Improving the Estimation of Site-Specific Effects and Their Distribution in Multisite Trials

被引:0
|
作者
Lee, JoonHo [1 ]
Che, Jonathan [2 ]
Rabe-Hesketh, Sophia [3 ]
Feller, Avi [4 ,5 ]
Miratrix, Luke [6 ]
机构
[1] Univ Alabama, Coll Educ, 520 Colonial Dr, Tuscaloosa, AL 35487 USA
[2] Exponent, San Francisco, CA USA
[3] Univ Calif Berkeley, Berkeley Sch Educ, 2121 Berkeley Way, Berkeley, CA 94720 USA
[4] Univ Calif Berkeley, Goldman Sch Publ Policy, 2607 Hearst Ave, Berkeley, CA 94720 USA
[5] Univ Calif Berkeley, Dept Stat, 2607 Hearst Ave, Berkeley, CA 94720 USA
[6] Harvard Univ, Harvard Grad Sch Educ, 14 Appian Way, Cambridge, MA 02140 USA
关键词
multisite trials; site-specific effects; finite-population estimand; Dirichlet process mixture; constrained Bayes; triple-goal estimator; heterogeneous treatment effects; EMPIRICAL BAYES; MONTE-CARLO; MODELS; DIRICHLET; PRECISION; HETEROGENEITY; PERFORMANCE; IMPACTS; CONTEXT; BIAS;
D O I
10.3102/10769986241254286
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
In multisite trials, researchers are often interested in several inferential goals: estimating treatment effects for each site, ranking these effects, and studying their distribution. This study seeks to identify optimal methods for estimating these targets. Through a comprehensive simulation study, we assess two strategies and their combined effects: semiparametric modeling of the prior distribution and alternative posterior summary methods tailored to minimize specific loss functions. Our findings highlight that the success of different estimation strategies depends largely on the amount of within-site and between-site information available from the data. We discuss how our results can guide balancing the trade-offs associated with shrinkage in limited data environments.
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页数:34
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