Analysis and interpretations of findings from subgroup comparisons

This thesis explores subgroup analyses within randomized trials,using cardiovascular interventions as a focal point.Reviewing 67 large trials`published between 1980 and 1997,we found that most reported on multiple single-factor subgroups without pre-specification or stastical tests for subgroup-treatment interactions.Instead of single-factor subgroups without pre-specification or stastical tests for subgroup treatment interactions.Instead of single-factor subgroup analyses,decision-makers can infer absolute benefits by appplying the relative effect size from the overall trial to baseline event rates in subgroups.We tested this approach in the SOLVD prevention (4228patients) and treatment(2569 patients)trials.We confirmed a published treatment interaction with ejection fraction(p=0.004),but successfully abolished that interaction when patients were divided into multivariate tertiles of baseline prognostic risk.This lack of significant variation could represent a beta error.We found no published method to perform a post-hoc power test for the most popular interaction test,the Breslow-Day statistic.

Autorentext

Andrea Parker graduated with a Master of Science in Chemistry from the University of Leipzig, Germany, a Master of Science in Epidemiology from Harvard School of Public Health in Boston, U.S.A., and a Ph.D. in Medical Sciences from the University of Toronto, Canada.