Studying missingness in spinal cord injury data: challenges and impact of data imputation.

ABSTRACT:

Background

In the last decades, medical research fields studying rare conditions such as spinal cord injury (SCI) have made extensive efforts to collect large-scale data. However, most analysis methods rely on complete data. This is particularly troublesome when studying clinical data as they are prone to missingness. Often, researchers mitigate this problem by removing patients with missing data from the analyses. Less commonly, imputation methods to infer likely values are applied.

Objective

Our objective was to study how handling missing data influences the results reported, taking the example of SCI registries. We aimed to raise awareness on the effects of missing data and provide guidelines to be applied for future research projects, in SCI research and beyond.

Metho

SUBMITTER: Bourguignon L 

PROVIDER: S-EPMC10770973 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature