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Research Methods & Reporting

Strategy for intention to treat analysis in randomised trials with missing outcome data

BMJ 2011; 342 doi: (Published 07 February 2011) Cite this as: BMJ 2011;342:d40
  1. Ian R White, senior statistician1,
  2. Nicholas J Horton, associate professor of mathematics and statistics2,
  3. James Carpenter3,
  4. reader in medical and social statistics,
  5. Stuart J Pocock, professor of medical statistics3
  1. 1MRC Biostatistics Unit, Cambridge CB2 0SR, UK
  2. 2Department of Mathematics and Statistics, Smith College, Clark Science Center, Northampton, MA 01063-0001, USA
  3. 3Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
  1. Correspondence to: I RWhite ian.white{at}
  • Accepted 5 November 2010

Loss to follow-up is often hard to avoid in randomised trials. This article suggests a framework for intention to treat analysis that depends on making plausible assumptions about the missing data and including all participants in sensitivity analyses

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 …

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