Abstract
Composite endpoints are commonly used to define primary outcomes in randomized controlled trials. A participant may be classified as meeting the endpoint if they experience an event in one or several components (eg, a favorable outcome based on a composite of being alive and attaining negative culture results in trials assessing tuberculosis treatments). Partially observed components that are not missing simultaneously complicate the analysis of the composite endpoint. An intuitive strategy frequently used in practice for handling missing values in the components is to derive the values of the composite endpoint from observed components when possible, and exclude from analysis participants whose composite endpoint cannot be derived. Alternatively, complete record analysis (CRA) (excluding participants with any missing components) or multiple imputation (MI) can be used. We compare a set of methods for analyzing a composite endpoint with partially observed components mathematically and by simulation, and apply these methods in a reanalysis of a published trial (TOPPS). We show that the derived composite endpoint can be missing not at random even when the components are missing completely at random. Consequently, the treatment effect estimated from the derived endpoint is biased while CRA results without the derived endpoint are valid. Missing at random mechanisms require MI of the components. We conclude that, although superficially attractive, deriving the composite endpoint from observed components should generally be avoided. Despite the potential risk of imputation model mis-specification, MI of missing components is the preferred approach in this study setting.
Highlights
Composite endpoints are commonly used to define primary outcomes in randomized controlled trials, such as those in rheumatoid arthritis, tuberculosis, and cardiovascular diseases.[1,2,3,4,5] A composite endpoint can be constructed from two or more components
While main effect and stratified by x (MIC-x)-z1, which accounts for the three-way interaction between the components and randomized treatment in the conditional imputation models, is the only correct approach in these cases, bias in MIC-x appears to be minimal for both parameter estimates across the missingness mechanisms
Multiple imputation (MI) at the component level occasionally suffered from perfect prediction when imputation was stratified by randomized treatment x and fully observed component z1 (MIC-x-z1); all occurrences of perfect prediction were overcome when augmentation was used in MI.[21]
Summary
Composite endpoints are commonly used to define primary outcomes in randomized controlled trials, such as those in rheumatoid arthritis, tuberculosis, and cardiovascular diseases.[1,2,3,4,5] A composite endpoint can be constructed from two or more components. A strategy often used in practice is to perform a complete record analysis (CRA) in which only participants with observed data in all components are included. Such a strategy may yield less efficient, and potentially even biased, estimates when the components are not missing completely at random (MCAR)
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