Critical care trials evaluate the effect of interventions in patients with diverse personal histories and causes of illness, often under the umbrella of heterogeneous clinical syndromes, such as sepsis or acute respiratory distress syndrome. Given this variation, it is reasonable to expect that the effect of treatment on outcomes may differ for individuals with variable characteristics. However, in randomized controlled trials, efficacy is typically assessed by the average treatment effect (ATE), which quantifies the average effect of the intervention on the outcome in the study population. Importantly, the ATE may hide variations of the treatment's effect on a clinical outcome across levels of patient characteristics, which may erroneously lead to the conclusion that an intervention does not work overall when it may in fact benefit certain patients. In this review, we describe methodological approaches for assessing heterogeneity of treatment effect (HTE), including expert-derived subgrouping, data-driven subgrouping, baseline risk modeling, treatment effect modeling, and individual treatment rule estimation. Next, we outline how insights from HTE analyses can be incorporated into the design of clinical trials. Finally, we propose a research agenda for advancing the field and bringing HTE approaches to the bedside.

中文翻译：

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实现重症监护研究的精确性：观察和干预研究的方法。


重症监护试验评估干预措施对具有不同个人病史和病因的患者的效果，通常是在脓毒症或急性呼吸窘迫综合征等异质临床综合征的保护下。鉴于这种差异，可以合理地预期，对于具有不同特征的个体，治疗对结果的影响可能会有所不同。然而，在随机对照试验中，疗效通常通过平均治疗效果（ATE）来评估，它量化了干预措施对研究人群结果的平均效果。重要的是，ATE 可能会隐藏治疗对不同患者特征水平的临床结果的影响的差异，这可能会错误地得出这样的结论：干预措施实际上可能使某些患者受益，但总体上不起作用。在这篇综述中，我们描述了评估治疗效果（HTE）异质性的方法学方法，包括专家派生的亚组、数据驱动的亚组、基线风险模型、治疗效果模型和个体治疗规则估计。接下来，我们概述了如何将 HTE 分析的见解纳入临床试验的设计中。最后，我们提出了一个研究议程，以推进该领域并将 HTE 方法引入临床。