选择性5-羟色胺再摄取抑制剂（SSRIs）对抑郁症的条目水平异质性治疗效应：对推断、可推广性和识别的启示
Item-Level Heterogeneous Treatment Effects of Selective Serotonin Reuptake Inhibitors (SSRIs) on Depression: Implications for Inference, Generalizability, and Identification
AI总结：
本文将IL-HTE模型扩展到多分类数据，并通过模拟与28项SSRI RCT数据分析，揭示HDRS条目间治疗效应的显著异质性及其对子量表推广和识别的意义。
摘要：
在对采用患者报告结局测量（PROMs）的随机对照试验（RCTs）进行分析时，允许治疗效应在条目水平存在异质性的项目反应理论（IRT）模型值得考虑。这些用于“条目水平异质性治疗效应”（IL-HTE）的模型能够提供更准确的统计推断，使研究者能够更好地推广其结果，并解决交互效应估计中的关键识别问题。在本研究中，我们将IL-HTE模型扩展至多分类数据，并应用该模型来确定选择性5-羟色胺再摄取抑制剂（SSRIs）对抑郁症的效应如何在抑郁评定量表的各个条目之间变化。我们首先开展蒙特卡洛模拟研究，以评估多分类IL-HTE模型在一系列条件下的表现。随后，我们将IL-HTE模型应用于来自28项RCT的条目水平数据，这些RCT使用17项汉密尔顿抑郁评定量表（HDRS-17）测量SSRIs对抑郁症的效应，并按子量表（HDRS-6）估计潜在异质性。结果表明，IL-HTE模型提供了更准确的统计推断，使结果能够推广至样本外条目，并解决了交互效应估计中的识别问题。实证应用显示，尽管SSRIs对抑郁症的平均效应是有益的（即为负）且具有统计显著性，但存在显著的IL-HTE，条目水平效应标准差的估计值几乎与平均效应一样大。我们表明，这种显著的IL-HTE主要由HDRS-6子量表条目上系统性更大的效应所驱动。IL-HTE模型有潜力为使用患者报告结局测量的临床试验中治疗效应的推断、可推广性和识别提供新的见解。
英文摘要：
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.
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