Performance of standard imputation methods for missing quality of life data as covariate in survival analysis based on simulations from the International Breast Cancer Study Group Trials VI and VII*

Abstract

Imputation methods for missing data on a time-dependent variable within time-dependent Cox models are investigated in a simulation study. Quality of life (QoL) assessments were removed from the complete simulated datasets, which have a positive relationship between QoL and disease-free survival (DFS) and delayed chemotherapy and DFS, by missing at random and missing not at random (MNAR) mechanisms. Standard imputation methods were applied before analysis. Method performance was influenced by missing data mechanism, with one exception for simple imputation. The greatest bias occurred under MNAR and large effect sizes. It is important to carefully investigate the missing data mechanism.

Keywords:

• Imputation methods
• Missing data mechanism
• Quality of life
• Simulation study
• Time-dependent Cox model

Acknowledgments

The authors thank Jürg Bernhard, Karen Price, Richard Gelber, Meredith Regan and the IBCSG for providing us with the breast cancer data.

Appendices containing supplementary summary tables

Full details of the results of the simulation study can be found in Appendix E and Appendix F of Procter M.J. (2016). Influence of missing explanatory variables and longitudinal assessments in breast cancer clinical trials. University of Strathclyde. Dept. of Mathematics and Statistics PhD thesis and available from the University of Strathclyde library website https://www.strath.ac.uk/library.