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*

Marion Procter, Chris Robertson

Research output: Contribution to journalArticlepeer-review

2 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)3063-3077
Number of pages15
JournalCommunications in Statistics: Simulation and Computation
Issue number10
Early online date13 Aug 2018
Publication statusPublished - 26 Nov 2019


  • imputation methods
  • missing data mechanism
  • quality of life
  • simulation study
  • time-dependent Cox model

Cite this