Algorithms for finding locally and Bayesian optimal designs for binary dose-response models with control mortality

被引:1
|
作者
Smith, DM
Ridout, MS
机构
[1] Med Coll Georgia, Dept Biostat, Augusta, GA 30912 USA
[2] Univ Kent, Inst Math & Stat, Canterbury CT2 7NF, Kent, England
关键词
generalized linear models; general equivalence theorem; Abbott's formula;
D O I
10.1016/j.jspi.2004.01.017
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Algorithms for finding optimal designs for three-parameter binary dose-response models that incorporate control mortality are described. Locally and Bayesian optimal designs for models with a range of link functions are considered. Design criteria looked at include D-optimal, D-A-optimal and V-optimal designs, together with D-s-optimal designs where the control mortality parameter is regarded as a nuisance parameter. The range of prior distributions for the Bayesian optimal designs includes uniform, trivariate normal and a combination of a bivariate normal prior for the parameters of the underlying dose-response with an independent uniform prior for the control mortality parameter. (c) 2004 Elsevier B.V. All rights reserved.
引用
收藏
页码:463 / 478
页数:16
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