A New Robust Imputation Method for Longitudinal Data with Non-Normal Continuous Outcomes

Nesma M.  Darwish; Yasmin A.  Mohamed; Ahmed M.  Gad; Abdelnaser S.  Abdrabou; Wafaa M.  Ibrahim

doi:10.6000/1929-6029.2025.14.70

Authors

Nesma M. Darwish Faculty of Political Science, Economics and Business Administration, May University, Egypt
Yasmin A. Mohamed Statistics Department, Faculty of Economics and Political Science, Cairo University, Egypt
Ahmed M. Gad Statistics Department, Faculty of Economics and Political Science, Cairo University, Egypt and Business Administration Department, Faculty of Business Administration, Economics and Political Science, The British University in Egypt (BUE), Egypt
Abdelnaser S. Abdrabou Statistics Department, Faculty of Economics and Political Science, Cairo University, Egypt
Wafaa M. Ibrahim Statistics Department, Faculty of Economics and Political Science, Cairo University, Egypt

DOI:

https://doi.org/10.6000/1929-6029.2025.14.70

Keywords:

Longitudinal data, Missing values, Robust imputation, Single imputation

Abstract

Missing values is very common in longitudinal data and it is the main challenge in analysis of longitudinal data. Missing values have a significant effect on longitudinal data analysis because they lead to loss of information, biased estimates, and misleading results. In practice there is a need for an imputation method to deal with missing values.

Aim: In this study a new robust regression-based imputation method to deal with missing values in longitudinal data is proposed. This method utilizes the modified adaptive linear regression model and does not require the normality of the responses. It is a novel robust imputation method as it is introduced for the first time.

Results and Conclusion: The simulation results show that the proposed method performs well compared to other methods especially for multivariate t-distribution and Chi-square distribution. Also, the proposed approach is effective apart from the missingness rate.

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