Assessing the Generalizability of Randomized Trial Results to Target Populations
Top Cited Papers
- 12 October 2014
- journal article
- research article
- Published by Springer Science and Business Media LLC in Prevention Science
- Vol. 16 (3), 475-485
- https://doi.org/10.1007/s11121-014-0513-z
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.Keywords
This publication has 40 references indexed in Scilit:
- Generalizing Evidence From Randomized Clinical Trials to Target Populations: The ACTG 320 TrialAmerican Journal of Epidemiology, 2010
- Matching Methods for Causal Inference: A Review and a Look ForwardStatistical Science, 2010
- Examining How Context Changes Intervention Impact: The Use of Effect Sizes in Multilevel Mixture Meta-AnalysisChild Development Perspectives, 2008
- Evaluating bias correction in weighted proportional hazards regressionLifetime Data Analysis, 2008
- Bias Modelling in Evidence SynthesisJournal of the Royal Statistical Society Series A: Statistics in Society, 2008
- Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete DataStatistical Science, 2007
- Recent developments in meta‐analysisStatistics in Medicine, 2007
- Model-Based Direct AdjustmentJournal of the American Statistical Association, 1987
- The central role of the propensity score in observational studies for causal effectsBiometrika, 1983
- A Generalization of Sampling Without Replacement from a Finite UniverseJournal of the American Statistical Association, 1952