Adjusting for partially missing baseline measurements in randomized trials
Article first published online: 29 NOV 2004
Copyright © 2004 John Wiley & Sons, Ltd.
Statistics in Medicine
Volume 24, Issue 7, pages 993–1007, 15 April 2005
How to Cite
White, I. R. and Thompson, S. G. (2005), Adjusting for partially missing baseline measurements in randomized trials. Statist. Med., 24: 993–1007. doi: 10.1002/sim.1981
- Issue published online: 25 FEB 2005
- Article first published online: 29 NOV 2004
- Manuscript Accepted: JUN 2004
- Manuscript Received: APR 2003
- randomized trials;
- analysis of covariance;
- missing covariate;
Adjustment for baseline variables in a randomized trial can increase power to detect a treatment effect. However, when baseline data are partly missing, analysis of complete cases is inefficient. We consider various possible improvements in the case of normally distributed baseline and outcome variables. Joint modelling of baseline and outcome is the most efficient method. Mean imputation is an excellent alternative, subject to three conditions. Firstly, if baseline and outcome are correlated more than about 0.6 then weighting should be used to allow for the greater information from complete cases. Secondly, imputation should be carried out in a deterministic way, using other baseline variables if possible, but not using randomized arm or outcome. Thirdly, if baselines are not missing completely at random, then a dummy variable for missingness should be included as a covariate (the missing indicator method). The methods are illustrated in a randomized trial in community psychiatry. Copyright © 2004 John Wiley & Sons, Ltd.