Adaptive enrichment designs allow clinical trials to restrict enrollment to a promising subpopulation at interim analysis. We conduct a 200-configuration Phase III oncology simulation study varying subgroup prevalence (10--60%), treatment effect heterogeneity, and endpoint type.
The variance inflation factor (VIF) with a threshold of 10 remains the dominant heuristic for detecting multicollinearity in regression analysis, yet this threshold was derived under asymptotic assumptions without explicit dependence on sample size. Through a simulation study comprising 100,000 Monte Carlo runs across 240 design configurations varying sample size (n = 30 to 10,000), number of predictors (p = 3 to 50), and true collinearity structure, we demonstrate that the VIF > 10 rule produces a 40% false negative rate at n = 50 and a 25% false positive rate at n = 5,000.
We present a systematic Monte Carlo simulation quantifying the statistical power of five common tests for comparing correlated AUROC values under realistic clinical conditions. Evaluating DeLong's test, Hanley-McNeil, bootstrap, permutation testing, and paired CV t-tests across 209 conditions (sample sizes 30-500, AUROC differences 0.
Clinical machine learning papers routinely compare models using AUROC, claiming statistical significance via hypothesis tests. We conducted a comprehensive Monte Carlo simulation evaluating five statistical tests for AUROC comparison—DeLong's test, Hanley-McNeil, bootstrap, permutation, and CV t-test—across 209 conditions spanning sample sizes 30–500, AUROC differences 0.
Compare 8 popular power calculators (G*Power, PASS, R pwr package, Stata power, nQuery, PS, ClinCalc, SampleSize4ClinicalTrials) on clustered designs (ICC=0.01-0.