title: Prescriptive models to support decision making in genetics.
abstract: Formal prescriptive models can help patients and clinicians better understand the risks and uncertainties they face and better formulate well-reasoned decisions. Using Bayes rule, the clinician can interpret pedigrees, historical data, physical findings and laboratory data, providing individualized probabilities of various diagnoses and outcomes of pregnancy. With the advent of screening programs for genetic disease, it becomes increasingly important to consider the prior probabilities of disease when interpreting an abnormal screening test result. Decision trees provide a convenient formalism for structuring diagnostic, therapeutic and reproductive decisions; such trees can also enhance communication between clinicians and patients. Utility theory provides a mechanism for patients to understand the choices they face and to communicate their attitudes about potential reproductive outcomes in a manner which encourages the integration of those attitudes into appropriate decisions. Using a decision tree, the relevant probabilities and the patients' utilities, physicians can estimate the relative worth of various medical and reproductive options by calculating the expected utility of each. By performing relevant sensitivity analyses, clinicians and patients can understand the impact of various soft data, including the patients' attitudes toward various health outcomes, on the decision making process. Formal clinical decision analytic models can provide deeper understanding and improved decision making in clinical genetics.
mesh_terms: D000649:Amniocentesis / Q000523:psychology; D001499:Bayes Theorem; D003657:Decision Making; D005817:Genetic Counseling; D030342:Genetic Diseases, Inborn / Q000517:prevention & control*; D006801:Humans; D008957:Models, Genetic; D008960:Models, Psychological; D010290:Parents / Q000523:psychology; D010375:Pedigree; D012306:Risk; D012309:Risk-Taking