Title
The effect of high prevalence of missing data on estimation of the coefficients of a logistic regression model when using multiple imputation
Author
Austin, P.C.
van Buuren, S.
Publication year
2022
Abstract
Background: Multiple imputation is frequently used to address missing data when conducting statistical analyses. There is a paucity of research into the performance of multiple imputation when the prevalence of missing data is very high. Our objective was to assess the performance of multiple imputation when estimating a logistic regression model when the prevalence of missing data for predictor variables is very high. Methods: Monte Carlo simulations were used to examine the performance of multiple imputation when estimating a multivariable logistic regression model. We varied the size of the analysis samples (N = 500, 1,000, 5,000, 10,000, and 25,000) and the prevalence of missing data (5–95% in increments of 5%). Results: In general, multiple imputation performed well across the range of scenarios. The exceptions were in scenarios when the sample size was 500 or 1,000 and the prevalence of missing data was at least 90%. In these scenarios, the estimated standard errors of the log-odds ratios were very large and did not accurately estimate the standard deviation of the sampling distribution of the log-odds ratio. Furthermore, in these settings, estimated confidence intervals tended to be conservative. In all other settings (i.e., sample sizes > 1,000 or when the prevalence of missing data was less than 90%), then multiple imputation allowed for accurate estimation of a logistic regression model. Conclusions: Multiple imputation can be used in many scenarios with a very high prevalence of missing data. © 2022, The Author(s).
Subject
Missing data
Monte Carlo simulations
Multiple imputation
Major clinical study
Monte Carlo method
Predictor variable
Prevalence
Sample size
To reference this document use:
http://resolver.tudelft.nl/uuid:12959177-a4de-47e5-a5ad-2d7cd5703ee0
DOI
https://doi.org/10.1186/s12874-022-01671-0
TNO identifier
973195
ISSN
1471-2288
Source
BMC Medical Research Methodology, 22 (22)
Document type
article