quality of life10 Markov Models - Representing the dynamics of uncertainty11 Utility for Time-Varying Outcomes - Preferences for when things happen12 Decision Thresholds - Deciding when to act13 Receiver Operating Characteristic Curves - Graphical analysis of diagnostic performance14 Influence Diagrams - Scalable alternative to decision trees15 Analyzing Screening Protocols - Predicting how periodic testing affects patient outcomes16 Monetary Costs - The elephant in the exam room17 Prospect Theory - A possible paradigm shift for decision analysis