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Regression-based estimation of heterogeneous treatment effects when extending inferences from a randomized trial to a target population

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Abstract

Most work on extending (generalizing or transporting) inferences from a randomized trial to a target population has focused on estimating average treatment effects (i.e., averaged over the target population’s covariate distribution). Yet, in the presence of strong effect modification by baseline covariates, the average treatment effect in the target population may be less relevant for guiding treatment decisions. Instead, the conditional average treatment effect (CATE) as a function of key effect modifiers may be a more useful estimand. Recent work on estimating target population CATEs using baseline covariate, treatment, and outcome data from the trial and covariate data from the target population only allows for the examination of heterogeneity over distinct subgroups. We describe flexible pseudo-outcome regression modeling methods for estimating target population CATEs conditional on discrete or continuous baseline covariates when the trial is embedded in a sample from the target population (i.e., in nested trial designs). We construct pointwise confidence intervals for the CATE at a specific value of the effect modifiers and uniform confidence bands for the CATE function. Last, we illustrate the methods using data from the Coronary Artery Surgery Study (CASS) to estimate CATEs given history of myocardial infarction and baseline ejection fraction value in the target population of all trial-eligible patients with stable ischemic heart disease.

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Data Availability

The analyses in our paper used CASS research materials obtained from the National Heart, Lung, and Blood Institute (NHLBI) Biologic Specimen and Data Repository Information Coordinating Center.

Code availability

Code is available online at GitHub [https://github.com/serobertson/GeneralizabilityCATE].

Abbreviations

CASS:

Coronary Artery Surgery Study

CATE:

Conditional average treatment effect

MI:

Myocardial infarction

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Funding

This work was supported in part by Agency for Healthcare Research and Quality (AHRQ) award R36HS028373-01 and Patient-Centered Outcomes Research Institute (PCORI) awards ME-1502-27794, ME-2019C3-17875, and ME-2021C2-22365, and National Library of Medicine (NLM) award R01LM013616.  The content of this paper is solely the responsibility of the authors and does not necessarily represent the official views of PCORI, the PCORI Board of Governors, or the PCORI Methodology Committee, NLM, or the CASS investigators.

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Authors and Affiliations

  1. CAUSALab, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Sarah E. Robertson & Issa J. Dahabreh

  2. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA

    Sarah E. Robertson & Issa J. Dahabreh

  3. Department of Biostatistics, Brown University School of Public Health, Providence, RI, USA

    Jon A. Steingrimsson

  4. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Issa J. Dahabreh

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  1. Sarah E. Robertson
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All authors were involved in drafting the manuscript and have read and approved the final version submitted. SER conducted the statistical analysis.

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Correspondence to Issa J. Dahabreh.

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Robertson, S.E., Steingrimsson, J.A. & Dahabreh, I.J. Regression-based estimation of heterogeneous treatment effects when extending inferences from a randomized trial to a target population. Eur J Epidemiol 38, 123–133 (2023). https://doi.org/10.1007/s10654-022-00901-5

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