Evidence map›Paper›PMID 42424497›Full record

ArticleInternational journal of epidemiology2026

Repeated sampling of different individuals within the same clusters to improve precision of longitudinal estimators: the DISC design.

Jordan Downey, Avi Kenny

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Article in International journal of epidemiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Jordan DowneyDivision of Biostatistics, University of California, Berkeley, CA, United States.ORCID 0000-0002-0167-9663
Avi KennyDepartment of Biostatistics and Bioinformatics, Duke University, Durham, NC, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLongitudinal studies often involve repeated cluster sampling of a population at multiple time points, such as in difference-in-differences (DID) studies. Although cohort designs typically lead to more efficient estimators relative to repeated cross-sectional (RCS) designs, they are often impractical.

methodsWe describe the DISC (Different Individuals, Same Clusters) design, a sampling scheme that improves the precision of estimators in these settings. The DISC design represents a hybrid between a cohort and an RCS design, in which the researcher takes a single sample of clusters at baseline, but takes different cross-sectional samples of individuals within clusters at each time point.

resultsWe show analytically that the DISC design yields DID estimators with much higher precision relative to an RCS design, particularly if cluster effects are present. For example, for a design with two surveys, 40 clusters, and 25 individuals per cluster, the variance of a commonly used DID treatment effect estimator is 2.3 times higher in the RCS design for an intraclass correlation coefficient (ICC) of 0.05 and 3.8 times higher for an ICC of 0.1. We also present results of a simulation study comparing the RCS and DISC designs, using both a simple DID estimator and a more complex doubly-robust DID estimator that leverages covariate information, and show gains in precision for both estimators when using the DISC design. Additionally, we illustrate DISC sampling using a household survey dataset from South Africa.

conclusionsUse of the DISC design can result in estimators that have substantially lower variance than the analogous estimator resulting from an RCS design.

Indexed as

Research DesignCluster AnalysisCross-Sectional StudiesHumansLongitudinal Studiesbefore and after surveycohort samplingdifference-in-differencesrepeated cross-sectional sampling

Identifiers

PMID42424497
PMCPMC13348717

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