Back to Search
Start Over
Comparison of paired ordinal data with mis-classification and covariates adjustment.
- Source :
- Journal of the Royal Statistical Society: Series C (Applied Statistics); Mar2024, Vol. 73 Issue 2, p478-496, 19p
- Publication Year :
- 2024
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00359254
- Volume :
- 73
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- Journal of the Royal Statistical Society: Series C (Applied Statistics)
- Publication Type :
- Academic Journal
- Accession number :
- 176131534
- Full Text :
- https://doi.org/10.1093/jrsssc/qlad105