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. 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 28 RCTs measuring the effect of SSRIs on depression using the 17-item Hamilton Depression Rating Scale (HDRS-17) and estimate potential heterogeneity by subscale (HDRS-6). Our results show that the IL-HTE model provides more accurate statistical inference, allows for generalizability of results to out-of-sample items, and resolves identification problems in the estimation of interaction effects. 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. The IL-HTE model has the potential to provide new insights for the inference, generalizability, and identification of treatment effects in clinical trials using patient reported outcome measures.

翻译：在使用患者报告结局指标（PROMs）的随机对照试验（RCTs）分析中，允许项目层面治疗效果存在异质性的项目反应理论（IRT）模型值得关注。这类"项目层面异质性治疗效果"（IL-HTE）模型能够提供更精确的统计推断，帮助研究者更好地推广其结果，并解决交互效应估计中的关键识别问题。本研究将IL-HTE模型扩展至多分类数据，并应用该模型探究选择性血清素再摄取抑制剂（SSRIs）对抑郁症的治疗效果在抑郁评定量表各项目间的变异情况。我们首先通过蒙特卡洛模拟研究评估多分类IL-HTE模型在不同条件下的性能表现。随后将IL-HTE模型应用于来自28项RCTs的项目层面数据（这些试验使用17项汉密尔顿抑郁评定量表（HDRS-17）测量SSRIs对抑郁症的疗效），并基于子量表（HDRS-6）估计潜在的异质性。研究结果表明：IL-HTE模型能提供更准确的统计推断，支持结果向样本外项目的可推广性，并能解决交互效应估计中的识别问题。实证应用显示，虽然SSRIs对抑郁症的平均治疗效果为正向（即负向效应）且具有统计显著性，但存在显著的IL-HTE现象——项目层面效应的标准差估计值接近平均效应值。我们进一步证明，这种显著的IL-HTE主要源于HDRS-6子量表项目上系统性更大的治疗效果。IL-HTE模型有望为使用患者报告结局指标的临床试验提供关于治疗效果推断、可推广性与识别问题的新见解。