Adjusting for partially missing baseline measurements in randomized trials.
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.
White
Ian R
IR
MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge CB2 2SR, U.K. ian.white@mrc-bsu.cam.ac.uk
Thompson
Simon G
SG
D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D017060:Patient Satisfaction; D011618:Psychotic Disorders / Q000628:therapy; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D018401:Sample Size