The intention to treat principle requires all participants in a clinical trial to be included in the analysis in the groups to which they were randomised, regardless of any departures from randomised treatment.1 This principle is a key defence against bias, since participants who depart from randomised treatment are usually a non-random subset whose exclusion can lead to serious selection bias.2

However, it is unclear how to apply the intention to treat principle when investigators are unable to follow up all randomised participants. Filling in (imputing) the missing values is often seen as the only alternative to omitting participants from the analysis.3 In particular, imputing by “last observation carried forward” is widely used,4 but this approach has serious drawbacks.3 For example, last observation carried forward was applied in a recent trial of a novel drug treatment in Alzheimer’s disease.5 The analysis was criticised because it effectively assumed that loss to follow-up halts disease progression,6 but the authors argued that their analysis was in fact conservative.7 Increasingly, trialists are expected to justify their handling of missing data and not simply rely on techniques that have been used in other clinical contexts.8

To guide investigators dealing with these tricky issues, we propose a four point framework for dealing with incomplete observations (box). Our aim is not to describe specific methods for analysing missing data, since these are described elsewhere,9 10 but to provide the framework within which methods can be chosen and implemented. We argue that all observed data should be included in the …