This work examines ways to make the best use of limited resources when selecting individuals to follow up in a longitudinal study estimating causal effects. In the setting under consideration, covariate information is available for all individuals but outcomes have not yet been collected and may be expensive to gather, and thus only a subset of the comparison participants are followed. Expressions in Rubin and Thomas (1996) show the benefits that can be obtained, in terms of reduced bias and variance of the estimated treatment effect, of selecting comparison individuals well matched to those in the treated group compared with a random sample of comparison individuals. We primarily consider nonexperimental settings but also consider implications for randomized trials. The methods are illustrated using data from the Johns Hopkins University Baltimore Prevention Program, which included data collection from age 6 to young adulthood of participants in an evaluation of 2 early elementary-school-based universal prevention programs.
ASJC Scopus subject areas
- Statistics and Probability
- Experimental and Cognitive Psychology
- Arts and Humanities (miscellaneous)