The effect of high prevalence of missing data on estimation of the coefficients of a logistic regression model when using multiple imputation.
BMC Med Res Methodol
; 22(1): 196, 2022 07 18.
Article
en En
| MEDLINE
| ID: mdl-35850734
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.Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Proyectos de Investigación
Tipo de estudio:
Etiology_studies
/
Prevalence_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
BMC Med Res Methodol
Asunto de la revista:
MEDICINA
Año:
2022
Tipo del documento:
Article
País de afiliación:
Canadá