Downer cows: a reanalysis of an old data set

Aims: To compare the performance of two predictive models for the survival of downer cows. Methods: The first model had been developed in 1987 using a dataset containing missing values, while the second, new model was developed on the same dataset but using modern data imputation and analytical methods. Missing data were imputed using multiple imputation by chained equations and a logistic regression model fitted to the imputed data, with survival or not as the outcome variable. The predictive ability of the model built on the imputed data was contrasted with the original prognostic model by testing them both on a second smaller but complete data set, collected contemporaneously with the development of the original model but from a different region of New Zealand. Sensitivity, specificity, accuracy, and cut point for the two models were calculated. Results: The original 1987 model had a slightly higher accuracy than that of the new one with a sensitivity of 0.85 (95% CI = 0.72-0.94) and a specificity of 0.82 (95% CI = 0.7-0.91), using a cut point for the probability of survival = 0.313. Conclusions: The original prognostic formula published by Clark et al. in 1987 performed as well as a modern model built on an imputed data set.

Automated summary

This study aimed to compare the performance of two predictive models for the survival of downer cows. The original 1987 model, developed using a dataset with missing values, performed as well as the new model that utilized modern data imputation and analytical methods. The results showed that the original model had a slightly higher accuracy with specificities of 0.82 and sensitivities of 0.85, using a cut point for the probability of survival at 0.313.