Bridging the gap between aggregate data and individual patient management: a Bayesian approach

Abstract

OBJECTIVES: The aim of this study was to explore whether Bayesian reasoning can be applied to therapeutic questions in a way that is similar to its application in diagnostics. METHODS: A clinically relevant, therapeutic question was formulated in accordance with Bayesian reasoning for the clinical management of patients with newly diagnosed rheumatoid arthritis (RA). Prior probability estimates of response to drug treatment (methotrexate, MTX) were obtained from the literature. As a marker of treatment response, changes in the Health Assessment Questionnaire (HAQ) scores were assessed after three months of treatment. Likelihood ratios for this marker were calculated on the basis of data from a clinical registry, using changes in the Disease Activity Score (DAS) as gold standard. Using Bayes' theorem, prior probability and likelihood ratios were combined to estimate posterior probabilities of treatment response in individual patients. RESULTS: On the basis of the literature, the prior probability of response of RA patients to MTX was estimated 45 percent. At 3 months follow-up, this probability increased to 80 percent or decreased to 23 percent, depending on the changes that were observed in Health Assessment Questionnaire scores. CONCLUSIONS: Bayesian reasoning can be applied to therapeutic issues in a way that is conceptually fully compatible with its use in diagnostics. As such, it can be used to bridge the gap between aggregate data and individual patient management.