An Outcome Model Approach to Translating a Randomized Controlled Trial Results to a Target Population

ACKNOWLEDGMENTS We thank the NAVIGATOR steering committee and investigators for access to the NAVIGATOR data Affiliations: Department of Biostatistics & Bioinformatics, Duke University, Durham, NC (BAG, MJP); Center For Predictive Medicine, Duke Clinical Research Institute, Durham, NC (BAG, MP, NHJ); Department of Medicine, Duke University, Durham, NC (NHJ); Diabetes Trials Unit, Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Oxford (RRH); Department of Biostatistics, Johns Hopkins University, Baltimore, MD (EAS) Funding: This work was supported by National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) career development award K25 DK097279 (B.A.G.), US Department of Education Institute of Education Sciences Grant R305D150003 (EAS). The project described was supported by the National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), 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 NIH. NAVIGATOR was funded by Novartis. An Outcome Model Approach to Translating a Randomized Controlled Trial Results to a Target Population Abstract Participants enrolled into randomized controlled trials (RCTs) often do not reflect real-world populations. Previous research in how best to translate 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 2x2 factorial NAVIGATOR trial which evaluated the impact of valsartan and nateglinide on cardiovascular outcomes and new-onset diabetes in a "pre-diabetic" population. Our target data consisted of people with "pre-diabetes" serviced at our institution. 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 the results compared to the traditional weighting approach.