Research Methodology, Biostatistics, and Evidence-Based Practice
Study design
Randomized controlled trials (RCTs) best establish causality; cohort and case-control studies assess associations. Systematic reviews and meta-analyses synthesize evidence. Bias types: selection, information, confounding, and publication bias. Intention-to-treat analysis preserves randomization. Power and sample size depend on effect size, alpha, and beta.
Statistics
Sensitivity, specificity, positive/negative predictive values, and likelihood ratios interpret diagnostic tests. PPV/NPV depend on prevalence. Confidence intervals convey precision. Number needed to treat (NNT) = 1/absolute risk reduction. Avoid confusing statistical significance with clinical importance.
Traps
- Confusing sensitivity with PPV; PPV depends on prevalence.
- Assuming observational associations prove causation.
- Ignoring intention-to-treat vs per-protocol differences.
- Overlooking publication bias in meta-analyses.
- Misinterpreting p-values as effect size.
Worked example
A test with 90% sensitivity and 90% specificity in a population with 1% prevalence yields a low PPV (about 8%); in a high-prevalence population, PPV rises. Always consider prevalence when interpreting test results.