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Analysis of a longitudinal ordinal response clinical trial using dynamic models

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  • P. J. Lindsey
  • J. Kaufmann

Abstract

Summary. In many areas of pharmaceutical research, there has been increasing use of categorical data and more specifically ordinal responses. In many cases, complex models are required to account for different types of dependences among the responses. The clinical trial that is considered here involved patients who were required to remain in a particular state to enable the doctors to examine their heart. The aim of this trial was to study the relationship between the dose of the drug administered and the time that was spent by the patient in the state permitting examination. The patient's state was measured every second by a continuous Doppler signal which was categorized by the doctors into one of four ordered categories. Hence, the response consisted of repeated ordinal series. These series were of different lengths because the drug effect wore off faster (or slower) on certain patients depending on the drug dose administered and the infusion rate, and therefore the length of drug administration. A general method for generating new ordinal distributions is presented which is sufficiently flexible to handle unbalanced ordinal repeated measurements. It consists of obtaining a cumulative mixture distribution from a Laplace transform and introducing into it the integrated intensity of a binary logistic, continuation ratio or proportional odds model. Then, a multivariate distribution is constructed by a procedure that is similar to the updating process of the Kalman filter. Several types of history dependences are proposed.

Suggested Citation

  • P. J. Lindsey & J. Kaufmann, 2004. "Analysis of a longitudinal ordinal response clinical trial using dynamic models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 53(3), pages 523-537, August.
    • Handle: RePEc:bla:jorssc:v:53:y:2004:i:3:p:523-537
    DOI: 10.1111/j.1467-9876.2004.04882.x
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    1. Lindsey, J. K., 1999. "Models for Repeated Measurements," OUP Catalogue, Oxford University Press, edition 2, number 9780198505594.
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    Cited by:

    1. Parsons, Nick R. & Costa, Matthew L. & Achten, Juul & Stallard, Nigel, 2009. "Repeated measures proportional odds logistic regression analysis of ordinal score data in the statistical software package R," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 632-641, January.
    2. Varin, Cristiano & Vidoni, Paolo, 2006. "Pairwise likelihood inference for ordinal categorical time series," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2365-2373, December.

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