Expert opinion as priors for random effects in Bayesian prediction models: Subclinical ketosis in dairy cows as an example
| Autor: | Ni, Haifang, Klugkist, Irene, van der Drift, Saskia, Jorritsma, Ruurd, Hooijer, Gerrit, Nielen, Mirjam, Leerstoel Klugkist, Methodology and statistics for the behavioural and social sciences, Sub Junior Docenten, FAH GZ herkauwer, dFAH AVR, Sub GZ Herkauwer, FAH Evidence based Veterinary Medicine |
|---|---|
| Přispěvatelé: | Leerstoel Klugkist, Methodology and statistics for the behavioural and social sciences, Sub Junior Docenten, FAH GZ herkauwer, dFAH AVR, Sub GZ Herkauwer, FAH Evidence based Veterinary Medicine |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Epidemiology
Physiology Normal Distribution 01 natural sciences Biochemistry 010104 statistics & probability Bayes' theorem Mathematical and Statistical Techniques 0302 clinical medicine Statistics Prevalence Medicine and Health Sciences Cluster Analysis 030212 general & internal medicine Mathematics Multidisciplinary Agricultural and Biological Sciences(all) Organic Compounds Regression analysis Prognosis Random effects model Veterinary Diagnostics Body Fluids Dairying Chemistry Milk Veterinary Diseases Physical Sciences Medicine Female Anatomy Research Article Statistical Distributions Veterinary Medicine Science Bayesian probability Cattle Diseases Research and Analysis Methods Beverages Normal distribution Acetones 03 medical and health sciences Frequentist inference Prior probability Animals Statistical Methods 0101 mathematics General Nutrition Biochemistry Genetics and Molecular Biology(all) Organic Chemistry Chemical Compounds Biology and Life Sciences Bayes Theorem Ketosis Probability Theory Probability Distribution Diet Medical Risk Factors Cattle Veterinary Science Predictive modelling Forecasting Genetics and Molecular Biology(all) |
| Zdroj: | PLoS ONE PLoS One, 16(1). Public Library of Science PLoS ONE, Vol 16, Iss 1, p e0244752 (2021) |
| ISSN: | 1932-6203 |
| Popis: | Random effects regression models are routinely used for clustered data in etiological and intervention research. However, in prediction models, the random effects are either neglected or conventionally substituted with zero for new clusters after model development. In this study, we applied a Bayesian prediction modelling method to the subclinical ketosis data previously collected by Van der Drift et al. (2012). Using a dataset of 118 randomly selected Dutch dairy farms participating in a regular milk recording system, the authors proposed a prediction model with milk measures as well as available test-day information as predictors for the diagnosis of subclinical ketosis in dairy cows. While their original model included random effects to correct for the clustering, the random effect term was removed for their final prediction model. With the Bayesian prediction modelling approach, we first used non-informative priors for the random effects for model development as well as for prediction. This approach was evaluated by comparing it to the original frequentist model. In addition, herd level expert opinion was elicited from a bovine health specialist using three different scales of precision and incorporated in the prediction as informative priors for the random effects, resulting in three more Bayesian prediction models. Results showed that the Bayesian approach could naturally take the clustering structure of clusters into account by keeping the random effects in the prediction model. Expert opinion could be explicitly combined with individual level data for prediction. However in this dataset, when elicited expert opinion was incorporated, little improvement was seen at the individual level as well as at the herd level. When the prediction models were applied to the 118 herds, at the individual cow level, with the original frequentist approach we obtained a sensitivity of 82.4% and a specificity of 83.8% at the optimal cutoff, while with the three Bayesian models with elicited expert opinion, we obtained sensitivities ranged from 78.7% to 84.6% and specificities ranged from 75.0% to 83.6%. At the herd level, 30 out of 118 within herd prevalences were correctly predicted by the original frequentist approach, and 31 to 44 herds were correctly predicted by the three Bayesian models with elicited expert opinion. Further investigation in expert opinion and distributional assumption for the random effects was carried out and discussed. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
| Nepřihlášeným uživatelům se plný text nezobrazuje | K zobrazení výsledku je třeba se přihlásit. |