2017
DOI: 10.1111/jvim.14710
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Biological Variability in Serum Cortisol Concentration Post‐adrenocorticotropic Hormone Stimulation in Healthy Dogs

Abstract: BackgroundThe ACTH stimulation has low sensitivity for the diagnosis of hypercortisolism possibly as a result of biological and analytical variability.Hypothesis/ObjectivesTo report the components of biological and analytical variability in serum cortisol concentration post‐ACTH stimulation ([cortisol]) in healthy dogs.AnimalsFourteen healthy harrier hound dogs.MethodsThe data were extracted from a separate, prospective, randomized, double‐blinded, controlled discovery study in which dogs treated with vehicle … Show more

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Cited by 4 publications
(6 citation statements)
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“…where Z X is the percentile of the standard normal distribution and D is the desired percentage closeness to the HSP (Z X 5 1.645 for X 5 90% and D 5 10%; Z X 5 1.96 for X 5 95% and D 5 5%). 19 We used the outliers package 23 cochran.test() function to test if any of the dogs was significantly influencing the variance from the other dogs and the BlandAltmanLeh package 24 bland.altman.plot() function to generate the Bland-Altman plot. We used the lmerTest package 22,25 lmer() function to perform a regression analysis for repeated measures, to evaluate the relationship between the fixed effect factor "day of sampling" and the dependent variables "SDMA" and "creatinine"; a random intercept was fitted for each dog.…”
Section: Discussionmentioning
confidence: 99%
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“…where Z X is the percentile of the standard normal distribution and D is the desired percentage closeness to the HSP (Z X 5 1.645 for X 5 90% and D 5 10%; Z X 5 1.96 for X 5 95% and D 5 5%). 19 We used the outliers package 23 cochran.test() function to test if any of the dogs was significantly influencing the variance from the other dogs and the BlandAltmanLeh package 24 bland.altman.plot() function to generate the Bland-Altman plot. We used the lmerTest package 22,25 lmer() function to perform a regression analysis for repeated measures, to evaluate the relationship between the fixed effect factor "day of sampling" and the dependent variables "SDMA" and "creatinine"; a random intercept was fitted for each dog.…”
Section: Discussionmentioning
confidence: 99%
“…The IOI was calculated as ( CVnormalI2 + CVnormalA2) 1/2 /CV G . The number of specimens that should be assayed to be X% confident of achieving an estimate of the HSP within D% of an individual dog was calculated from the formula n = Z X 2 ( CVnormalI2 + CVnormalA2)/ D 2 , where Z X is the percentile of the standard normal distribution and D is the desired percentage closeness to the HSP ( Z X = 1.645 for X = 90% and D = 10%; Z X = 1.96 for X = 95% and D = 5%) . We used the outliers package cochran.test() function to test if any of the dogs was significantly influencing the variance from the other dogs and the BlandAltmanLeh package bland.altman.plot() function to generate the Bland–Altman plot.…”
Section: Methodsmentioning
confidence: 99%
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