Handling incomplete data in survival analysis with multiple covariates

Yu, Yi, Sun, Yong, & Gu, YuanTong (2011) Handling incomplete data in survival analysis with multiple covariates. In The 5th World Congress on Engineering Asset Management (WCEAM 2010), 25-27 October 2010, Brisbane Convention and Exhibition Centre, Brisbane, Qld.


This paper studies the missing covariate problem which is often encountered in survival analysis. Three covariate imputation methods are employed in the study, and the effectiveness of each method is evaluated within the hazard prediction framework. Data from a typical engineering asset is used in the case study. Covariate values in some time steps are deliberately discarded to generate an incomplete covariate set. It is found that although the mean imputation method is simpler than others for solving missing covariate problems, the results calculated by it can differ largely from the real values of the missing covariates. This study also shows that in general, results obtained from the regression method are more accurate than those of the mean imputation method but at the cost of a higher computational expensive. Gaussian Mixture Model (GMM) method is found to be the most effective method within these three in terms of both computation efficiency and predication accuracy.

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ID Code: 49251
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Survival Analysis, Missing Covariates, Gaussian Mixture Model
ISBN: 9780857293015
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > MECHANICAL ENGINEERING (091300)
Divisions: Current > Schools > School of Chemistry, Physics & Mechanical Engineering
Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Past > Schools > School of Engineering Systems
Deposited On: 23 Mar 2012 03:13
Last Modified: 23 Mar 2012 03:13

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