Methods for comparative effectiveness based on time to confirmed disability progression with irregular observations in multiple sclerosis

Real-world data sources offer opportunities to compare the effectiveness of treatments in practical clinical settings. However, relevant outcomes are often recorded selectively and collected at irregular measurement times. It is therefore common to convert the available visits to a standardized schedule with equally spaced visits. Although more advanced imputation methods exist, they are not designed to recover longitudinal outcome trajectories and typically assume that missingness is non-informative. We, therefore, propose an extension of multilevel multiple imputation methods to facilitate the analysis of real-world outcome data that is collected at irregular observation times. We illustrate multilevel multiple imputation in a case study evaluating two disease-modifying therapies for multiple sclerosis in terms of time to confirmed disability progression. This survival outcome is derived from repeated measurements of the Expanded Disability Status Scale, which is collected when patients come to the healthcare center for a clinical visit and for which longitudinal trajectories can be estimated. Subsequently, we perform a simulation study to compare the performance of multilevel multiple imputation to commonly used single imputation methods. Results indicate that multilevel multiple imputation leads to less biased treatment effect estimates and improves the coverage of confidence intervals, even when outcomes are missing not at random.

Automated summary

Real-world data sources provide opportunities to compare treatment effectiveness in practical clinical settings, but relevant outcomes are often selectively recorded and collected at irregular times. This study proposes an extension of multilevel multiple imputation methods to analyze real-world outcome data collected at irregular observation times. The multilevel multiple imputation approach is shown to produce less biased treatment effect estimates and improve the coverage of confidence intervals, even when outcomes are missing not at random.