期刊
BIOMETRICS
卷 70, 期 2, 页码 449-456出版社
WILEY
DOI: 10.1111/biom.12151
关键词
Bridge distribution; Immortal cohort inference; Informative missingness; Missing not at random; Mortal cohort inference; Semi-continuous data
资金
- MRC [U1052 60558, MC_US_A030_0015, G0600657]
- Medical Research Council [MC_U105260558, MC_EX_G0800814, G0600657] Funding Source: researchfish
- MRC [G0600657, MC_U105260558, MC_EX_G0800814] Funding Source: UKRI
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.
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