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. 2024 Aug 15;43(18):3539-3561.
doi: 10.1002/sim.10133. Epub 2024 Jun 9.

Bayesian transition models for ordinal longitudinal outcomes

Maximilian D Rohde  1 , Benjamin French  1 , Thomas G Stewart  2 , Frank E Harrell Jr  1
Affiliations

Bayesian transition models for ordinal longitudinal outcomes

Maximilian D Rohde et al. Stat Med. .

Abstract

Ordinal longitudinal outcomes are becoming common in clinical research, particularly in the context of COVID-19 clinical trials. These outcomes are information-rich and can increase the statistical efficiency of a study when analyzed in a principled manner. We present Bayesian ordinal transition models as a flexible modeling framework to analyze ordinal longitudinal outcomes. We develop the theory from first principles and provide an application using data from the Adaptive COVID-19 Treatment Trial (ACTT-1) with code examples in R. We advocate that researchers use ordinal transition models to analyze ordinal longitudinal outcomes when appropriate alongside standard methods such as time-to-event modeling.

Keywords: Bayesian modeling; clinical trials; ordinal longitudinal outcomes; transition models.

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References

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