Analysis of an incomplete longitudinal composite variable using a marginalized random effects model and multiple imputation. (July 2018)

• Record Type: Journal Article
• Authors: Gosho, Masahiko; Maruo, Kazushi; Ishii, Ryota; Hirakawa, Akihiro
• Other Names: Dattner Itai guest-editor.; Huppert Amit guest-editor.
• Is Part Of: Statistical methods in medical research. Volume 27:Number 7(2018)
• Page Start: 2200
• Page End: 2215
• Publication Date: 2018-07
• Subjects: Correlation structure -- item-level missingness -- longitudinal data -- mixed effects model for repeated measures -- subject-level missingness; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280216677879
• Languages: English
• ISSNs: 0962-2802

Abstract

The total score, which is calculated as the sum of scores in multiple items or questions, is repeatedly measured in longitudinal clinical studies. A mixed effects model for repeated measures method is often used to analyze these data; however, if one or more individual items are not measured, the method cannot be directly applied to the total score. We develop two simple and interpretable procedures that infer fixed effects for a longitudinal continuous composite variable. These procedures consider that the items that compose the total score are multivariate longitudinal continuous data and, simultaneously, handle subject-level and item-level missing data. One procedure is based on a multivariate marginalized random effects model with a multiple of Kronecker product covariance matrices for serial time dependence and correlation among items. The other procedure is based on a multiple imputation approach with a multivariate normal model. In terms of the type-1 error rate and the bias of treatment effect in total score, the marginalized random effects model and multiple imputation procedures performed better than the standard mixed effects model for repeated measures analysis with listwise deletion and single imputations for handling item-level missing data. In particular, the mixed effects model for repeated measures with listwise deletion resulted in substantial inflation of the type-1 error rate. The marginalized random effects model and multiple imputation methods provide for a more efficient analysis by fully utilizing the partially available data, compared to the mixed effects model for repeated measures method with listwise deletion.