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Bivariate Longitudinal Model for Detecting Prescribing Change in Two Drugs Simultaneously with Correlated Errors

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  • Jabu S. Sithole
  • Peter W. Jones

Abstract

Bivariate responses of repeated measures data are usually analysed as two separate responses in the literature by several authors. The two responses usually tend to be related in some way and analysing this data jointly presents an opportunity to account for the joint movement, which may impact on the conclusions reached compared to analysing the responses separately. In this paper, a bivariate regression model with random effects (linear mixed model) is used to detect a change if any in the prescribing habits in the UK at the general practice (family medicine) level due to an educational intervention given repeated measures data before and after the intervention and a control group. The message was to increase the prescribing of one drug while simultaneously decreasing the prescribing of another. The effects of modelling a bivariate auto-regressive process are evaluated.

Suggested Citation

  • Jabu S. Sithole & Peter W. Jones, 2007. "Bivariate Longitudinal Model for Detecting Prescribing Change in Two Drugs Simultaneously with Correlated Errors," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(3), pages 339-352.
    • Handle: RePEc:taf:japsta:v:34:y:2007:i:3:p:339-352
    DOI: 10.1080/02664760601005020
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    Cited by:

    1. Jiguo Cao & Liangliang Wang & Zhongwen Huang & Junyi Gai & Rongling Wu, 2017. "Functional Mapping of Multiple Dynamic Traits," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(1), pages 60-75, March.
    2. Hemant Kulkarni & Jayabrata Biswas & Kiranmoy Das, 2019. "A joint quantile regression model for multiple longitudinal outcomes," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(4), pages 453-473, December.
    3. Kiranmoy Das & Michael J. Daniels, 2014. "A semiparametric approach to simultaneous covariance estimation for bivariate sparse longitudinal data," Biometrics, The International Biometric Society, vol. 70(1), pages 33-43, March.

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