Randomized trials are considered the gold standard for estimating causal effects, and evidence from trials is highly regarded in decision making processes that impact entire populations. While rigorous in design, RCTs can still be flawed; leveraging data and information from additional non-experimental or “real world” studies can be advantageous for addressing statistical issues and improving inferences. This dissertation addresses two complications that arise in trials and can be addressed in this way: poor external validity and measurement error. To deal with both of these issues, it is important to consider (and account for) differences in baseline covariates between the RCT sample and the external data source. In other words, it is crucial to address how “transportable” inferences are between the two studies. This work focuses on transportability between an RCT and an external non-experimental study in two contexts: 1) when generalizing RCT findings to a well-defined target population and 2) when correcting for outcome measurement error in an RCT.