Characterizing and Managing Missing Structured Data in Electronic Health Records: Data Analysis.

ABSTRACT:

Background

Missing data is a challenge for all studies; however, this is especially true for electronic health record (EHR)-based analyses. Failure to appropriately consider missing data can lead to biased results. While there has been extensive theoretical work on imputation, and many sophisticated methods are now available, it remains quite challenging for researchers to implement these methods appropriately. Here, we provide detailed procedures for when and how to conduct imputation of EHR laboratory results.

Objective

The objective of this study was to demonstrate how the mechanism of missingness can be assessed, evaluate the performance of a variety of imputation methods, and describe some of the most frequent problems that can be encountered.

Methods

We analyzed

SUBMITTER: Beaulieu-Jones BK 

PROVIDER: S-EPMC5845101 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature