Methods for Observed-Cluster Inference When Cluster Size Is Informative: A Review and Clarifications

Clustered data commonly arise in epidemiology. We assume each cluster member has an outcome Y and covariates . When there are missing data in Y, the distribution of Y given in all cluster members (“complete clusters”) may be different from the distribution just in members with observed Y (“observed...

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Main Authors:	Seaman, Shaun R, Pavlou, Menelaos, Copas, Andrew J
Format:	Online
Language:	English
Published:	Blackwell Publishing Ltd 2014
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4312901/

id	pubmed-4312901
recordtype	oai_dc
spelling	pubmed-43129012015-02-10 Methods for Observed-Cluster Inference When Cluster Size Is Informative: A Review and Clarifications Seaman, Shaun R Pavlou, Menelaos Copas, Andrew J Original Articles Clustered data commonly arise in epidemiology. We assume each cluster member has an outcome Y and covariates . When there are missing data in Y, the distribution of Y given 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 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. Blackwell Publishing Ltd 2014-06 2014-01-30 /pmc/articles/PMC4312901/ /pubmed/24479899 http://dx.doi.org/10.1111/biom.12151 Text en © 2014 The Authors. Biometrics published by Wiley Periodicals, Inc. on behalf of International Biometric Society http://creativecommons.org/licenses/by/3.0/ This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Seaman, Shaun R Pavlou, Menelaos Copas, Andrew J
spellingShingle	Seaman, Shaun R Pavlou, Menelaos Copas, Andrew J Methods for Observed-Cluster Inference When Cluster Size Is Informative: A Review and Clarifications
author_facet	Seaman, Shaun R Pavlou, Menelaos Copas, Andrew J
author_sort	Seaman, Shaun R
title	Methods for Observed-Cluster Inference When Cluster Size Is Informative: A Review and Clarifications
title_short	Methods for Observed-Cluster Inference When Cluster Size Is Informative: A Review and Clarifications
title_full	Methods for Observed-Cluster Inference When Cluster Size Is Informative: A Review and Clarifications
title_fullStr	Methods for Observed-Cluster Inference When Cluster Size Is Informative: A Review and Clarifications
title_full_unstemmed	Methods for Observed-Cluster Inference When Cluster Size Is Informative: A Review and Clarifications
title_sort	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 . When there are missing data in Y, the distribution of Y given 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 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.
publisher	Blackwell Publishing Ltd
publishDate	2014
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4312901/
_version_	1613182859150884864

Methods for Observed-Cluster Inference When Cluster Size Is Informative: A Review and Clarifications

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