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Optimal designs for multivariate logistic mixed models with longitudinal data

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  • Hong-Yan Jiang
  • Rong-Xian Yue
  • Xiao-Dong Zhou

Abstract

This paper considers the optimal design problem for multivariate mixed-effects logistic models with longitudinal data. A decomposition method of the binary outcome and the penalized quasi-likelihood are used to obtain the information matrix. The D-optimality criterion based on the approximate information matrix is minimized under different cost constraints. The results show that the autocorrelation coefficient plays a significant role in the design. To overcome the dependence of the D-optimal designs on the unknown fixed-effects parameters, the Bayesian D-optimality criterion is proposed. The relative efficiencies of designs reveal that both the cost ratio and autocorrelation coefficient play an important role in the optimal designs.

Suggested Citation

  • Hong-Yan Jiang & Rong-Xian Yue & Xiao-Dong Zhou, 2019. "Optimal designs for multivariate logistic mixed models with longitudinal data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(4), pages 850-864, February.
    • Handle: RePEc:taf:lstaxx:v:48:y:2019:i:4:p:850-864
    DOI: 10.1080/03610926.2017.1419263
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