  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.
