Randomized controlled trials provide evidence for regulatory agencies, shape clinical practice, influence formulary decisions and have important implications for patients. However, many patient groups that are major consumers of drugs are underrepresented. We review 3 methods that use information from real world data (RWD) to extrapolate evidence from trial participants to different target populations following market approval and discuss how they could be implemented to support regulatory and health technology assessment (HTA) decisions. The strategies include 1) re-weighting (standardization) methods, 2) cross-design synthesis, and 3) discrete event simulation. As a motivating example, we work through a fictional case-study of a phase III trial investigating the efficacy of a new drug, fantastistatin, compared to the standard of care, normastatin, on risk of major adverse cardiac events. The three methods share the ability to provide estimated measures of occurrence (e.g. rates) and measures of effect (e.g. risk differences and risk ratios) in populations different from enrolled trial participants. The methods have some shared limitations. They require that relevant outcomes and risk factors are measurable, measured, and that algorithms used to define conditions capture approximately the same clinical concepts in trial and observational data. Additionally, when the target population has characteristics that are underrepresented in the trial, a few trial participants can have a great deal of influence on weights or extrapolation algorithms. The methods have unique strengths and limitations that may make one more appropriate than another in different contexts. While these methods are not a substitute for less restrictive pre-approval RCTs or rigorous observational studies after accumulation of sufficient data post-approval, they can help fill the evidence gap in the early marketing period. Early evidence using methods to generalize trial evidence should be reported with clear explanation of assumptions and limitations when used to support regulatory and health technology assessment decisions.