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Updated: Jun 3, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Statistical methods to adjust for the effects on intervention compliance in randomized clinical trials where
Nur Hani Zainal1, Corina Benjet2, Yesica Albor2
1Department of Psychology, National University of Singapore, Singapore, Singapore.
Background:
Heterogeneity of treatment effects (HTEs) can occur because of either differential treatment compliance or differential treatment effectiveness. This distinction is important, as it has action implications, but it is unclear how to distinguish these two possibilities statistically in precision treatment analysis given that compliance is not observed until after randomization. We review available statistical methods and illustrate a recommended method in secondary analysis in a trial focused on HTE.
Methods:
The trial randomized n = 880 anxious and/or depressed university students to guided internet-delivered cognitive behavioral therapy (i-CBT) or treatment-as-usual (TAU) and evaluated joint remission. Previously reported analyses documented superiority of i-CBT but significant HTE. In the reanalysis reported here, we used baseline (i.e., pre-randomization) covariates to predict compliance among participants randomized to guided i-CBT, generated a cross-validated within-person expected compliance score based on this model in both intervention groups, and then used this expected composite score as a predictor in an expanded HTE analysis.
Results:
The significant intervention effect was limited to participants with high expected compliance. Residual HTE was nonsignificant.
Conclusions:
Future psychotherapy HTE trials should routinely develop and include expected compliance composite scores to distinguish the effects of differential treatment compliance from the effects of differential treatment effectiveness.
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