Five Interval Estimators of the Risk Difference Under Stratified Randomized Clinical Trials with Noncompliance and Repeated Measurements

Abstract

We often employ stratified analysis to control the confounding effect due to centers in a multicenter trial or the confounding effect due to trials in a meta-analysis. On the basis of a general risk additive model, we focus discussion on interval estimation of the risk difference (RD) in repeated binary measurements under a stratified randomized clinical trial (RCT) in the presence of noncompliance. We develop five asymptotic interval estimators for the RD in closed form. These include the interval estimator using the weighted least-squares (WLS) estimator, the WLS interval estimator with tanh −1(x) transformation, the Mantel–Haenszel (MH) type interval estimator, the MH interval estimator with tanh −1(x) transformation, and the interval estimator using the idea of Fieller's theorem and a randomization-based variance. We employ Monte Carlo simulation to study and compare the finite-sample performance of these interval estimators in a variety of situations. We include an example studying the use of macrophage colony-stimulating factor to reduce the risk of febrile neutropenia events in acute myeloid leukaemia patients published elsewhere to illustrate the use of these estimators.