Title: Evaluation of missing data imputation in longitudinal cohort studies in breast cancer survival

Authors: Ana S. Fernandes, Jose M. Fonseca, Ian H. Jarman, Terence A. Etchells, Paulo J.G. Lisboa, Elia Biganzoli, Chris Bajdik

Addresses: Faculdade de Ciencias e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal. ' Faculdade de Ciencias e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal. ' School of Computing and Mathematical Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK. ' School of Computing and Mathematical Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK. ' School of Computing and Mathematical Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK. ' Universita degli Studi di Milano, 20133 Milano, Italy. ' British Columbia Cancer Agency, Vancouver BC V5Z 1L3, Canada

Abstract: Missing values are common in medical datasets and may be amenable to data imputation when modelling a given data set or validating on an external cohort. This paper discusses model averaging over samples of the imputed distribution and extends this approach to generic non-linear modelling with the partial logistic artificial neural network (PLANN) regularised with automatic relevance determination (ARD). The study then applies the imputation to external validation, considering also predictions made for individual patients. A prognostic index is defined for the non-linear model and validation results show that four statistically significant risk groups identified at 95% level of confidence from the modelling data, from Christie Hospital (n = 931), retain good separation during external validation with data from the BC Cancer Agency (BCCA) (n = 4,083). A satisfactory discrimination and calibration performance was assessed with the time dependent C index (C^td) and Hosmer-Lemeshow statistic, respectively, for both, training and validated model.

Keywords: missing data; missing values; data imputation; survival analysis; nonlinear modelling; breast cancer survival; artificial neural networks; partial logistic ANNs; PLANN; automatic relevance determination.

DOI: 10.1504/IJKESDP.2009.028818

International Journal of Knowledge Engineering and Soft Data Paradigms, 2009 Vol.1 No.3, pp.257 - 276

Published online: 03 Oct 2009 *

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