title: Survival analysis of clinical mastitis data using a nested frailty Cox model fit as a mixed-effects Poisson model.
abstract: Mastitis is a complex disease affecting dairy cows and is considered to be the most costly disease of dairy herds. The hazard of mastitis is a function of many factors, both managerial and environmental, making its control a difficult issue to milk producers. Observational studies of clinical mastitis (CM) often generate datasets with a number of characteristics which influence the analysis of those data: the outcome of interest may be the time to occurrence of a case of mastitis, predictors may change over time (time-dependent predictors), the effects of factors may change over time (time-dependent effects), there are usually multiple hierarchical levels, and datasets may be very large. Analysis of such data often requires expansion of the data into the counting-process format - leading to larger datasets - thus complicating the analysis and requiring excessive computing time. In this study, a nested frailty Cox model with time-dependent predictors and effects was applied to Canadian Bovine Mastitis Research Network data in which 10,831 lactations of 8035 cows from 69 herds were followed through lactation until the first occurrence of CM. The model was fit to the data as a Poisson model with nested normally distributed random effects at the cow and herd levels. Risk factors associated with the hazard of CM during the lactation were identified, such as parity, calving season, herd somatic cell score, pasture access, fore-stripping, and proportion of treated cases of CM in a herd. The analysis showed that most of the predictors had a strong effect early in lactation and also demonstrated substantial variation in the baseline hazard among cows and between herds. A small simulation study for a setting similar to the real data was conducted to evaluate the Poisson maximum likelihood estimation approach with both Gaussian quadrature method and Laplace approximation. Further, the performance of the two methods was compared with the performance of a widely used estimation approach for frailty Cox models based on the penalized partial likelihood. The simulation study showed good performance for the Poisson maximum likelihood approach with Gaussian quadrature and biased variance component estimates for both the Poisson maximum likelihood with Laplace approximation and penalized partial likelihood approaches.
authors.affiliation: Centre for Veterinary Epidemiological Research, University of Prince Edward Island, Charlottetown, PEI C1A 4P3, Canada; Department of Statistics, Faculty of Science, University of Misurata, P.O. Box 2478, Misurata, Libya. Electronic address: aelghafghuf@upei.ca.
authors.forename: Adel
authors.initials: A
authors.lastname: Elghafghuf
authors.affiliation: Department of Pathology and Microbiology, Faculty of Veterinary Medicine, University of Montreal, C.P. 5000, St-Hyacinthe, Quebec J2S 7C6, Canada.
authors.forename: Simon
authors.initials: S
authors.lastname: Dufour
authors.affiliation: Farm Animal Science, School of Veterinary Sciences, University of Bristol, Langford BS40 5DU, UK.
authors.forename: Kristen
authors.initials: K
authors.lastname: Reyher
authors.affiliation: Centre for Veterinary Epidemiological Research, University of Prince Edward Island, Charlottetown, PEI C1A 4P3, Canada.
authors.forename: Ian
authors.initials: I
authors.lastname: Dohoo
authors.affiliation: Centre for Veterinary Epidemiological Research, University of Prince Edward Island, Charlottetown, PEI C1A 4P3, Canada.
authors.forename: Henrik
authors.initials: H
authors.lastname: Stryhn
mesh_terms: D000818:Animals; D002170:Canada; D002417:Cattle; D005260:Female; D007774:Lactation; D016013:Likelihood Functions; D008414:Mastitis, Bovine / Q000453:epidemiology* / Q000382:microbiology; D016012:Poisson Distribution; D016016:Proportional Hazards Models; D012307:Risk Factors; D016019:Survival Analysis
keywords: Clinical mastitis; Mixed-effects Poisson model; Nested frailty Cox models; Poisson maximum likelihood; Simulation study