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Träfflista för sökning "WFRF:(Dodds G) srt2:(2005-2009)"

Sökning: WFRF:(Dodds G) > (2005-2009)

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1.
  • Kennedy, L J, et al. (författare)
  • Association of canine hypothyroidism with a common major histocompatibility complex DLA class II allele.
  • 2006
  • Ingår i: Tissue Antigens. - : Wiley. - 0001-2815 .- 1399-0039. ; 68:1, s. 82-6
  • Tidskriftsartikel (refereegranskat)abstract
    • Dogs exhibit a range of immune-mediated conditions including a lymphocytic thyroiditis which has many similarities to Hashimoto's thyroiditis in man. We have recently reported an association in Doberman Pinschers between canine hypothyroidism and a rare DLA class II haplotype that contains the DLA-DQA1*00101 allele. We now report a further series of 173 hypothyroid dogs in a range of breeds where a significant association with DLA-DQA1*00101 is shown.
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2.
  • Dodds, Michael G, et al. (författare)
  • Robust population pharmacokinetic experiment design.
  • 2005
  • Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 32:1, s. 33-64
  • Tidskriftsartikel (refereegranskat)abstract
    • The population approach to estimating mixed effects model parameters of interest in pharmacokinetic (PK) studies has been demonstrated to be an effective method in quantifying relevant population drug properties. The information available for each individual is usually sparse. As such, care should be taken to ensure that the information gained from each population experiment is as efficient as possible by designing the experiment optimally, according to some criterion. The classic approach to this problem is to design "good" sampling schedules, usually addressed by the D-optimality criterion. This method has the drawback of requiring exact advanced knowledge (expected values) of the parameters of interest. Often, this information is not available. Additionally, if such prior knowledge about the parameters is misspecified, this approach yields designs that may not be robust for parameter estimation. In order to incorporate uncertainty in the prior parameter specification, a number of criteria have been suggested. We focus on ED-optimality. This criterion leads to a difficult numerical problem, which is made tractable here by a novel approximation of the expectation integral usually solved by stochastic integration techniques. We present two case studies as evidence of the robustness of ED-optimal designs in the face of misspecified prior information. Estimates from replicate simulated population data show that such misspecified ED-optimal designs recover parameter estimates that are better than similarly misspecified D-optimal designs, and approach estimates gained from D-optimal designs where the parameters are correctly specified.
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