Item-level heterogeneous treatment effects of selective serotonin reuptake inhibitors (SSRIs) on depression: implications for inference, generalizability, and identification

Abstract

Abstract Objectives In analysis of randomized controlled trials (RCTs) with patient-reported outcome measures (PROMs), Item Response Theory (IRT) models that allow for heterogeneity in the treatment effect at the item level merit consideration. These models for “item-level heterogeneous treatment effects” (IL-HTE) can provide more accurate statistical inference, allow researchers to better generalize their results, and resolve critical identification problems in the estimation of interaction effects. In this study, we extend the IL-HTE model to polytomous data and apply the model to determine how the effect of selective serotonin reuptake inhibitors (SSRIs) on depression varies across the items on a depression rating scale. Methods We first conduct a Monte Carlo simulation study to assess the performance of the polytomous IL-HTE model under a range of conditions. We then apply the IL-HTE model to item-level data from 24 RCTs measuring the effect of SSRIs on depression using the 17-item Hamilton Depression Rating Scale (HDRS-17) and estimate heterogeneity by subscale (HDRS-6). Results Our simulation results show that ignoring IL-HTE can yield standard errors that are as much as 50 % too small and create significant bias in treatment by covariate interaction effects when item-specific treatment effects are correlated with item location, and that the application of the IL-HTE model resolves these issues. Our empirical application shows that while the average effect of SSRIs on depression is beneficial (i.e., negative) and statistically significant, there is substantial IL-HTE, with estimates of the standard deviation of item-level effects nearly as large as the average effect. We show that this substantial IL-HTE is driven primarily by systematically larger effects on the HDRS-6 subscale items. Conclusions The IL-HTE model has the potential to provide new insights for the inference, generalizability, and identification of treatment effects in clinical trials using PROMs.