• Emma J Graham Linck, Ewan C Goligher, Matthew W Semler, and Matthew M Churpek.
• Department of Biostatistics and Medical Informatics, UW-Madison, Madison, WI.
• Crit. Care Med. 2024 Sep 1; 52 (9): 143914501439-1450.

AbstractCritical 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.Copyright © 2024 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.

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