Variance estimation in tests of clustered categorical data with informative cluster size.

ABSTRACT: In the analysis of clustered data, inverse cluster size weighting has been shown to be resistant to the potentially biasing effects of informative cluster size, where the number of observations within a cluster is associated with the outcome variable of interest. The method of inverse cluster size reweighting has been implemented to establish clustered data analogues of common tests for independent data, but the method has yet to be extended to tests of categorical data. Many variance estimators have been implemented across established cluster-weighted tests, but potential effects of differing methods on test performance has not previously been explored. Here, we develop cluster-weighted estimators of marginal proportions that remain unbiased under informativeness, and derive analogues of

SUBMITTER: Gregg M 

PROVIDER: S-EPMC11220780 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature