Which statistical concepts are most relevant to clinical decision-making in case discussions?

• A. Likelihoood ratios, Bayes' theorem, sensitivity, specificity, predictive values, and confidence intervals
• B. P-values and descriptive statistics only
• C. Absolute risk reductions without probabilities
• D. Regression coefficients are all you need

Answer: (D)

In clinical decision-making, you need a way to translate test results into a real-world probability that a patient actually has the disease, and to know how certain you are about that estimate. This relies on how a test performs (sensitivity and specificity), how common the disease is in the patient population (pre-test probability), and how a positive or negative result shifts your estimate after testing. Likelihood ratios are especially practical because they combine the test result into a single number that updates odds, and Bayes’ theorem provides the rule for moving from pre-test to post-test probability. Predictive values tell you, given a specific test result and disease prevalence, the actual probability of disease or non-disease, while confidence intervals show how precise those estimates are. Put together, these concepts form a direct, patient-centered framework for making decisions under uncertainty.

P-values and descriptive statistics describe data at the population level and don’t tell you how likely a particular patient is to have the disease after a test. Absolute risk reductions without context don’t connect to an individual’s baseline risk or what a test result means for them. Regression coefficients describe associations in models but don’t automatically provide actionable probabilities or decision thresholds for a specific patient.