Refined moderation analysis with categorical outcomes in precision medicine

Moderation analysis is an integral part of precision medicine research. Concerning moderation analysis with categorical outcomes, we start with an interesting observation, which shows that heterogeneous treatment effects could be equivalently estimated via a role exchange between the outcome and the treatment variable in logistic regression models. Hence two estimators of moderating effects can be obtained. We then established the joint asymptotic normality for the two estimators, on which basis refined inference can be made for moderation analysis. The improved precision is helpful in addressing the lack-of-power problem that is common in search of moderators. The above-mentioned results hold for both experimental and observational data. We investigate the proposed method by simulation and provide an illustration with data from a randomized trial on wart treatment.

Automated summary

Moderation analysis is crucial in precision medicine research. By exchanging the roles of outcome and treatment variable, equivalent estimation of heterogeneous treatment effects can be achieved in logistic regression models. This study establishes the joint asymptotic normality for the two estimators, enabling refined inference in moderation analysis.