Refined moderation analysis with binary outcomes in precision medicine research

Moderation analysis for evaluating differential treatment effects serves as the bedrock of precision medicine, which is of growing interest in many fields. In the analysis of data with binary outcomes, we observe an interesting symmetry property concerning the ratio of odds ratios, which suggests that heterogeneous treatment effects could be equivalently estimated via a role exchange between the outcome and treatment variable in logistic regression models. We then obtain refined inference on moderating effects by rearranging data and combining two models into one via a generalized estimating equation approach. The improved efficiency is helpful in addressing the lack-of-power problem that is common in the search for important moderators. 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 for precision medicine and can be used to evaluate differential treatment effects. In the analysis of binary outcomes, a symmetry property concerning odds ratios suggests that heterogeneous treatment effects can be estimated by exchanging the roles of the outcome and treatment variables. By combining two models into one using a generalized estimating equation approach, we obtain refined inference on moderating effects and improve efficiency in identifying important moderators. Simulation studies and a trial on wart treatment demonstrate the effectiveness of the proposed method.