Analysis of correlated continuous repeated observations: modelling the effect of ketosis on milk yield in dairy cows

This study used mixed models analysis to demonstrate the advantages of a re peated measures technique for a continuous variable over a single measure t echnique. As an illustration, the loss of milk yield due to ketosis was stu died in 2604 multiparous New York State Holstein cows belonging to eight he rds, calving between 1991 and 1993. Two methods of analysis were presented: The first treated milk yield as a continuous, summary measure (projected 3 05-day milk yield); the second treated milk yield as repeated measurements (test-day milk yields). In the first example, with 305-day milk yield as th e outcome, ketosis was treated as a binary covariate. Ketosis had no effect on the 305-day milk yield. In the second example, with monthly test-day mi lk yields as the outcome, four different covariance structures (simple, com pound symmetry, autoregressive, and unstructured) to model the association among the repeated measurements were compared. With this approach, ketotic cows yielded significantly less milk per day both before and immediately af ter diagnosis than did non-ketotic cows. Based on the goodness-of-fit stati stics, it was unclear whether an autoregressive or unstructured covariance structure was best. However, an autoregressive structure, in which the prev ious and current test-day milk yields are assumed to be correlated, was con sidered more suitable in this study; it is a simpler and more appropriate c ovariance structure for this particular problem than is an unstructured cov ariance structure. Nevertheless, with the test-day approach, any of these c orrelation structures could be used to estimate milk loss after disease. Ba sed on these findings, it is recommended that a repeated measures approach, rather than a single measure approach, he used to study the short-term eff ect of disease on milk yield. (C) 1999 Elsevier Science B.V. All rights res erved.