Optimal Designs for Binary Logistic Regression with a Qualitative Classifier with Independent Levels |
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| Authors: | Karabi Nandy Sami Helle Antti Liski Erkki Liski |
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| Institution: | 1. School of Nursing and Department of Biostatistics , University of California Los Angeles , Los Angeles, California, USA karabi@ucla.edu;3. Department of Mathematics and Statistics and Finnish Defence Forces Technical Research Centre , University of Tampere , Kalevantie, Finland;4. Department of Signal Processing , Tampere University of Technology , Kalevantie, Finland;5. Department of Mathematics, Statistics and Philosophy , University of Tampere , Kalevantie, Finland |
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| Abstract: | Dose response studies arise in many medical applications. Often, such studies are considered within the framework of binary-response experiments such as success-failure. In such cases, popular choices for modeling the probability of response are logistic or probit models. Design optimality has been well studied for the logistic model with a continuous covariate. A natural extension of the logistic model is to consider the presence of a qualitative classifier. In this work, we explore D-, A-, and E-optimal designs in a two-parameter, binary logistic regression model after introducing a binary, qualitative classifier with independent levels. |
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| Keywords: | A-optimality D-optimality E-optimality Information matrix Qualitative classifier |
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