TY - JOUR
T1 - An outcome model approach to transporting a randomized controlled trial results to a target population
AU - Goldstein, Benjamin A.
AU - Phelan, Matthew
AU - Pagidipati, Neha J.
AU - Holman, Rury R.
AU - Pencina, Michael J.
AU - Stuart, Elizabeth A.
N1 - Funding Information:
This work was supported by National Institute of Diabetes and Digestive and Kidney Diseases career development award K25 DK097279 (to BAG), U.S. Department of Education Institute of Education Sciences grant R305D150003 (to EAS). The project described was supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant Award Number UL1TR001117 at Duke University. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Publisher Copyright:
© The Author(s) 2019.
PY - 2019/3/14
Y1 - 2019/3/14
N2 - Objective: Participants enrolled into randomized controlled trials (RCTs) often do not reflect real-world populations. Previous research in how best to transport RCT results to target populations has focused on weighting RCT data to look like the target data. Simulation work, however, has suggested that an outcome model approach may be preferable. Here, we describe such an approach using source data from the 2-2 factorial NAVIGATOR (Nateglinide And Valsartan in Impaired Glucose Tolerance Outcomes Research) trial, which evaluated the impact of valsartan and nateglinide on cardiovascular outcomes and new-onset diabetes in a prediabetic population. Materials and Methods: Our target data consisted of people with prediabetes serviced at the Duke University Health System. We used random survival forests to develop separate outcome models for each of the 4 treatments, estimating the 5-year risk difference for progression to diabetes, and estimated the treatment effect in our local patient populations, as well as subpopulations, and compared the results with the traditional weighting approach. Results: Our models suggested that the treatment effect for valsartan in our patient population was the same as in the trial, whereas for nateglinide treatment effect was stronger than observed in the original trial. Our effect estimates were more efficient than the weighting approach and we effectively estimated subgroup differences. Conclusions: The described method represents a straightforward approach to efficiently transporting an RCT result to any target population.
AB - Objective: Participants enrolled into randomized controlled trials (RCTs) often do not reflect real-world populations. Previous research in how best to transport RCT results to target populations has focused on weighting RCT data to look like the target data. Simulation work, however, has suggested that an outcome model approach may be preferable. Here, we describe such an approach using source data from the 2-2 factorial NAVIGATOR (Nateglinide And Valsartan in Impaired Glucose Tolerance Outcomes Research) trial, which evaluated the impact of valsartan and nateglinide on cardiovascular outcomes and new-onset diabetes in a prediabetic population. Materials and Methods: Our target data consisted of people with prediabetes serviced at the Duke University Health System. We used random survival forests to develop separate outcome models for each of the 4 treatments, estimating the 5-year risk difference for progression to diabetes, and estimated the treatment effect in our local patient populations, as well as subpopulations, and compared the results with the traditional weighting approach. Results: Our models suggested that the treatment effect for valsartan in our patient population was the same as in the trial, whereas for nateglinide treatment effect was stronger than observed in the original trial. Our effect estimates were more efficient than the weighting approach and we effectively estimated subgroup differences. Conclusions: The described method represents a straightforward approach to efficiently transporting an RCT result to any target population.
KW - electronic health records
KW - machine learning
KW - public health informatics
KW - treatment heterogeneity
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U2 - 10.1093/jamia/ocy188
DO - 10.1093/jamia/ocy188
M3 - Article
C2 - 30869798
AN - SCOPUS:85063712401
SN - 1067-5027
VL - 26
SP - 429
EP - 437
JO - Journal of the American Medical Informatics Association
JF - Journal of the American Medical Informatics Association
IS - 5
ER -