scholarly journals Locally optimal designs for binary dose-response models

2018 ◽  
Vol 46 (2) ◽  
pp. 336-354 ◽  
Author(s):  
Yi Zhai ◽  
Zhide Fang
Keyword(s):  
Dose Response ◽  
Optimal Designs ◽  
Response Models ◽  
Locally Optimal Designs

Optimal designs for dose–response models with linear effects of covariates

2018 ◽  
Vol 127 ◽  
pp. 217-228 ◽  
Author(s):  
Jun Yu ◽  
Xiangshun Kong ◽  
Mingyao Ai ◽  
Kwok Leung Tsui
Keyword(s):  
Dose Response ◽  
Optimal Designs ◽  
Response Models ◽  
Linear Effects

scholarly journals Optimal designs for discriminating between dose-response models in toxicology studies

Bernoulli ◽  
2010 ◽  
Vol 16 (4) ◽  
pp. 1164-1176 ◽  
Author(s):  
Holger Dette ◽  
Andrey Pepelyshev ◽  
Piter Shpilev ◽  
Weng Kee Wong
Keyword(s):  
Dose Response ◽  
Optimal Designs ◽  
Response Models

D optimal designs for three Poisson dose–response models

2013 ◽  
Vol 40 (2) ◽  
pp. 201-211 ◽  
Author(s):  
Alan Maloney ◽  
Ulrika S. H. Simonsson ◽  
Marloes Schaddelee
Keyword(s):  
Dose Response ◽  
Optimal Designs ◽  
Response Models

scholarly journals Optimal Designs for Dose–Response Models With Restricted Design Spaces

2006 ◽  
Vol 101 (474) ◽  
pp. 747-759 ◽  
Author(s):  
Stefanie Biedermann ◽  
Holger Dette ◽  
Wei Zhu
Keyword(s):  
Dose Response ◽  
Optimal Designs ◽  
Response Models

scholarly journals Weighted mixed-effects dose–response models for tables of correlated contrasts

2021 ◽  
Vol 21 (2) ◽  
pp. 320-347
Author(s):  
Nicola Orsini
Keyword(s):  
Dose Response ◽  
Response Pattern ◽  
Mixed Effects ◽  
Data Generation ◽  
Measures Of Association ◽  
Response Models ◽  
Response Data ◽  
Hazard Ratios ◽  
Health Related ◽  
Generation Mechanisms

Recognizing a dose–response pattern based on heterogeneous tables of contrasts is hard. Specification of a statistical model that can consider the possible dose–response data-generating mechanism, including its variation across studies, is crucial for statistical inference. The aim of this article is to increase the understanding of mixed-effects dose–response models suitable for tables of correlated estimates. One can use the command drmeta with additive (mean difference) and multiplicative (odds ratios, hazard ratios) measures of association. The postestimation command drmeta_graph greatly facilitates the visualization of predicted average and study-specific dose–response relationships. I illustrate applications of the drmeta command with regression splines in experimental and observational data based on nonlinear and random-effects data-generation mechanisms that can be encountered in health-related sciences.

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