Comparison of imputation methods for handling missing covariate data when fitting a Cox proportional hazards model: a resampling study.

ABSTRACT:

Background

The appropriate handling of missing covariate data in prognostic modelling studies is yet to be conclusively determined. A resampling study was performed to investigate the effects of different missing data methods on the performance of a prognostic model.

Methods

Observed data for 1000 cases were sampled with replacement from a large complete dataset of 7507 patients to obtain 500 replications. Five levels of missingness (ranging from 5% to 75%) were imposed on three covariates using a missing at random (MAR) mechanism. Five missing data methods were applied; a) complete case analysis (CC) b) single imputation using regression switching with predictive mean matching (SI), c) multiple imputation using regression switching imputation, d) multiple imputation using r

SUBMITTER: Marshall A 

PROVIDER: S-EPMC3019210 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature