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ForwardStatistical Thinking in Medicine: Applying Biostatistics in Clinical Practice
Clinicians routinely encounter statistical concepts such as p-values and confidence intervals in the literature and quality reports, yet few have formal training in how to interpret these statistics in context. This microcredential from The Medical College of Wisconsin develops practical "statistical thinking" skills to enhance evidence-based, patient-centered care. Participants will learn to...
- Distinguish clinical significance from statistical significance.
- Interpret relative and absolute risk, number needed to treat or harm, and measures of effect size like the odds and hazard ratios.
- Utilize Bayes' rule and likelihood ratios to interpret diagnostic tests.
- Understand elementary probability theory and the Central Limit Theorem as it relates to Biostatistics.
- Apply the appropriate statistical test to a given dataset and research question, including sample size considerations.
- Recognize common statistical pitfalls that lead to misinterpretation of results.
Supplemented by a series of story-based articles from the Wisconsin Medical Journal, clinicians will work through real-world scenarios, critically appraise the reporting of benefits and harms, and translate quantitative findings into plain language patients can understand.
Learning is primarily asynchronous with live introductory and capstone sessions. In the capstone, learners will present a brief critical appraisal of a paper relevant to their practice. Earners of this badge will demonstrate the ability to interpret core biostatistical results in peer-reviewed literature, communicate them clearly, and integrate them into shared decision-making.
Target Audience
- Physicians
- Primary Care Providers
- Residents
Learning Objectives
Participants who engage in this educational intervention will be able to:
- Distinguish statistical significance from clinical significance and explain why p-values are insufficient for clinical judgement.
- Calculate and interpret relative risk, absolute risk, number needed to treat/harm, odds ratio, and other common effect size measurements.
- Clinically interpret reported effect sizes, recognizing when relative measures overstate potential clinical benefits.
- Apply Bayes' rule and likelihood ratios to estimate post-test probability of disease for common diagnostic tests.
- Explain the relationship among sensitivity, specificity, positive/negative predictive values, and disease prevalence for diagnostic tests.
- Describe fundamental probability concepts including independent events, the central limit theorem, measures of central tendency, and probability distributions, with a focus on how these are used to determine which results are unusual.
- Utilize the right statistical test for a given dataset and research question.
Teaching Formats and Modalities
This eight-week course includes audio visual and text-based content, and includes two synchronous modules (Module 0 and 7). Synchronous sessions include an introductory lecture and syllabus, as well as a final presentation. Self-paced asynchronous Modules 1-6 include articles, audio visual materials, quizzes, and reflection prompts.
Expectations for Participation
Participants are expected to complete all of the weekly learning activities and assignments for the course and attend the online synchronous modules 0 and 7.
Participant Performance Evaluation
Successful completion of the microcredential requires weekly attendance in the self-paced modules, synchronous class participation, completion of weekly quizzes and reflections, and the delivery of a final presentation.
Dr. Calder is board certified in preventive medicine and has spent most of his medical career explaining medical information to both healthcare professionals and laypeople. Starting as a medic in the Army at age 17, he explained to fellow soldiers how to avoid illness and perform first aid. After attending college at UW-Madison and medical school at the Medical College of Wisconsin, he joined the Army again and commanded a preventive medicine unit during residency. After two years on active duty, he joined the state health department in Florida and later became state epidemiologist. In 1990, he was recruited to Merck & Co. where he taught sales people, gave lectures at major medical centers, and edited advertising material for medical accuracy. In all of these positions, Dr. Calder most enjoyed explaining medical data using stories to convey complex ideas.Available Credit
- 10.50 Hours of ParticipationHours of Participation credit.