Extending Inferences from a Randomized Trial to Trial-eligible and Treatment-candidate Target Populations: Examples of Generalizability and Transportability.

Abstract

Background: When decision makers use evidence from a randomized trial to inform population-level decisions, the target population they envision rarely aligns with the population of individuals who enrolled in the trial. Here, we extend inferences from the VALIDATE-SWEDEHEART randomized trial (hereafter, the index trial), which compared the effects of bivalirudin and heparin during percutaneous coronary intervention on the risk of death, reinfarction, and bleeding, to two clinically relevant target populations: first, the trial-eligible population of individuals eligible for the index trial regardless of enrollment, and second, the treatment-candidate population of individuals who are considered candidates for bivalirudin and heparin under routine care, regardless of eligibility for the index trial.

Methods: Using data from the index trial, we fit logistic regression models for the outcome at 180 days in each group based on assigned treatment. We then standardized risk estimates to the baseline covariate distribution of the trial-eligible and treatment-candidate target populations, which were characterized using data from Swedish healthcare registries.

Results: The estimated risk difference comparing bivalirudin versus heparin was -1.1% (-3.1%, 0.9%) in the trial-eligible population and -1.0% (-3.0%, 1.0%) in the treatment-candidate population. The corresponding risk ratios were 0.92 (0.80, 1.07) and 0.93 (0.80, 1.07), respectively, aligning closely with estimates from the index trial. Absolute risks in each treatment group were, however, between 0.8 and 1.2 percentage points higher in comparison with the index trial.

Conclusions: Estimated risk ratios for the broader trial-eligible and treatment-candidate populations generally align with the findings from the index trial. While trials provide essential evidence for healthcare, questions often arise about wider, clinically relevant populations beyond the population of trial participants. By leveraging data from trials and observational data sources, we can attempt to address questions in these wider target populations.