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Open Access Research article

Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study

Andrea Marshall12*, Douglas G Altman1, Patrick Royston3 and Roger L Holder4

Author Affiliations

1 Centre for Statistics in Medicine, University of Oxford, Oxford, UK

2 Warwick Clinical Trials Unit, University of Warwick, Coventry, UK

3 Hub for Trials Methodology Research and UCL, MRC Clinical Trials Unit, London, UK

4 Department of Primary Care Clinical Sciences, University of Birmingham, Birmingham, UK

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BMC Medical Research Methodology 2010, 10:7  doi:10.1186/1471-2288-10-7

Published: 19 January 2010

Abstract

Background

There is no consensus on the most appropriate approach to handle missing covariate data within prognostic modelling studies. Therefore a simulation study was performed to assess the effects of different missing data techniques on the performance of a prognostic model.

Methods

Datasets were generated to resemble the skewed distributions seen in a motivating breast cancer example. Multivariate missing data were imposed on four covariates using four different mechanisms; missing completely at random (MCAR), missing at random (MAR), missing not at random (MNAR) and a combination of all three mechanisms. Five amounts of incomplete cases from 5% to 75% were considered. Complete case analysis (CC), single imputation (SI) and five multiple imputation (MI) techniques available within the R statistical software were investigated: a) data augmentation (DA) approach assuming a multivariate normal distribution, b) DA assuming a general location model, c) regression switching imputation, d) regression switching with predictive mean matching (MICE-PMM) and e) flexible additive imputation models. A Cox proportional hazards model was fitted and appropriate estimates for the regression coefficients and model performance measures were obtained.

Results

Performing a CC analysis produced unbiased regression estimates, but inflated standard errors, which affected the significance of the covariates in the model with 25% or more missingness. Using SI, underestimated the variability; resulting in poor coverage even with 10% missingness. Of the MI approaches, applying MICE-PMM produced, in general, the least biased estimates and better coverage for the incomplete covariates and better model performance for all mechanisms. However, this MI approach still produced biased regression coefficient estimates for the incomplete skewed continuous covariates when 50% or more cases had missing data imposed with a MCAR, MAR or combined mechanism. When the missingness depended on the incomplete covariates, i.e. MNAR, estimates were biased with more than 10% incomplete cases for all MI approaches.

Conclusion

The results from this simulation study suggest that performing MICE-PMM may be the preferred MI approach provided that less than 50% of the cases have missing data and the missing data are not MNAR.