All Times EDT
Keywords: Intercurrent Event, Estimand, Recovery, Survival Analysis
In clinical trials with the objective to evaluate the treatment effect on time to recovery, such as investigational trials on therapies for COVID-19 hospitalized patients, the patients may face a mortality risk that competes with the opportunity to recover (e.g., be discharged from the hospital). Therefore, an appropriate analytical strategy to account for death as an intercurrent event when defining the estimand of interest is particularly important due to its potential impact on the estimation of the treatment effect. To address this challenge, we conducted a thorough evaluation and comparison of multiple survival analysis methods with different strategies to account for death, including composite variable and hypothetical strategies. The evaluated methods include standard survival analysis methods with different censoring strategies and competing risk analysis methods; semi-parametric and non-parametric methods; and methods using ‘slopes’, ranks, or areas under the curve to estimate inferential statistics. We report results of a comprehensive simulation study that employed design parameters commonly seen in COVID-19 trials and case studies using reconstructed data from a published COVID-19 clinical trial. Our research results demonstrate that, when there is a moderate to large proportion of patients who died before observing their recovery, competing risk analyses and survival analyses with the strategy to censor death at the maximum follow-up timepoint would be able to better detect a treatment effect on recovery than the standard survival analysis that treat death as a non-informative censoring event. The aim of this research is to raise awareness of the importance of handling death appropriately in time-to-recovery analysis when planning clinical trials.