Randomized controlled trials (RCTs) can provide unbiased estimates of sample average treatment effects. However, a common concern is that RCTs may fail to provide unbiased estimates of population average treatment effects. We derive assumptions sufficient for identifying population average treatment effects from RCTs. We advocate relying on stronger identification assumptions than required because the stronger assumptions allow for falsification tests. We offer new research designs for estimating population effects that use non-randomized studies (NRSs) to adjust the RCT data. This approach is considered in a cost-effectiveness analysis of a clinical intervention, Pulmonary Artery Catheterization (PAC).