Proper analysis in clinical trials: How to report and adjust for missing outcome data

Missing data is a problem that occurs widely in medical research, and indeed is difficult to avoid. The aim of this commentary is to deal with its implications in clinical trials. In analysing the results of a randomised controlled trial, it is important to ensure that the main benefit of randomisation has not been compromised: namely, that the treatment arms (experimental and control) remain comparable in everything except the interventions being compared.1 Missing outcome data is a serious problem because of its ability to bias the findings of a clinical trial. The bias affects the direction of the effect observed, but data loss can also affect the precision, making the results unreliable.

A good definition of missing data is given by Little, ‘values that are not available and that would be meaningful for analysis if they were observed’.2 As our interest is in trials, we concentrate on missing outcome data, although methods have been developed to handle missing covariates as well. The bias because of missing data may depend on the reason why data are missing. We therefore have to consider the mechanism of how data comes to be lost.