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Increasing Statistical Efficiency using Ordinal Transition Models: A Simulation Study

Abstract Number:

3012 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Maximilian Rohde (1), Benjamin French (1), Frank Harrell (1)

Institutions:

(1) Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN

Co-Author(s):

Benjamin French  
Department of Biostatistics, Vanderbilt University School of Medicine
Frank Harrell  
Department of Biostatistics, Vanderbilt University School of Medicine

First Author:

Maximilian Rohde  
Department of Biostatistics, Vanderbilt University School of Medicine

Presenting Author:

Maximilian Rohde  
N/A

Abstract Text:

Ordinal longitudinal data on patient health status have been widely collected as an outcome in COVID-19 clinical trials. However, published analyses commonly simplify the outcome by neglecting either the ordinal or longitudinal components. Examples include time-to-event analysis based on reaching a particular ordinal state, and analysis of ordinal outcomes at a single timepoint. We instead advocate for the use of the ordinal transition model (OTM), an extension of the proportional odds model to longitudinal outcomes using transition modeling, to analyze ordinal longitudinal data because it leverages the full information within the outcomes. We conducted a comprehensive simulation study to assess the power and statistical efficiency of OTM models compared to simpler methods. Our simulations include scenarios where the assumptions of the OTM are satisfied as well as those where they are violated. For a representative example where assumptions were satisfied, power increased from 0.43 using the time-to-event model to 0.84 using the OTM model. We also present an R package for conducting power calculations using simulation to enable the design of clinical trials using OTM models.

Keywords:

Ordinal longitudinal data|Statistical efficiency|Power|Transition models|Ordinal models|Simulation

Sponsors:

Biometrics Section

Tracks:

Longitudinal/Correlated Data

Can this be considered for alternate subtype?

Yes

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No

I have read and understand that JSM participants must abide by the Participant Guidelines.

Yes

I understand that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is non-refundable.

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