Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

Multivariate Analysis of Longitudinal Ordinal Data with Mixed E ects Models, with Application to Clinical Outcomes in Osteoarthritis

Abstract : Our objective was to evaluate the efficacy of robenacoxib in osteoarthritic dogs using four ordinal responses measured repeatedly over time. We propose a multivariate probit mixed effects model to describe the joint evolution of endpoints and to evidence the intrinsic correlations between responses that are not due to treatment effect. Maximum likelihood computation is intractable within reasonable time frames. We therefore use a pairwise modeling approach in combination with a stochastic EM algorithm. Multidimensional ordinal responses with longitudinal measurements are a common feature in clinical trials. However, the standard methods for data analysis use unidimensional models, resulting in a loss of information. Our methodology provides substantially greater insight than these methods for the evaluation of treatment effects and shows a good performance at low computational cost. We thus believe that it could be used in routine practice to optimize the evaluation of treatment efficacy.
Document type :
Preprints, Working Papers, ...
Complete list of metadatas

Cited literature [2 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01003741
Contributor : Didier Concordet <>
Submitted on : Tuesday, June 10, 2014 - 3:39:56 PM
Last modification on : Tuesday, March 17, 2020 - 1:37:14 AM
Long-term archiving on: : Wednesday, September 10, 2014 - 12:00:47 PM

File

JASA_2014.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01003741, version 1

Collections

Citation

Celine Marielle Laffont, Marc Vandemeulebroecke, Didier Concordet. Multivariate Analysis of Longitudinal Ordinal Data with Mixed E ects Models, with Application to Clinical Outcomes in Osteoarthritis. 2013. ⟨hal-01003741⟩

Share

Metrics

Record views

406

Files downloads

387