Chapter 5. Designs for Logistic Regression Models

  1. Martijn P. F. Berger1 and
  2. Weng Kee Wong2

Published Online: 27 MAY 2009

DOI: 10.1002/9780470746912.ch5

An Introduction to Optimal Designs for Social and Biomedical Research

An Introduction to Optimal Designs for Social and Biomedical Research

How to Cite

Berger, M. P. F. and Wong, W. K. (2009) Designs for Logistic Regression Models, in An Introduction to Optimal Designs for Social and Biomedical Research, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470746912.ch5

Author Information

  1. 1

    Department of Methodology and Statistics, Maastricht University, The Netherlands

  2. 2

    Department of Biostatistics, School of Public Health, University of California, Los Angeles, USA

Publication History

  1. Published Online: 27 MAY 2009
  2. Published Print: 29 MAY 2009

ISBN Information

Print ISBN: 9780470694503

Online ISBN: 9780470746912

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Keywords:

  • designs for logistic regression models;
  • dichotomous response variables, occurring in social and biomedical research;
  • logistic regression model, describing relation between a dichotomous dependent variable Y and one or more independent variables;
  • logit transformation;
  • local optimality;
  • RE of the maximin design - maximin value (MMV);
  • performance of a maximin design, depending on range of probability values for p1 and p2;
  • item response theory (IRT) models

Summary

This chapter contains sections titled:

  • Design problem for logistic regression

  • The design

  • The logistic regression model

  • Approaches to deal with local optimality

  • Designs for calibration of item parameters in item response theory models

  • Matrix formulation of designs for logistic regression

  • Summary