Post‐hoc subgroup analyses in clinical trials: Essential but methodologically constrained

In our recent publication, we described results from a secondary post-hoc moderation analysis of a randomized clinical trial across race, ethnicity, and sex assigned at birth; however, due to the post-hoc nature and limited power, findings are constrained to hypothesis-generating [1]. This letter details challenges and new insights identified while conducting this research and suggestions for future directions, including methods learned belatedly that we will incorporate moving forward and hope many others will, too. Since 1993, the NIH Revitalization Act has required researchers to analyze data by socio-demographic subgroups, including race, ethnicity and sex assigned at birth [2-5]. Despite this mandate and repeated calls for equitable treatment evaluation [6-9], few alcohol treatment trials report subgroup analyses [10-12], leaving limited evidence on treatment efficacy across subgroups. When subgroup results are reported, there are often methodological challenges, such as Type 1 error (i.e. ‘significant-in-one-group-only’ bias). Formal interaction tests are more appropriate, but simulations show that they require sample sizes over four times larger than those of typical treatment trials to achieve adequate power [13]. Subgroup analyses are limited by long-standing structural barriers, including research funding and timeline caps, heterogeneity within subgroups, and differing definitions of and access to subgroups. These challenges are exacerbated by declines in the purchasing power of US research grants [14, 15], and have recently been compounded by political pressures threatening large-scale research funding cuts [16] and rollbacks in diversity initiatives [17]. In the face of these formidable challenges, researchers must make the most of existing data and engage analysis strategies that can yield meaningful insights, even from studies with small samples. One strategy is the use of meta-analytic methods to achieve adequate sample sizes for subgroup analyses, including meta-regression or individual participant data analysis [13, 18]. A valuable next step for meta-analytic reporting frameworks, such as PRISMA 2020 (Preferred Reporting Items for Systematic reviews and Meta-Analyses) [19] and the Cochrane Handbook for Systematic Reviews of Interventions [20], is to encourage and even require subgroup analyses. For clinical trials, the strengthening of reporting standards, such as extending CONSORT (Consolidated Standards of Reporting Trials) and local funder requirements to ensure transparent subgroup data collection, reporting and sharing, would help generate the requisite data sets to support future meta-analytic subgroup analyses. Beyond meta-analysis, newer frameworks such as the Predictive Approaches to Treatment Effect Heterogeneity (PATH) Statement, offer practical tools for researchers leading individual trials [21]. One option is risk modeling, which draws on relevant baseline and socio-demographic variables to stratify a participant's risk of experiencing a certain outcome and allow researchers to test whether treatment effects differ across risk levels. Relevant subgroups can be used to predict risk and can then be revisited to assess whether those at greater risk show larger or smaller treatment gains. The second approach, effect modeling, directly tests multiple treatment-by-subgroup interactions within a single model. This approach estimates treatment effects across combinations of traits, avoiding separate subgroup tests that can lead to false positives. To avoid overfitting and unstable estimates in smaller samples, researchers can pre-specify subgroups and use robust methods like penalized regression, cross-validation or Bayesian partial pooling. Collaboration among statisticians and trialists is needed to further these approaches so that modestly sized trials yield credible, equity-focused insights and, ultimately, contribute to larger meta-analyses that also test treatment effects by subgroup. As researchers, we have an ethical obligation to ensure that treatments for alcohol use disorder (AUD) work equitably for all people. We cannot wait for the system to act—we must find ways to overcome barriers to the recruitment of people from marginalized subgroups. At the same time, we must develop and utilize analysis strategies that make the most of the data we have. Silvi C. Goldstein: Writing—original draft; conceptualization; methodology. Nicole H. Weiss: Writing—review and editing. Susan E. Collins: Methodology; writing—review and editing. S.E.C. co-owns a social purpose corporation that provides trainings and consultation in harm-reduction treatment. The authors have no other competing interests to report. Code and materials are available upon request to the corresponding author.