Estimation of the intervention effect in a non-randomized study with pre- and post-mismeasured binary responses

In non-randomized clinical studies, the regression phenomenon can confound interpretation of the effectiveness of an intervention. The regression effect arises due to daily variation and/or misclassification of the biologic marker used in selection as well as in the assessment of the intervention effect. We consider a scenario in which the selection criterion for a subject's participation in the study is such that he/she must have a positive diagnostic test at screening. The disease status is then reassessed at the end of intervention. Thus, two repeated measurements of a binary disease outcome are available, with only selected subjects having a second measurement upon follow-up. We propose methods for estimating the change in event probability resulting from implementing the intervention while adjusting for the misclassification that produces the regression effect. We extend this approach to estimation of both the placebo and intervention effects in placebo-controlled studies designed with a misclassified binary outcome. Analyses of two biomedical studies are used for illustration.