A Bayesian approach for analysis of ordered categorical responses subject to misclassification

• Published in: PloS one Vol. 13; no. 12; p. e0208433
• Main Authors: Ling, Ashley, Hay, El Hamidi, Aggrey, Samuel E., Rekaya, Romdhane
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 13.12.2018; Public Library of Science (PLoS)
• Subjects: Accuracy; Analysis; Animals; asymmetry; Bayes Theorem; Bayesian analysis; Bayesian theory; Beef; Beef cattle; Bias; Binary data; Bioinformatics; Biology and Life Sciences; Body Weight - physiology; Breeding; Breeding - methods; Breeding - statistics & numerical data; Categories; Cattle; Cattle - classification; Cattle - genetics; Computer simulation; Data collection; Data processing; Datasets; Datasets as Topic - classification; Datasets as Topic - statistics & numerical data; Decision making; Errors (Mistakes); Female; Genetic Association Studies - statistics & numerical data; Genetic Association Studies - veterinary; Heritability; Markov Chains; Meat - statistics & numerical data; Medical diagnosis; Medicinal plants; medicine; Medicine and Health Sciences; Methods; Models, Statistical; Parameter estimation; Parturition - physiology; Phenotype; Physical Fitness; Physical Sciences; plant improvement; Power efficiency; Pregnancy; probability; Quantitative Trait, Heritable; Reduction; Research and Analysis Methods; Statistical analysis; Statistical inference; surveys; Variables; United States > US; Georgia
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0208433

Ordinal categorical responses are frequently collected in survey studies, human medicine, and animal and plant improvement programs, just to mention a few. Errors in this type of data are neither rare nor easy to detect. These errors tend to bias the inference, reduce the statistical power and ultimately the efficiency of the decision-making process. Contrarily to the binary situation where misclassification occurs between two response classes, noise in ordinal categorical data is more complex due to the increased number of categories, diversity and asymmetry of errors. Although several approaches have been presented for dealing with misclassification in binary data, only limited practical methods have been proposed to analyze noisy categorical responses. A latent variable model implemented within a Bayesian framework was proposed to analyze ordinal categorical data subject to misclassification using simulated and real datasets. The simulated scenario consisted of a discrete response with three categories and a symmetric error rate of 5% between any two classes. The real data consisted of calving ease records of beef cows. Using real and simulated data, ignoring misclassification resulted in substantial bias in the estimation of genetic parameters and reduction of the accuracy of predicted breeding values. Using our proposed approach, a significant reduction in bias and increase in accuracy ranging from 11% to 17% was observed. Furthermore, most of the misclassified observations (in the simulated data) were identified with a substantially higher probability. Similar results were observed for a scenario with asymmetric misclassification. While the extension to traits with more categories between adjacent classes is straightforward, it could be computationally costly. For traits with high heritability, the performance of the methodology would be expected to improve.