An Interactive Workshop Reviewing Basic Biostatistics and Applying Bayes' Theorem to Diagnostic Testing and Clinical Decision-Making

Abstract

Introduction: Sensitivity, specificity, and predictive values-the basic statistics behind using and interpreting screening and diagnostic tests-are taught in all medical schools, yet studies have shown that a majority of physicians cannot correctly define and apply these concepts. Previous work has not rigorously examined this disconnect and attempted to address it.

Methods: We used adult learning theory to design a case-based interactive workshop to review biostatistics and apply them to clinical decision-making using Bayes' theorem. Participants took an anonymous multiple-choice pretest, posttest, and delayed posttest on definitions and application of the concepts, and we compared the scores between the three tests. Several experiences with early iterations provided feedback to improve the workshop but were not included for analysis.

Results: We conducted the finalized workshop with 54 pediatrics students, residents, and faculty. All learners completed the immediate pre- and posttests, and eight completed the delayed posttest. Average scores rose from 4.5/8 (56%) on the pretest to 6.5/8 (81%) on the posttest and 6.4/8 (80%) on the delayed posttest. Two-tailed t tests showed p < .001 for the difference between the pretest and both posttests, and post hoc power analysis showed a power of 99% to detect the observed differences. There was no significant difference (p = .8) between the posttest and delayed posttest.

Discussion: Our work demonstrates that an interactive workshop reviewing basic biostatistics and teaching rational diagnostic testing using Bayes' theorem can be effective in connecting theoretical knowledge of biostatistics to evidence-based decision-making in real clinical practice.

Keywords: Clinical Decision-Making; Epidemiology and Biostatistics; Evidence-Based Medicine; Medical Education.