Multiple Imputation as a Better Approach for Analyzing Hierarchical Composite Outcomes

Music City Center 

Hierarchical composite outcomes (HCO) provide a useful alternative to conventional composite outcomes by emphasizing the relative importance of the outcome components. Treatment effects on these HCOs are commonly described using win statistics such as Pocock's win ratio. This framework is often used with multiple censored time-to-event outcomes, which poses an issue due to differential censoring between subjects. Comparisons on these HCOs can lead to intransitive relationships since analyses are based on common follow-up time between pairs of subjects. Multiple imputation circumvents the problem of intransitivity. We propose a multiple imputation (MI) approach to augment a more complete discussion about estimating treatment effects on HCOs by leveraging the parallelism between the win ratio and the cumulative logit odds ratio. We demonstrate the greater utility and interpretability of the MI approach through simulations and practical examples.

Hierarchical Composite Outcome

Multiple Imputation

Win Ratio

Proportional odds logistic regression

Missing data 

Biopharmaceutical Section