This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 638
Type: Contributed
Date/Time: Thursday, August 5, 2010 : 8:30 AM to 10:20 AM
Sponsor: WNAR
Abstract - #308596
Title: Optimal Designs for Binary Logistic Regression with a Qualitative Classifier
Author(s): Karabi Sinha*+
Companies: University of California, Los Angeles
Address: 700 Tiverton Rd, Los Angeles, CA, 90095, U.S.A.
Keywords: A-optimality ; D-optimality ; Information Matrix ; Qualitative Classifier

Dose response studies are an important counterpart to efficient development of clinical intervention. Such studies can often be considered within the framework of binary responses such as success-failure, dead-alive etc. In such cases, popular choices for modeling the probability of response are logistic or probit models. Thus, if a practitioner wishes to select various doses, within an admissible range, and the number of patients on each dose, then given a sample size n, the question is how to allocate those doses and patients to obtain "best" estimates of the unknown parameters. In particular, this setup may further be considered in the presence of a qualitative classifier, i.e., where patients are classified by some qualitative factor. Here we explore D- and A-optimal designs in a 2-parameter, binary logistic regression model after introducing a binary, qualitative classifier.

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