MIIPW: An R package for Generalized Estimating Equations with missing data integration using a combination of mean score and inverse probability weighted approaches and multiple imputation

The presence of missing values in longitudinal cohort data can lead to inaccuracies in parameter estimation and decreased statistical power. To address this issue, we have developed the MIIPW R package, which incorporates the mean score and inverse probability weighted methods in generalized estimating equations to impute missing scores due to incomplete covariate data. Augmentation of incomplete data in the estimating equation is done through a multiple imputation model. The package employs various methods such as mean score, SIPW, AIPW, miSIPW, and miAIPW to estimate parameters while considering four different covariance structures (AR-1, Exchangeable, Unstructured, and Independent). Additionally, the package uses the QIC for model selection and pays special attention to the calculation of weights within the dataset. The performance of the above mentioned methods has been evaluated through simulation study as well as real data analysis. The MIIPW package is available for download from the comprehensive R archive network and this article provides a practical guide to solve missing data issues. https://CRAN.R-project.org/package=MIIPW.

Automated summary

This article introduces the features and applications of the MIIPW R package, which can be used to address missing values in longitudinal cohort data. It also provides a practical guide for solving missing data issues.