Abstract
BackgroundEvaluation of heterogeneity of treatment effect among participants in large randomized clinical trials may provide insights as to the value of individualizing clinical decisions. The effect modeling approach to predictive heterogeneity of treatment effect analysis offers a promising framework for heterogeneity of treatment effect estimation by simultaneously considering multiple patient characteristics and their interactions with treatment to predict differences in outcomes between randomized treatments. However, its implementation in clinical research remains limited and so we provide a detailed example of its application in a randomized trial that compared raltegravir-based vs darunavir/ritonavir-based therapy as initial antiretroviral treatments for people living with HIV.MethodsThe heterogeneity of treatment effect analysis used a two-step procedure, in which a working proportional hazards model was first selected to construct an index score for ranking the treatment difference for individuals, and then a second calibration step used a non-parametric kernel approach to estimate the true treatment difference for participants with similar index scores. Sensitivity and supplemental analyses were conducted to evaluate the robustness of the results. We further explored the impact of covariates on heterogeneity of treatment effect and the choice between treatments.ResultsThe heterogeneity of treatment effect analysis showed that while there is a clear trend favoring raltegravir-based therapy over darunavir/ritonavir-based therapy for the vast majority of the target population, there were a small subset of patients, characterized by more advanced HIV disease status, for whom the choice between the two treatments might be equivocal.ConclusionsThrough this example, we illustrate how an exploratory heterogeneity of treatment effect analysis might provide further insights into the comparative efficacy of treatments evaluated in a randomized trial. We also highlight some of the issues in implementing and interpreting effect modeling analyses in randomized trials.