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Learning about treatment effects in a new target population under transportability assumptions for relative effect measures

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Abstract

Investigators often believe that relative effect measures conditional on covariates, such as risk ratios and mean ratios, are “transportable” across populations. Here, we examine the identification of causal effects in a target population using an assumption that conditional relative effect measures are transportable from a trial to the target population. We show that transportability for relative effect measures is largely incompatible with transportability for difference effect measures, unless the treatment has no effect on average or one is willing to make even stronger transportability assumptions that imply the transportability of both relative and difference effect measures. We then describe how marginal (population-averaged) causal estimands in a target population can be identified under the assumption of transportability of relative effect measures, when we are interested in the effectiveness of a new experimental treatment in a target population where the only treatment in use is the control treatment evaluated in the trial. We extend these results to consider cases where the control treatment evaluated in the trial is only one of the treatments in use in the target population, under an additional partial exchangeability assumption in the target population (i.e., an assumption of no unmeasured confounding in the target population with respect to potential outcomes under the control treatment in the trial). We also develop identification results that allow for the covariates needed for transportability of relative effect measures to be only a small subset of the covariates needed to control confounding in the target population. Last, we propose estimators that can be easily implemented in standard statistical software and illustrate their use using data from a comprehensive cohort study of stable ischemic heart disease.

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Availability of data and material

Interested readers can obtain CASS data by applying to the National Heart, Lung, and Blood Institute.

Code availability

Available on GitHub.

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Acknowledgements

We thank Dr. Anders Huitfeldt (École Polytechnique Fédérale, Lausanne) for helpful discussions regarding their work related to this manuscript, and Dr. Guanbo Wang (Harvard University) for helpful conversations regarding the methods proposed here.

Funding

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

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Authors and Affiliations

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

    Issa J. Dahabreh & Sarah E. Robertson

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

    Issa J. Dahabreh & Sarah E. Robertson

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

    Issa J. Dahabreh

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

    Jon A. Steingrimsson

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  1. Issa J. Dahabreh
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Correspondence to Issa J. Dahabreh.

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This research was deemed to not be human subjects research (Harvard protocol number: IRB23-0296).

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Dahabreh, I.J., Robertson, S.E. & Steingrimsson, J.A. Learning about treatment effects in a new target population under transportability assumptions for relative effect measures. Eur J Epidemiol (2024). https://doi.org/10.1007/s10654-023-01067-4

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