A Machine-Learning Approach for Estimating Subgroup- and Individual-Level Treatment Effects: An Illustration Using the 65 Trial

Zia Sadique, Richard Grieve, Karla Diaz-Ordaz, Paul Mouncey, Francois Lamontagne and Stephen O’Neill
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Zia Sadique: Department of Health Services Research and Policy, London School of Hygiene & Tropical Medicine, London, UK
Richard Grieve: Department of Health Services Research and Policy, London School of Hygiene & Tropical Medicine, London, UK
Karla Diaz-Ordaz: Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK
Paul Mouncey: Clinical Trials Unit, Intensive Care National Audit & Research Centre (ICNARC), London, UK
Francois Lamontagne: Université de Sherbrooke, Quebec, Canada
Stephen O’Neill: Department of Health Services Research and Policy, London School of Hygiene & Tropical Medicine, London, UK

Medical Decision Making, 2022, vol. 42, issue 7, 923-936

Abstract: Personalizing treatment recommendations or guidelines requires evidence about the heterogeneity of treatment effects (HTE). Machine-learning (ML) approaches can explore HTE by considering many covariates, including complex interactions between them. Causal ML approaches can avoid overfitting, which arises when the same dataset is used to select covariate by treatment interaction terms as to make inferences and reduce reliance on the correct specification of fixed parametric models. We investigate causal forests (CF), a ML method based on modified decision trees that can estimate subgroup- and individual-level treatment effects, without requiring correct prespecification of the effect model. We consider CF alongside parametric approaches for estimating HTE, within the 65 Trial, which evaluates the effect of a permissive hypotension strategy versus usual care on 90-d mortality for critically ill patients aged 65 y or older with vasodilatory hypotension. Here, the CF approach provides similar estimates of treatment effectiveness for prespecified and post hoc subgroups to the parametric approach, and the results of a test for overall HTE show weak evidence of heterogeneity. The CF estimates of individual-level treatment effects, the expected effects of treatment for individuals in subpopulations defined by their covariates, suggest that the permissive hypotension strategy is expected to reduce 90-d mortality for 98.7% of patients but with 95% confidence intervals that include zero for 71.6% of patients. A ML approach is then used to assess the patient characteristics associated with these individual-level effects, and to help target future research that can identify those patient subgroups for whom the intervention is most effective. Highlights This article examines a causal machine-learning approach, causal forests (CF), for exploring the heterogeneity of treatment effects, without prespecifying a specific functional form. The CF approach is considered in the reanalysis of the 65 Trial and was found to provide similar estimates of subgroup effects to using a fixed parametric model. The CF approach also provides estimates of individual-level treatment effects that suggest that for most patients in the 65 Trial, the intervention is expected to reduce 90-d mortality but with wide levels of statistical uncertainty. The study illustrates how individual-level treatment effect estimates can be analyzed to generate hypotheses for further research about those patients who are likely to benefit most from an intervention.

Keywords: causal forests; heterogeneous treatment effects; machine learning; personalized medicine (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:42:y:2022:i:7:p:923-936

DOI: 10.1177/0272989X221100717

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