Intention to treat analysis in clinical trials when there are missing data

Open Access
Publisher Website
Google Scholar
Abstract
Whenever follow up data are incomplete, the researcher faces some major problems. Firstly, because fewer subjects have complete data than was originally planned for, the study may be under powered; that is, not have enough subjects in order to show that the difference between the groups is statistically significant, even though it may be clinically important.1 Secondly, it is a dictum of research that people do not drop out of studies for trivial reasons. Those who do not complete a trial of a new treatment for depression, for example, may be those who (a) improved the most, and don't see the necessity of continuing; (b) improved the least, and see no reason to continue to comply with a programme that isn't working for them; or (c) may have become so depressed that they committed suicide. If the majority of dropouts are those who improved, then this will serve to make the interventions appear less effective than they actually are. Conversely, if most of the people dropped out because the new treatment was ineffective, it will, paradoxically, make the intervention look better, because many of the non-responders are no longer in that arm of the study. It is generally accepted, therefore, that the most clinically informative, as well as the most statistically robust, method of analysis is an intention to treat (ITT) analysis, which includes all randomised study participants in the groups to which they were randomised. But how do you do an ITT analysis if an appreciable number of patients have dropped out?