Does Mother Know Best? Treatment Adherence as a Function of Anticipated Treatment Benefit.

BACKGROUND: We describe bias resulting from individualized treatment selection, which occurs when treatment has heterogeneous effects and individuals selectively choose treatments of greatest benefit to themselves. This pernicious bias may confound estimates from observational studies and lead to important misinterpretation of intent-to-treat analyses of randomized trials. Despite the potentially serious threat to inferences, individualized treatment selection has rarely been formally described or assessed. METHODS: The Moving To Opportunity trial randomly assigned subsidized rental vouchers to low-income families in high-poverty public housing. We assessed the Kessler-6 psychological distress and Behavior Problems Index outcomes for 2,829 adolescents 4-7 years after randomization. Among families randomly assigned to receive vouchers, we estimated probability of moving (treatment), predicted by prerandomization characteristics (c statistic = 0.63). We categorized families into tertiles of this estimated probability of moving, and compared instrumental variable effect estimates for moving on behavior problems index and Kessler-6 across tertiles. RESULTS: Instrumental variable estimated effects of moving on behavioral problems index were most adverse for boys least likely to move (b = 0.93; 95% confidence interval: 0.33, 1.53) compared with boys most likely to move (b = 0.14; 95% confidence interval: -0.15, 0.44; P = 0.02 for treatment × tertile interaction). Effects on Kessler-6 were more beneficial for girls least likely to move compared with girls most likely to move (-0.62 vs. 0.02; interaction; P = 0.03). CONCLUSIONS: Evidence of individualized treatment selection differed by child gender and outcome and should be evaluated in randomized trial reports, especially when heterogeneous treatment effects are likely and nonadherence is common.

Duke Scholars

Author Roland Albert Matsouaka Biostatistics & Bioinformatics, Division of Biostatistics