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Full-Text Articles in Life Sciences
Dose-Response Modeling With Marginal Information On A Missing Categorical Covariate, John R. Stevens, David I. Schlipalius
Dose-Response Modeling With Marginal Information On A Missing Categorical Covariate, John R. Stevens, David I. Schlipalius
Conference on Applied Statistics in Agriculture
When the relationship between a dosage-type variable and a binary outcome depends on a categorical variable, a common analysis would employ a dose-response model with the categorical variable as a covariate. When the level of the categorical variable is not known for all subjects, however, the standard dose-response model alone cannot provide useful inference. We present an EM-based approach to account for the missing covariate in a dose-response model setting when additional knowledge about the marginal distribution of the covariate is available. This approach is motivated by a study of the beetle Rhyzopertha dominica, a pest of stored grain in …
The Effect Of Design And Dose Level Choice On Estimatlng The Optimal Dose In A Quantitative Dose-Response Experiment, Henry R. Rolka, George A. Milliken, James R. Schwenke, Marta Remmenga
The Effect Of Design And Dose Level Choice On Estimatlng The Optimal Dose In A Quantitative Dose-Response Experiment, Henry R. Rolka, George A. Milliken, James R. Schwenke, Marta Remmenga
Conference on Applied Statistics in Agriculture
D-optimality is a commonly used criterion to evaluate a design with respect to parameter estimation. The variance of the optimal dose estimate is another criterion for evaluating a design. The quantitative dose-response experiment involves fitting a model to data and estimating an optimal dose. Two techniques for estimating an optimal dose and three models are used to compare the variances of optimal dose estimates over nine equally spaced balanced designs and five fixed unequally spaced six-point designs. The results show that a design which is more D-optimal than another design does not necessarily produce optimal dose estimates with less variance.