Statistical power in randomized intervention studies with noncompliance.
This study examined various factors that affect statistical power in randomized intervention studies with noncompliance. On the basis of Monte Carlo simulations, this study demonstrates how statistical power changes depending on compliance rate, study design, outcome distributions, and covariate information. It also examines how these factors influence power in different methods of estimating intervention effects. Intent-to-treat analysis and complier average causal effect estimation are compared as 2 alternative ways of estimating intervention effects under noncompliance. The results of this investigation provide practical implications in designing and evaluating intervention studies taking into account noncompliance.
Jo
Booil
B
Social Research Methodology Division, Graduate School of Education and Information Studies, University of California, Los Angeles 90095-1521, USA. booil@ucla.edu
D006801:Humans; D008960:Models, Psychological; D016032:Randomized Controlled Trials as Topic; D016312:Treatment Refusal / Q000706:statistics & numerical data*