期刊
ANNALS OF APPLIED STATISTICS
卷 16, 期 1, 页码 596-611出版社
INST MATHEMATICAL STATISTICS-IMS
DOI: 10.1214/21-AOAS1518
关键词
Clustered data; longitudinal data; generalized estimating equations; periodontal disease; tooth loss
资金
- NIH [F31DE027589, R03DE021730, R01CA226805]
This study proposes a novel modeling approach that incorporates the technique of inverse probability censoring weights into CWGEE with binary outcomes to address issues related to informative cluster size and informative drop-out over time. In an extensive simulation study, it is demonstrated that this method yields lower bias and excellent coverage probability compared to traditional methods.
Periodontal disease is a serious gum infection impacting half of the U.S. adult population that may lead to loss of teeth. Using standard marginal models to study the association between patient-level predictors and tooth-level outcomes can lead to biased estimates because the independence assumption between the outcome (periodontal disease) and cluster size (number of teeth per patient) is violated. Specifically, the baseline number of teeth of a patient is informative. In this setting a cluster-weighted generalized estimating equations (CWGEE) approach can be used to obtain unbiased marginal inference from data with informative cluster size (ICS). However, in many longitudinal studies of dental health, including the Veterans Affairs Dental Longitudinal Study, the rate of tooth-loss or tooth drop-out over time is also informative, creating a missing at random data mechanism. Here, we propose a novel modeling approach that incorporates the technique of inverse probability censoring weights into CWGEE with binary outcomes to account for ICS and informative drop-out over time. In an extensive simulation study we demonstrate that results obtained from our proposed method yield lower bias and excellent coverage probability, compared to those obtained from traditional methods which do not account for ICS or drop-out.
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