A robust joint modeling approach for longitudinal data with informative dropouts

Weiping Zhang (), Feiyue Xie and Jiaxin Tan
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Weiping Zhang: University of Science and Technology of China
Feiyue Xie: University of Science and Technology of China
Jiaxin Tan: University of Science and Technology of China

Computational Statistics, 2020, vol. 35, issue 4, No 11, 1759-1783

Abstract: Abstract This article proposes a robust method for analysing longitudinal continuous responses with informative dropouts and potential outliers by using the multivariate t-distribution. We specify a dropout mechanism and a missing covariate distribution and incorporate them into the complete data log-likelihood. Unlike the existing approaches which mainly focus on the inference of regression mean and dropouts process, our approach aims to reveal the dynamics in the location function, marginal scale function and association by joint parsimonious modeling the location and dependence structure. A parametric fractional imputation algorithm is developed to speed up the computation associated with the EM algorithm for maximum likelihood estimation with missing data. The resulting estimators are shown to be consistent and asymptotically normally distributed. Data examples and simulations demonstrate the effectiveness of the proposed approach.

Keywords: Robust estimation; EM algorithm; Joint modeling (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s00180-020-00972-6

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