 Use of multiple imputation in supersampled nested case‐control and case‐cohort studiesØrnulf Borgan, Ruth H. Keogh and Aleksander Njøs Scandinavian Journal of Statistics, 2023, vol. 50, issue 1, 13-37 Abstract: Nested case‐control and case‐cohort studies are useful for studying associations between covariates and time‐to‐event when some covariates are expensive to measure. Full covariate information is collected in the nested case‐control or case‐cohort sample only, while cheaply measured covariates are often observed for the full cohort. Standard analysis of such case‐control samples ignores any full cohort data. Previous work has shown how data for the full cohort can be used efficiently by multiple imputation of the expensive covariate(s), followed by a full‐cohort analysis. For large cohorts this is computationally expensive or even infeasible. An alternative is to supplement the case‐control samples with additional controls on which cheaply measured covariates are observed. We show how multiple imputation can be used for analysis of such supersampled data. Simulations show that this brings efficiency gains relative to a traditional analysis and that the efficiency loss relative to using the full cohort data is not substantial. Date: 2023 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:scjsta:v:50:y:2023:i:1:p:13-37 Ordering information: This journal article can be ordered from Access Statistics for this article Scandinavian Journal of Statistics is currently edited by ÿrnulf Borgan and Bo Lindqvist More articles in Scandinavian Journal of Statistics from Danish Society for Theoretical Statistics, Finnish Statistical Society, Norwegian Statistical Association, Swedish Statistical Association |
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