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Estimands play an important role for aligning study objectives, study design and analyses through a precise definition of the quantity of interest. For COVID-19 studies, apart from intercurrent events, high volume of missing data has been observed. We explore their impact on several estimands through a synthetic COVID-19 data generated from a discrete-time multi-state model. We compare estimators of these estimands based on their ability to closely match the true response rates and retain assumed power. The final choice of the estimand then needs to be aligned with clinically meaningful quantities of interest to patients, clinicians, regulators and payers.

Estimands, Handling of Missing Data and Impact on Assumed Effect Size and Power in Pivotal COVID-19 Treatment Trials