C B Pipper

Publications (2)3.82 Total impact

• Article: Prevalence of hypertrophic cardiomyopathy in a cohort of British Shorthair cats in Denmark.

  S Granström, M T Nyberg Godiksen, M Christiansen, C B Pipper, J L Willesen, J T Willesen, J Koch
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  ABSTRACT: Familial hypertrophic cardiomyopathy (HCM) has been described previously in British Shorthair cats (BSH), but until now, no reports have been published describing the prevalence of the disease within this breed. The aim of this study was to assess the prevalence of HCM in a large cohort of BSH and to evaluate the effect of sex, weight, and increasing age as potential risk factors for this disease. Three hundred and twenty-nine BSH presented for routine HCM screening during a 4-year period. Prospective cross-sectional study in which all cats were screened for HCM by conventional echocardiography. A total of 329 cats were examined, 214 females and 115 males, with a median age of 2.3 years (range, 0.8-14.1). Twenty-eight cats (8.5%) were classified as HCM-positive, 14 (4.3%) as equivocal, 282 (85.7%) as HCM-negative, and 5 (2.1%) were diagnosed with other cardiac diseases. The median age for diagnosis of HCM was 2.7 years (range, 0.9-14.1). Male cats had a significantly higher occurrence of HCM (20.4%) compared with the females (2.1%) corresponding to an odds ratio of 7.89 (95 % CI, 2.54-28.08) for males versus females adjusted for age and weight (P < .001). The BSH in our cohort had a high prevalence of HCM, often of early onset and with a significant male sex predisposition. We strongly recommend echocardiographic screening in this breed, especially cats used for breeding.
  Journal of Veterinary Internal Medicine 07/2011; 25(4):866-71. · 1.99 Impact Factor
• Article: Explained variation in a fully specified model for data-grouped survival data.

  C B Pipper, C Ritz, T H Scheike
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  ABSTRACT: An additive hazards model may be used to quantify the effect of genetic and environmental predictors on flowering of sugar beet plants recorded as data-grouped time-to-event data. Estimated predictor effects have an intuitive interpretation rooted in the underlying time dynamics of the flowering process. However, agricultural experiments are often designed using several plots containing a large number of plants that are subsequently being monitored. In this article, we consider an additive hazards model with an additional plot structure induced by latent shared frailty variables. This approach enables us to derive a method to assess the quality of predictors in terms of how much plot variation they explain. We apply the method to a large data set exploring flowering of sugar beet and conclude that the genetic predictor biotype, which has a strong effect, also explains a substantial amount of the plot variation. The method is also applied to a data set from medical research concerning days to virus positivity of serum samples in AIDS patients.
  Biometrics 04/2011; 67(4):1361-8. · 1.83 Impact Factor