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

TL;DR

This paper presents an outcome model approach using Random Survival Forests to translate RCT results to a target population, showing improved efficiency and different effect estimates compared to traditional weighting methods.

Contribution

It introduces an outcome model method for RCT result translation and demonstrates its advantages over weighting approaches using real trial and target data.

Findings

Valsartan treatment effect was consistent with the trial.

Nateglinide treatment effect was stronger in the target population.

Outcome models provided more efficient estimates than weighting methods.

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 sub-populations, and the results compared to the traditional weighting approach. 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.