19 Increasing Statistical Efficiency using Ordinal Transition Models: A Simulation Study

Oregon Convention Center 

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.

Ordinal longitudinal data

Statistical efficiency

Power

Transition models

Ordinal models

Simulation 

Biometrics Section