How much missing data is too much to impute for longitudinal health indicators? A preliminary guideline for the choice of the extent of missing proportion to impute with multiple imputation by chained equations.

ABSTRACT:

Background

The multiple imputation by chained equations (MICE) is a widely used approach for handling missing data. However, its robustness, especially for high missing proportions in health indicators, is under-researched. The study aimed to provide a preliminary guideline for the choice of the extent of missing proportion to impute longitudinal health-related data using the MICE method.

Methods

The study obtained complete data on five mortality-related health indicators of 100 countries (2015-2019) from the Global Health Observatory. Nine incomplete datasets with missing rates from 10 to 90% were generated and imputed using MICE. The robustness of MICE was assessed through three approaches: comparison of means using the Repeated Measures- Analysis of variance, estimation o

SUBMITTER: Junaid KP 

PROVIDER: S-EPMC11787761 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature