{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Seaman SR"],"funding":["Medical Research Council"],"pagination":["449-56"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC4312901"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["70(2)"],"pubmed_abstract":["Clustered data commonly arise in epidemiology. We assume each cluster member has an outcome Y and covariates X. When there are missing data in Y, the distribution of Y given X in all cluster members (\"complete clusters\") may be different from the distribution just in members with observed Y (\"observed clusters\"). Often the former is of interest, but when data are missing because in a fundamental sense Y does not exist (e.g., quality of life for a person who has died), the latter may be more meaningful (quality of life conditional on being alive). Weighted and doubly weighted generalized estimating equations and shared random-effects models have been proposed for observed-cluster inference when cluster size is informative, that is, the distribution of Y given X in observed clusters depends on observed cluster size. We show these methods can be seen as actually giving inference for complete clusters and may not also give observed-cluster inference. This is true even if observed clusters are complete in themselves rather than being the observed part of larger complete clusters: here methods may describe imaginary complete clusters rather than the observed clusters. We show under which conditions shared random-effects models proposed for observed-cluster inference do actually describe members with observed Y. A psoriatic arthritis dataset is used to illustrate the danger of misinterpreting estimates from shared random-effects models."],"journal":["Biometrics"],"pubmed_title":["Methods for observed-cluster inference when cluster size is informative: a review and clarifications."],"pmcid":["PMC4312901"],"funding_grant_id":["60558","MC_EX_G0800814","MC_U105260558","MC US A030 0015","G0600657","U1052","U1052 60558"],"pubmed_authors":["Pavlou M","Seaman SR","Copas AJ"],"additional_accession":[]},"is_claimable":false,"name":"Methods for observed-cluster inference when cluster size is informative: a review and clarifications.","description":"Clustered data commonly arise in epidemiology. We assume each cluster member has an outcome Y and covariates X. When there are missing data in Y, the distribution of Y given X in all cluster members (\"complete clusters\") may be different from the distribution just in members with observed Y (\"observed clusters\"). Often the former is of interest, but when data are missing because in a fundamental sense Y does not exist (e.g., quality of life for a person who has died), the latter may be more meaningful (quality of life conditional on being alive). Weighted and doubly weighted generalized estimating equations and shared random-effects models have been proposed for observed-cluster inference when cluster size is informative, that is, the distribution of Y given X in observed clusters depends on observed cluster size. We show these methods can be seen as actually giving inference for complete clusters and may not also give observed-cluster inference. This is true even if observed clusters are complete in themselves rather than being the observed part of larger complete clusters: here methods may describe imaginary complete clusters rather than the observed clusters. We show under which conditions shared random-effects models proposed for observed-cluster inference do actually describe members with observed Y. A psoriatic arthritis dataset is used to illustrate the danger of misinterpreting estimates from shared random-effects models.","dates":{"release":"2014-01-01T00:00:00Z","publication":"2014 Jun","modification":"2025-04-19T14:40:06.752Z","creation":"2019-03-27T01:44:22Z"},"accession":"S-EPMC4312901","cross_references":{"pubmed":["24479899"],"doi":["10.1111/biom.12151"]}}