Missing data matter: an empirical evaluation of the impacts of missing EHR data in comparative effectiveness research.

ABSTRACT:

Objectives

The impacts of missing data in comparative effectiveness research (CER) using electronic health records (EHRs) may vary depending on the type and pattern of missing data. In this study, we aimed to quantify these impacts and compare the performance of different imputation methods.

Materials and methods

We conducted an empirical (simulation) study to quantify the bias and power loss in estimating treatment effects in CER using EHR data. We considered various missing scenarios and used the propensity scores to control for confounding. We compared the performance of the multiple imputation and spline smoothing methods to handle missing data.

Results

When missing data depended on the stochastic progression of disease and medical practice patterns, the spline sm

SUBMITTER: Zhou Y 

PROVIDER: S-EPMC10280351 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature