Optimal designs for the emax, log-linear and exponential models

• 成果类型: Article
• 署名作者: Dette, H.; Kiss, C.; Bevanda, M.; Bretz, Frank
• 署名单位: Ruhr University Bochum; Novartis
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asq020
• 发表日期: 2010
• 页码: 513518
• 关键词: michaelis-menten model; regression-models; points

摘要

We derive locally D- and EDp-optimal designs for the exponential, log-linear and three-parameter emax models. For each model the locally D- and EDp-optimal designs are supported at the same set of points, while the corresponding weights are different. This indicates that for a given model, D-optimal designs are efficient for estimating the smallest dose that achieves 100p% of the maximum effect in the observed dose range. Conversely, EDp-optimal designs also yield good D-efficiencies. We illustrate the results using several examples and demonstrate that locally D- and EDp-optimal designs for the emax, log-linear and exponential models are relatively robust with respect to misspecification of the model parameters.