Assessing the generalizability of randomized trial results to target populations

Abstract

Recent years have seen increasing interest in and attention to evidence-based practices, where the "evidence" generally comes from well-conducted randomized trials. However, while those trials yield accurate estimates of the effect of the intervention for the participants in the trial (known as "internal validity"), they do not always yield relevant information about the effects in a particular target population (known as "external validity"). This may be due to a lack of specification of a target population when designing the trial, difficulties recruiting a sample that is representative of a prespecified target population, or to interest in considering a target population somewhat different from the population directly targeted by the trial. This paper first provides an overview of existing design and analysis methods for assessing and enhancing the ability of a randomized trial to estimate treatment effects in a target population. It then provides a case study using one particular method, which weights the subjects in a randomized trial to match the population on a set of observed characteristics. The case study uses data from a randomized trial of school-wide positive behavioral interventions and supports (PBIS); our interest is in generalizing the results to the state of Maryland. In the case of PBIS, after weighting, estimated effects in the target population were similar to those observed in the randomized trial. The paper illustrates that statistical methods can be used to assess and enhance the external validity of randomized trials, making the results more applicable to policy and clinical questions. However, there are also many open research questions; future research should focus on questions of treatment effect heterogeneity and further developing these methods for enhancing external validity. Researchers should think carefully about the external validity of randomized trials and be cautious about extrapolating results to specific populations unless they are confident of the similarity between the trial sample and that target population.

Trial registration: ClinicalTrials.gov NCT01583127.

Figure 1

Bias in estimating the Population Average Treatment Effect using a randomized trial sample when there is effect heterogeneity and the probability of participation in the trial is related to the characteristic driving treatment effects. P(S) denotes the probability of participation in the trial; P(Z) refers to the prevalence of a characteristic related to treatment effects and participation, P(S|Z=1) (and the related quantity reflecting the odds ratio between S and Z, OR(S,Z)) reflects the degree of association between the heterogeneity characteristic Z and participation S, and b_TZ reflects the degree of association between Z and treatment effects, as expressed in the form of the outcome model: E(Y_i) = b₀ + b_T T + b_Z Z + b_TZ TZ.