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A Semiparametric Bayesian Approach for Analyzing Longitudinal Data from Multiple Related Groups

Author

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  • Das Kiranmoy

    (Interdisciplinary Statistical Research Unit, Indian Statistical Institute, Kolkata 700108, India)

  • Afriyie Prince

    (Department of Statistics, Temple University, Philadelphia PA, 19122)

  • Spirko Lauren

    (Department of Statistics, Temple University, Philadelphia PA, 19122)

Abstract

Often the biological and/or clinical experiments result in longitudinal data from multiple related groups. The analysis of such data is quite challenging due to the fact that groups might have shared information on the mean and/or covariance functions. In this article, we consider a Bayesian semiparametric approach of modeling the mean trajectories for longitudinal response coming from multiple related groups. We consider matrix stick-breaking process priors on the group mean parameters which allows information sharing on the mean trajectories across the groups. Simulation studies are performed to demonstrate the effectiveness of the proposed approach compared to the more traditional approaches. We analyze data from a one-year follow-up of nutrition education for hypercholesterolemic children with three different treatments where the children are from different age-groups. Our analysis provides more clinically useful information than the previous analysis of the same dataset. The proposed approach will be a very powerful tool for analyzing data from clinical trials and other medical experiments.

Suggested Citation

  • Das Kiranmoy & Afriyie Prince & Spirko Lauren, 2015. "A Semiparametric Bayesian Approach for Analyzing Longitudinal Data from Multiple Related Groups," The International Journal of Biostatistics, De Gruyter, vol. 11(2), pages 273-284, November.
    • Handle: RePEc:bpj:ijbist:v:11:y:2015:i:2:p:273-284:n:6
    DOI: 10.1515/ijb-2015-0002
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    References listed on IDEAS

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    1. Yisheng Li & Xihong Lin & Peter Müller, 2010. "Bayesian Inference in Semiparametric Mixed Models for Longitudinal Data," Biometrics, The International Biometric Society, vol. 66(1), pages 70-78, March.
    2. Jianxin Pan, 2003. "On modelling mean-covariance structures in longitudinal studies," Biometrika, Biometrika Trust, vol. 90(1), pages 239-244, March.
    3. Dunson, David B. & Xue, Ya & Carin, Lawrence, 2008. "The Matrix Stick-Breaking Process: Flexible Bayes Meta-Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 317-327, March.
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