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Computational and Mathematical Methods in Medicine
Volume 2012 (2012), Article ID 821643, 11 pages
http://dx.doi.org/10.1155/2012/821643
Research Article

Application of Seemingly Unrelated Regression in Medical Data with Intermittently Observed Time-Dependent Covariates

1Department of Epidemiology, School of Health & Nutrition, Shiraz University of Medical Sciences, Shiraz 7153675541, Iran
2Department of Biostatistics, School of Medicine, Shiraz University of Medical Sciences, Shiraz 7134845794, Iran
3Department of Internal Medicine, School of Medicine, Shiraz University of Medical Sciences, Shiraz 7134845794, Iran

Received 17 June 2012; Revised 17 October 2012; Accepted 31 October 2012

Academic Editor: Guang Wu

Copyright © 2012 Sareh Keshavarzi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background. In many studies with longitudinal data, time-dependent covariates can only be measured intermittently (not at all observation times), and this presents difficulties for standard statistical analyses. This situation is common in medical studies, and methods that deal with this challenge would be useful. Methods. In this study, we performed the seemingly unrelated regression (SUR) based models, with respect to each observation time in longitudinal data with intermittently observed time-dependent covariates and further compared these models with mixed-effect regression models (MRMs) under three classic imputation procedures. Simulation studies were performed to compare the sample size properties of the estimated coefficients for different modeling choices. Results. In general, the proposed models in the presence of intermittently observed time-dependent covariates showed a good performance. However, when we considered only the observed values of the covariate without any imputations, the resulted biases were greater. The performances of the proposed SUR-based models in comparison with MRM using classic imputation methods were nearly similar with approximately equal amounts of bias and MSE. Conclusion. The simulation study suggests that the SUR-based models work as efficiently as MRM in the case of intermittently observed time-dependent covariates. Thus, it can be used as an alternative to MRM.