OBJECTIVES : To propose methods for extending inferences from randomized trial participants to the population of all eligible individuals using clinical trials nested within cohorts of trial-eligible individuals, including those who refuse randomization.

METHODS : We show that the data from clinical trials nested within cohorts can be used to identify the average treatment effect in the super-population of eligible individuals. We examine three classes of estimators of the average treatment effect: (1) outcome model-based; (2) probability of trial participation-based; and (3) doubly robust. We assess the finite-sample performance of different estimators in a simulation study. Lastly, we demonstrate the implementation of the methods using data from the Coronary Artery Surgery Study of 2099 eligible patients with coronary artery disease, of whom 780 were randomized into coronary revascularization surgery or medical therapy and 1319 refused randomization and self-selected into treatment.

RESULTS : In the simulation study, when all models were correctly specified, all estimators were approximately unbiased. The outcome-model based estimator had the lowest variance, followed closely by the two doubly robust estimators. The probability of trial participation based estimator had substantially larger variance than all other estimators. All methods produced similar results when applied to the Coronary Artery Surgery Study.

CONCLUSIONS : We propose methods for extending inferences from trial participants to the population of all trial-eligible patients, while avoiding confounding of the treatment effect among eligible individuals who refuse randomization. These methods are appropriate for clinical trials embedded in large healthcare systems.