A method for comparing multiple imputation techniques: a case study on the U.S. National COVID Cohort Collaborative

TL;DR

This paper introduces a new framework for evaluating multiple imputation methods to handle missing data in healthcare datasets, demonstrated on COVID-19 patient data from the N3C cohort, highlighting the most effective strategies.

Contribution

The authors propose a novel, generalizable framework for numerically comparing multiple imputation techniques in complex datasets, addressing a key challenge in missing data analysis.

Findings

The framework effectively identified the most valid imputation strategy for the case study.

Different models' behaviors varied with parameter changes, providing insights into their robustness.

The approach is applicable across various research fields and heterogeneous datasets.

Abstract

Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful to assess associations between patients' predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases and the simple removal of these cases may introduce severe bias. For these reasons, several multiple imputation algorithms have been proposed to attempt to recover the missing information. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithms works best in a given scenario. Furthermore, the selection of each algorithm parameters and data-related modelling choices are also both crucial and challenging. In this paper, we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. The experiments presented here show that our approach could effectively highlight the most valid and performant missing-data handling strategy for our case study. Moreover, our methodology allowed us to gain an understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types.