The non-linear effect (determined by the penalised partial-likelihood approach) of milk-protein concentration on time to first insemination in Belgian dairy cows

Nonlinear and time-dependent effects of sparsely measured continuous time-varying covariates in time-to-event analysis

Many flexible extensions of the Cox proportional hazards model incorporate time-dependent (TD) and/or nonlinear (NL) effects of time-invariant covariates. In contrast, little attention has been given to the assessment of such effects for continuous time-varying covariates (TVCs). We propose a flexible regression B-spline-based model for TD and NL effects of a TVC. To account for sparse TVC measurements, we added to this model the effect of time elapsed since last observation (TEL), which acts as an effect modifier. TD, NL, and TEL effects are estimated with the iterative alternative conditional estimation algorithm. Furthermore, a simulation extrapolation (SIMEX)-like procedure was adapted to correct the estimated effects for random measurement errors in the observed TVC values. In simulations, TD and NL estimates were unbiased if the TVC was measured with a high frequency. With sparse measurements, the strength of the effects was underestimated but the TEL estimate helped reduce the bias, whereas SIMEX helped further to correct for bias toward the null due to "white noise" measurement errors. We reassessed the effects of systolic blood pressure (SBP) and total cholesterol, measured at two-year intervals, on cardiovascular risks in women participating in the Framingham Heart Study. Accounting for TD effects of SBP, cholesterol and age, the NL effect of cholesterol, and the TEL effect of SBP improved substantially the model's fit to data. Flexible estimates yielded clinically important insights regarding the role of these risk factors. These results illustrate the advantages of flexible modeling of TVC effects.

A herd health approach to dairy cow nutrition and production diseases of the transition cow

This paper presents a practical, on-farm approach for the monitoring and prevention of production disease in dairy cattle. This integrated approach, should be used in an interdisciplinary way by farmers, veterinarians, nutrition advisors and other relevant professionals for the improvement of animal health and welfare and producer profitability. The key areas that form the basis for this approach are body condition score management, negative energy balance, hypocalcaemia, rumen health and trace element status. Monitoring criteria are described for each of these key areas, which when considered collectively, will facilitate the assessment of dairy cow health with regard to clinical and subclinical disease. The criteria, which are informed by published scientific literature, are based on farm management and environmental factors, clinical data, milk production records, dietary analysis, and assessment of blood and liver concentrations of various metabolites or trace elements. The aim is to review the efficacy of production disease control measures currently in place, and if necessary to modify them or formulate new ones.