Including all individuals is not enough: lessons for intention-to-treat analysis.


White, IR; Carpenter, J; Horton, NJ; (2012) Including all individuals is not enough: lessons for intention-to-treat analysis. Clinical trials (London, England), 9 (4). pp. 396-407. ISSN 1740-7745 DOI: https://doi.org/10.1177/1740774512450098

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Abstract

BACKGROUND Intention-to-treat (ITT) analysis requires all randomised individuals to be included in the analysis in the groups to which they were randomised. However, there is confusion about how ITT analysis should be performed in the presence of missing outcome data. PURPOSES To explain, justify, and illustrate an ITT analysis strategy for randomised trials with incomplete outcome data. METHODS We consider several methods of analysis and compare their underlying assumptions, plausibility, and numbers of individuals included. We illustrate the ITT analysis strategy using data from the UK700 trial in the management of severe mental illness. RESULTS Depending on the assumptions made about the missing data, some methods of analysis that include all randomised individuals may be less valid than methods that do not include all randomised individuals. Furthermore, some methods of analysis that include all randomised individuals are essentially equivalent to methods that do not include all randomised individuals. LIMITATIONS This work assumes that the aim of analysis is to obtain an accurate estimate of the difference in outcome between randomised groups and not to obtain a conservative estimate with bias against the experimental intervention. CONCLUSIONS Clinical trials should employ an ITT analysis strategy, comprising a design that attempts to follow up all randomised individuals, a main analysis that is valid under a stated plausible assumption about the missing data, and sensitivity analyses that include all randomised individuals in order to explore the impact of departures from the assumption underlying the main analysis. Following this strategy recognises the extra uncertainty arising from missing outcomes and increases the incentive for researchers to minimise the extent of missing data.

Item Type: Article
Faculty and Department: Faculty of Epidemiology and Population Health > Dept of Medical Statistics
PubMed ID: 22752633
Web of Science ID: 307982600003
URI: http://researchonline.lshtm.ac.uk/id/eprint/1883947

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