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*

Tools

Procter, Marion and Robertson, Chris (2019) 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*. Communications in Statistics: Simulation and Computation, 48 (10). pp. 3063-3077. ISSN 0361-0918 (https://doi.org/10.1080/03610918.2018.1473587)

[thumbnail of Procter-Robertson-CSSC-2018-Performance-of-standard-imputation-methods-for-missing-quality-of-life-data]
Preview
 Text. Filename: Procter_Robertson_CSSC_2018_Performance_of_standard_imputation_methods_for_missing_quality_of_life_data.pdf
Accepted Author Manuscript
Download (328kB)| Preview

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.

CORE (COnnecting REpositories)