Department of Medicine: Allergy, Pulmonary and Critical Care Division; UW School of Medicine and Public Health.
HCor Research Institute, Sao Paulo, Brazil.
Curr Opin Crit Care. 2024 Oct 1;30(5):427-438. doi: 10.1097/MCC.0000000000001186. Epub 2024 Jul 4.
To date, most randomized clinical trials in critical care report neutral overall results. However, research as to whether heterogenous responses underlie these results and give opportunity for personalized care is gaining momentum but has yet to inform clinical practice guidance. Thus, we aim to provide an overview of methodological approaches to estimating heterogeneity of treatment effects in randomized trials and conjecture about future paths to application in patient care.
Despite their limitations, traditional subgroup analyses are still the most reported approach. More recent methods based on subphenotyping, risk modeling and effect modeling are still uncommonly embedded in primary reports of clinical trials but have provided useful insights in secondary analyses. However, further simulation studies and subsequent guidelines are needed to ascertain the most efficient and robust manner to validate these results for eventual use in practice.
There is an increasing interest in approaches that can identify heterogeneity in treatment effects from randomized clinical trials, extending beyond traditional subgroup analyses. While prospective validation in further studies is still needed, these approaches are promising tools for design, interpretation, and implementation of clinical trial results.
迄今为止，大多数重症监护的随机临床试验报告结果均为中性。然而，关于这些结果是否存在异质性反应，并为个性化治疗提供机会的研究正在兴起，但尚未为临床实践指南提供信息。因此，我们旨在概述估计随机试验中治疗效果异质性的方法，并推测未来在患者护理中的应用途径。
尽管存在局限性，但传统的亚组分析仍然是最常报道的方法。基于亚表型、风险建模和效应建模的更新方法在临床试验的主要报告中仍不常见，但在二次分析中提供了有用的见解。然而，需要进一步的模拟研究和后续指南来确定最有效和最稳健的方法来验证这些结果，以便最终在实践中使用。
人们越来越感兴趣于从随机临床试验中识别治疗效果异质性的方法，这些方法超越了传统的亚组分析。虽然在进一步的研究中仍然需要前瞻性验证，但这些方法是设计、解释和实施临床试验结果的有前途的工具。
