Using real-world data to assess representativeness and improve generalizations of study findings
Randomized trials are considered the gold standard for estimating causal effects. Trial findings are often used to inform policy and programming efforts, yet their results may not generalize well to a relevant target population if the trial sample is not representative of the population of interest. More specifically, generalizations will be hindered if a trial is not similar to the population with respect to characteristics that moderate the treatment effect. Statistical methods have been developed to assess representativeness and improve upon generalizability by combining trials with data from non-experimental studies.