Operating characteristics of analysis methods for clinical trials in viral respiratory disease: A simulation study protocol

Acute viral respiratory infections (ARVIs) are a major cause of hospitalization and death worldwide, yet randomized clinical trials in this setting face substantial challenges in selecting efficient and clinically meaningful primary endpoints. Mortality is often too infrequent to serve as a feasible primary endpoint. Several alternative approaches have been proposed, including ordinal scales, time-to-event endpoints, recovery-based composite outcomes, and longitudinal ordinal models. However, their comparative operating characteristics under realistic ARVI disease courses remain insufficiently understood. We describe a simulation study to compare the type I error and power of commonly used and recently proposed endpoints and analysis strategies for two-arm randomized trials in hospitalized participants with ARVIs. Data will be generated under several mechanisms designed to mimic plausible participant trajectories, including a latent Brownian motion process, a first-order ordinal Markov process, a latent recurrent-event process with frailty, and resampling from individual participant data from the ACTT-2 trial. Simulated outcomes will use 4-, 6-, and 8-level ordinal severity scales and will reflect moderately and severely ill populations, follow-up horizons of 28 or 60 days, varying treatment effects, and sample sizes. Methods to be compared include Markov ordinal state transition models, proportional-odds models at a fixed time point, days-to-recovery scale analyses, Cox models for time-to-event endpoints, logistic regression for binary endpoints, generalized pairwise comparisons for hierarchical composites, and t-tests for days alive and out of hospital. This study will provide a systematic comparison of endpoint definitions and analysis methods for ARVI trials under clinically motivated data-generating mechanisms. The results are intended to inform the selection of feasible, interpretable, and statistically efficient primary analysis strategies for future trials in viral respiratory disease.