Estimating time-to-event from longitudinal ordinal data using random-effects Markov models: Application to multiple sclerosis progression

Micha Mandel, Rebecca Betensky

Research output: Contribution to journalArticle

Abstract

Longitudinal ordinal data are common in many scientific studies, including those of multiple sclerosis (MS), and are frequently modeled using Markov dependency. Several authors have proposed random-effects Markov models to account for heterogeneity in the population. In this paper, we go one step further and study prediction based on random-effects Markov models. In particular, we show how to calculate the probabilities of future events and confidence intervals for those probabilities, given observed data on the ordinal outcome and a set of covariates, and how to update them over time. We discuss the usefulness of depicting these probabilities for visualization and interpretation of model results and illustrate our method using data from a phase III clinical trial that evaluated the utility of interferon beta-1a (trademark Avonex) to MS patients of type relapsing-remitting.

Original languageEnglish (US)
Pages (from-to)750-764
Number of pages15
JournalBiostatistics
Volume9
Issue number4
DOIs
StatePublished - Oct 1 2008

Fingerprint

Multiple Sclerosis
Ordinal Data
Random Effects Model
Longitudinal Data
Progression
Markov Model
Clinical Trials
Confidence interval
Covariates
Visualization
Update
Calculate
Prediction
Ordinal data
Multiple sclerosis
Markov model
Random effects
Model

Keywords

  • Markov model
  • Ordinal response
  • Prediction
  • Transition model

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

Estimating time-to-event from longitudinal ordinal data using random-effects Markov models : Application to multiple sclerosis progression. / Mandel, Micha; Betensky, Rebecca.

In: Biostatistics, Vol. 9, No. 4, 01.10.2008, p. 750-764.

Research output: Contribution to journalArticle

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