Handling Missing Data in Real-World Evidence Studies Managing Missing Data in RWE Studies

Abstract

Missing data is a pervasive challenge in real-world evidence (RWE) studies, arising from incomplete or inconsistent data collection. Proper handling of missing data is critical to ensure the validity and reliability of study outcomes. This paper explores strategies to address missingness, focusing on mechanisms, methods, and tools available to researchers.

Understanding the underlying mechanism of missingness—Missing Completely at Random (MCAR), Missing at Random (MAR), or Missing Not at Random (MNAR)—is the foundation of effective data management. Methods such as complete case analysis and single imputation are suitable for MCAR scenarios but may introduce bias or underestimate variability. Advanced approaches, like multiple imputation and maximum likelihood estimation, better address MAR data, preserving uncertainty and improving robustness. For MNAR cases, sensitivity analyses are essential to evaluate the impact of missingness on study conclusions.

Innovative tools in R, including mice, missForest, VIM, and naniar, enable effective imputation, visualization, and modeling of missing data. Machine learning techniques and Bayesian approaches offer promising alternatives for complex datasets. Combining methods, such as multiple imputation followed by sensitivity analysis, ensures more reliable inferences.

Best practices emphasize assessing missingness patterns, transparent documentation of assumptions, and thorough reporting of strategies used to address missing data. Adopting these approaches minimizes bias and enhances the credibility of RWE studies, ultimately supporting better-informed healthcare decisions. This paper underscores the importance of a systematic, informed approach to handling missing data in RWE.