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Statistics in medicine
03 15, 2022;41 (6): 1042-1058.

Multiple imputation for longitudinal data using Bayesian lasso imputation model.

Yusuke Yamaguchi, Satoshi Yoshida, Toshihiro Misumi, Kazushi Maruo
Author Information
  1. Yusuke Yamaguchi: Data Science, Development, Astellas Pharma Inc., Tokyo, Japan. ORCID
  2. Satoshi Yoshida: Data Science, Development, Astellas Pharma Inc., Tokyo, Japan.
  3. Toshihiro Misumi: Department of Biostatistics, School of Medicine, Yokohama City University, Yokohama, Japan.
  4. Kazushi Maruo: Department of Biostatistics, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan. ORCID
PMID: 35064581 DOI: 10.1002/sim.9315

Abstract

Multiple imputation is a promising approach to handle missing data and is widely used in analysis of longitudinal clinical studies. A key consideration in the implementation of multiple imputation is to obtain accurate imputed values by specifying an imputation model that incorporates auxiliary variables potentially associated with missing variables. The use of informative auxiliary variables is known to be beneficial to make the missing at random assumption more plausible and help to reduce uncertainty of the imputations; however, it is not straightforward to pre-specify them in many cases. We propose a data-driven specification of the imputation model using Bayesian lasso in the context of longitudinal clinical study, and develop a built-in function of the Bayesian lasso imputation model which is performed within the framework of multiple imputation using chained equations. A simulation study suggested that the Bayesian lasso imputation model worked well in a variety of longitudinal study settings, providing unbiased treatment effect estimates with well-controlled type I error rates and coverage probabilities of the confidence interval; in contrast, ignorance of the informative auxiliary variables led to serious bias and inflation of type I error rate. Moreover, the Bayesian lasso imputation model offered higher statistical powers compared with conventional imputation methods. In our simulation study, the gains in statistical power were remarkable when the sample size was small relative to the number of auxiliary variables. An illustration through a real example also suggested that the Bayesian lasso imputation model could give smaller standard errors of the treatment effect estimate.

Keywords

Bayesian lasso longitudinal clinical study missing data multiple imputation

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MeSH Term

Bayes Theorem
Bias
Computer Simulation
Data Interpretation, Statistical
Humans
Longitudinal Studies
Models, Statistical
Journal Article
Article has an altmetric score of 2

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