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

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.