Research Methodology, Biostatistics, and Evidence-Based Oncology
Core concepts
Understand study designs (RCT, cohort, case-control, cross-sectional, meta-analysis), randomisation and blinding, allocation concealment, and bias (selection, information, confounding, publication). Know outcome measures: overall survival, progression-free survival, disease-free survival, response rate, quality of life, and surrogate endpoints. Apply hypothesis testing: p-values, type I/II error, power, confidence intervals, hazard ratios, Kaplan–Meier, log-rank test, Cox regression, and intention-to-treat vs per-protocol. Understand sensitivity/specificity, number needed to treat, absolute vs relative risk reduction, odds ratio vs risk ratio, and meta-analysis (heterogeneity, forest plots, funnel plots). Recognise levels of evidence and GRADE; know clinical practice guidelines and conflict of interest.
Traps
Questions test relative vs absolute benefit (a 20% relative reduction may be a 1% absolute reduction). Do not confuse hazard ratio with risk ratio. Watch intention-to-treat interpretation and non-inferiority trial logic. Distinguish statistical significance from clinical significance. Beware surrogate endpoint over-interpretation.
Worked example / rule of thumb
A trial reports HR 0.80 (95% CI 0.70–0.92) for OS: benefit is statistically significant and the CI excludes 1. If the 5-year survival rises from 50% to 55%, the absolute gain is 5% (NNT = 20). Rule of thumb: always ask for the absolute benefit and the CI, not just the p-value.