TY - JOUR
T1 - Matching methods for selection of participants for follow-up
AU - Stuart, Elizabeth A.
AU - Lalongo, Nicholas S.
N1 - Funding Information:
This work was supported by National Institute of Mental Health (NIMH) grants MH083846 (PI: Stuart) and MH066247 (PI: Lalongo). Thanks to Amy Goldstein at NIMH for helping to motivate this work and to Hendricks Brown and the Prevention Science Methodology Group (NIMH Grant R01MH040859) for helpful discussions.
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=77955909187&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77955909187&partnerID=8YFLogxK
U2 - 10.1080/00273171.2010.503544
DO - 10.1080/00273171.2010.503544
M3 - Article
C2 - 21221424
AN - SCOPUS:77955909187
SN - 0027-3171
VL - 45
SP - 746
EP - 765
JO - Multivariate Behavioral Research
JF - Multivariate Behavioral Research
IS - 4
ER -