Graphing and reporting heterogeneous treatment effects through reference classes

BackgroundExploration and modelling of individual treatment effects and treatment heterogeneity is an important aspect of precision medicine in randomized controlled trials (RCTs). The usual approach is to develop a predictive model for individual outcomes and then look for an interaction effect between treatment allocation and important patient covariates. However, such models are prone to overfitting and multiple testing, and typically demand a transformation of the outcome measurement, for example, from the absolute risk in the original RCT to log-odds of risk in the predictive model.\n\nMethodsWe show how reference classes derived from background information can be used to alleviate this problem through a two-stage approach where we first estimate a key aspect of heterogeneity in the trial population and then explore for an interaction with the treatment effect along this axis of variation. This bypasses the search for interactions, protecting against multiple testing, and allows for exploration of heterogeneous treatment effects on the original outcome scale of the RCT. This would typically be applied to multivariate models of baseline risk to assess the stability of average treatment effects with respect to the distribution of risk in the population studied. We show how local and tilting schemes based on ranking patients by baseline risk can be used as a general approach for exploring heterogeneity of treatment effect.\n\nResultsWe illustrate this approach using the single largest randomised treatment trial in severe falciparum malaria and show how the estimated treatment effect in terms of absolute mortality risk reduction increases considerably for higher risk strata.