The use of decision analytic models to inform clinical decision making in the management of hepatocellular carcinoma.
Decision analysis helps evaluate competing strategies under conditions of uncertainty in a wide variety of clinical settings. Despite some limitations, decision trees and Markov models remain essential tools for medical decision analysts. These techniques allow comparison of competing management strategies in a quantitative fashion. Sensitivity analysis is an important feature of decision analytic models that identify important factors that affect the outcome of decisions under considerations. Judiciously used, decision analytic models allow a quantitative evaluation of existing data as they relate to strategies ranging from optimizing clinical management at the patient level to allocating health care resources at the societal level.
Kim
W Ray
WR
Mayo Clinic College of Medicine, Division of Gastroenterology, Rochester, MN 55905, USA. kim.woong@mayo.edu
D006528:Carcinoma, Hepatocellular / Q000401:mortality* / Q000473:pathology / Q000628:therapy*; D002423:Cause of Death; D003131:Combined Modality Therapy; D003657:Decision Making; D005260:Female; D006801:Humans; D008113:Liver Neoplasms / Q000401:mortality* / Q000473:pathology / Q000628:therapy*; D008297:Male; D008390:Markov Chains; D008403:Mass Screening / Q000379:methods; D009367:Neoplasm Staging; D018570:Risk Assessment; D012680:Sensitivity and Specificity; D016019:Survival Analysis; D016896:Treatment Outcome