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An outcome model approach to transporting a randomized controlled trial results to a target population.

Publication ,  Journal Article
Goldstein, BA; Phelan, M; Pagidipati, NJ; Holman, RR; Pencina, MJ; Stuart, EA
Published in: J Am Med Inform Assoc
May 1, 2019

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.

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Published In

J Am Med Inform Assoc

DOI

EISSN

1527-974X

Publication Date

May 1, 2019

Volume

26

Issue

5

Start / End Page

429 / 437

Location

England

Related Subject Headings

  • Valsartan
  • Translational Research, Biomedical
  • Randomized Controlled Trials as Topic
  • Prediabetic State
  • Outcome Assessment, Health Care
  • Nateglinide
  • Medical Informatics
  • Machine Learning
  • Hypoglycemic Agents
  • Humans
 

Citation

APA
Chicago
ICMJE
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Goldstein, B. A., Phelan, M., Pagidipati, N. J., Holman, R. R., Pencina, M. J., & Stuart, E. A. (2019). An outcome model approach to transporting a randomized controlled trial results to a target population. J Am Med Inform Assoc, 26(5), 429–437. https://doi.org/10.1093/jamia/ocy188
Goldstein, Benjamin A., Matthew Phelan, Neha J. Pagidipati, Rury R. Holman, Michael J. Pencina, and Elizabeth A. Stuart. “An outcome model approach to transporting a randomized controlled trial results to a target population.J Am Med Inform Assoc 26, no. 5 (May 1, 2019): 429–37. https://doi.org/10.1093/jamia/ocy188.
Goldstein BA, Phelan M, Pagidipati NJ, Holman RR, Pencina MJ, Stuart EA. An outcome model approach to transporting a randomized controlled trial results to a target population. J Am Med Inform Assoc. 2019 May 1;26(5):429–37.
Goldstein, Benjamin A., et al. “An outcome model approach to transporting a randomized controlled trial results to a target population.J Am Med Inform Assoc, vol. 26, no. 5, May 2019, pp. 429–37. Pubmed, doi:10.1093/jamia/ocy188.
Goldstein BA, Phelan M, Pagidipati NJ, Holman RR, Pencina MJ, Stuart EA. An outcome model approach to transporting a randomized controlled trial results to a target population. J Am Med Inform Assoc. 2019 May 1;26(5):429–437.
Journal cover image

Published In

J Am Med Inform Assoc

DOI

EISSN

1527-974X

Publication Date

May 1, 2019

Volume

26

Issue

5

Start / End Page

429 / 437

Location

England

Related Subject Headings

  • Valsartan
  • Translational Research, Biomedical
  • Randomized Controlled Trials as Topic
  • Prediabetic State
  • Outcome Assessment, Health Care
  • Nateglinide
  • Medical Informatics
  • Machine Learning
  • Hypoglycemic Agents
  • Humans