Abstract
Longitudinal randomized controlled trials generally assign individuals randomly to interventions at baseline and then evaluate how differential average treatment effects evolve over time. This study shows that longitudinal settings could benefit from Recurrent Individual Treatment Assignment (RITA) instead, particularly in the face of (dynamic) heterogeneous treatment effects. Focusing on the optimization of treatment assignment, rather than on estimating treatment effects, acknowledges the presence of unobserved heterogeneous treatment effects and improves overall intervention response when compared to intervention policies in longitudinal settings based on Randomized Controlled Trials (RCTs)-derived average treatment effects. This study develops a RITA-algorithm and evaluates its performance in a multi-period simulation setting, considering two alternative interventions and varying the extent of unobserved heterogeneity in individual treatment response. The results show that RITA learns quickly, and adapts individual assignments effectively. If treatment heterogeneity exists, the inherent focus on both exploit and explore enables RITA to outperform a conventional assignment strategy that relies on RCT-derived average treatment effects.
Highlights
Intervention studies examine the effectiveness of a particular intervention relative to the status quo or another competing intervention
When there is considerable individual variation in intervention response when estimating the effectiveness of a particular intervention using average treatment effect (ATE) results can misguide the attempts at effectively personalizing intervention; yielding potentially substantial benefit for some individuals, a little benefit for many, and even harm for a few[4,5]
Given that the ATE can be non-informative for individual treatment assignment, learning what the individualized treatment rules ought to be instead is methodologically challenging in that it requires causal results and accurate prediction estimations regarding ITE9
Summary
Intervention studies examine the effectiveness of a particular intervention relative to the status quo or another competing intervention. A single individual cannot be randomly assigned to both intervention A and B, such that one of both potential outcomes (i.e., yAi or yBi) is observed for each individual which prevents the estimation of the ITE3 This is why RCTs are frequently considered as the golden standard: randomization followed by a comparison of the generated outcomes yields the (differential) ATE. Based on the RCT results, the conclusion is that intervention A is on average more effective than intervention B, and this would generally translate into the following treatment assignment rule which states that individuals whose reading outcomes need to be improved can better receive intervention A than B The latter conclusion is tricky given that the figure shows that there is substantial individual variation in treatment response. If heterogeneous treatment effects are ignored, it can be dangerous to formulate an individual treatment
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