Introduction: Numerous studies have demonstrated that many physicians lack fundamental skills of interpreting and using clinical care evidence. Building a foundation for evidence-based medicine (EBM) will provide an initial scaffold that can be integrated during later clinical training. We built this module as a two-session experience to provide first- or second-year medical students the opportunity to build fundamental knowledge about treatment clinical research and how to appraise randomized trials. Methods: The first session of the module consists of a didactic or prerecorded online didactic to review basic concepts and principles about treatment research. The second session is an in-class exercise where students may take preparatory quizzes to assess readiness followed by an application exercise where teams of seven to eight students appraise a randomized trial. The module design uses several team-based learning (TBL) principles, and the module can be slightly modified to use the traditional TBL method if time permits. The module also includes two practice exercises and several supplementary learning resources. Results: We have been teaching this module to first- and second-year medical students for 9 years and have made several iterative improvements. No students have failed the courses within which this module is integrated. The module individual quizzes have averaged 60%-100%, the group quizzes > 80%, and the application exercises > 80%. We performed a pre-post assessment of alternative multiple-choice question items (10 pre, 10 post) covering the same objectives for a recent student cohort. The mean premodule scores were 47.7% and the mean postmodule scores were 71.3%. Discussion: We have used this module successfully for 9 years to teach treatment evidence. This resource represents the culmination of our collective experience with this topic. This module can be taught as a stand-alone session but is better integrated with other EBM modules (e.g., risk trials, prognosis trials, searching, etc.) to provide comprehensive knowledge about how to acquire and understand all the variables that are considered for clinical decisions.
By the end of this session, learners will be able to:
- Rank the strength of research study designs used to examine the effectiveness of treatment.
- Define the following common forms of bias or systematic error in studies of treatment and explain how their presence can lead to incorrect inferences about the effectiveness or safety of treatments: confounding, contamination, and cointerventions; selection bias; and ascertainment or detection bias.
- Explain the rationale for, and practical methods of, several steps used to minimize bias in studies of treatments, including random allocation to treatment groups, concealment of allocation to treatment groups, intention-to-treat principle, masking or blinding to treatments being received and objective criteria for determining study, standardizing cointerventions patients receive, and complete follow-up of study patients.
- Explain the risk of bias introduced when trials are stopped early.
- Given a randomized trial of treatment, demonstrate how to appraise the study critically and judge whether the study is overall sufficiently valid to inform decisions about therapy.
- Given the results from a randomized trial of treatment, properly calculate and interpret these quantitative measures or statements: risks of events in control group and experimental group; risk difference between control and experimental groups with four interpretations (absolute risk reduction, absolute risk increase, absolute benefit increase, and absolute benefit reduction); number needed to treat and number needed to harm; how the risk difference compares to the control group risk (labeled the relative risk reduction or relative risk increase); ratio of risks comparing experimental to control groups (labeled the risk ratio or relative risk); odds of events in the control group and experimental group; and ratio of odds comparing experimental to control groups (labeled the odds ratio).
- Given the results from a randomized trial of treatment, interpret properly these additional quantitative measures or statements: 95% confidence intervals, p values, and survival curves (including Kaplan-Meier curves).
- Black DM, Delmas PD, Eastell R, et al. Once-yearly zoledronic acid for treatment of postmenopausal osteoporosis. N Engl J Med. 2007;356(18):1809-1822. http://dx.doi.org/10.1056/NEJMoa067312
- Bombardier C, Laine L, Reicin A, et al. Comparison of upper gastrointestinal toxicity of rofecoxib and naproxen in patients with rheumatoid arthritis. N Engl J Med. 2000;343(21):1520-1528. http://dx.doi.org/10.1056/NEJM200011233432103
- Crites GE, Markert RJ, Goggans DS, Richardson WS. Local development of MCQ tests for evidence-based medicine and clinical decision making can be successful. Teach Learn Med. 2012;24(4):341-347. http://dx.doi.org/10.1080/10401334.2012.715258
- Crites GE, Richardson WS, Stolfi A, Sydelko BS, Markert RJ. An evidence-based clinical decision making course was successfully integrated into a medical school’s preclinical, systems-based curriculum. Med Sci Educ. 2012;22(1):17-23. http://dx.doi.org/10.1007/BF03341747
- Guyatt G, Rennie D, Meade MO, Cook DJ. Users’ Guides to the Medical Literature: Essentials of Evidence-Based Clinical Practice. 2nd ed. New York, NY: McGraw-Hill Professional; 2008.
- Parmelee D, Michaelsen LK, Cook S, Hudes PD. Team-based learning: a practical guide: AMEE Guide no. 65. Med Teach. 2012;34(5):e275-e287. http://dx.doi.org/10.3109/0142159X.2012.651179
- Pocock SJ, Travison TG, Wruck LM. How to interpret figures in reports of clinical trials. BMJ. 2008;336:1166-1169. http://dx.doi.org/10.1136/bmj.39561.548924.94
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