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- Title
Comparison of paired ordinal data with mis-classification and covariates adjustment.
- Authors
Han, Yuanyuan; Lu, Zhao-Hua; Li, Yimei; Poon, Wai-Yin
- Abstract
In this paper, we develop an estimation and testing procedure for comparing matched-pair ordinal outcomes in studies with confounding factors. The classification method for the categories of ordinal outcomes that is accessible for all units may be prone to mis-classification, and thus another error-free classification method that can only be affordable for a fraction of the units are used, resulting in a dataset with partial validation. The distribution of categorical variables is modelled using correlated bivariate Gaussian latent variables, and the confounding factors are adjusted as covariates. The mis-classification of ordinal outcomes is addressed by estimating the mis-classification probabilities through the partial validation structure of the dataset. The mis-classification probabilities and the other parameters are estimated by a two-stage maximum likelihood estimator, and the difference between the matched-pair ordinal outcomes are assessed by a Wald test statistic. Simulation studies were conducted to investigate the accuracy of the estimates of the model parameters, and the type I error rates and power of the proposed testing procedure. The motivating dataset from the Garki Project was analysed to demonstrate the applicability of the proposed approach.
- Subjects
FALSE positive error; MAXIMUM likelihood statistics; PAIRED comparisons (Mathematics); LATENT variables; ERROR rates; CONFOUNDING variables; PROBABILITY theory
- Publication
Journal of the Royal Statistical Society: Series C (Applied Statistics), 2024, Vol 73, Issue 2, p478
- ISSN
0035-9254
- Publication type
Article
- DOI
10.1093/jrsssc/qlad105