• Miguel A Hernán and Sonia Hernández-Díaz.
• Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA. miguel_hernan@post.harvard.edu
• Clin Trials. 2012 Feb 1; 9 (1): 48-55.

BackgroundThe intention-to-treat comparison is the primary, if not the only, analytic approach of many randomized clinical trials.PurposeTo review the shortcomings of intention-to-treat analyses, and of 'as treated' and 'per protocol' analyses as commonly implemented, with an emphasis on problems that are especially relevant for comparative effectiveness research.Methods And ResultsIn placebo-controlled randomized clinical trials, intention-to-treat analyses underestimate the treatment effect and are therefore nonconservative for both safety trials and noninferiority trials. In randomized clinical trials with an active comparator, intention-to-treat estimates can overestimate a treatment's effect in the presence of differential adherence. In either case, there is no guarantee that an intention-to-treat analysis estimates the clinical effectiveness of treatment. Inverse probability weighting, g-estimation, and instrumental variable estimation can reduce the bias introduced by nonadherence and loss to follow-up in 'as treated' and 'per protocol' analyses.LimitationsThese analyse require untestable assumptions, a dose-response model, and time-varying data on confounders and adherence.ConclusionsWe recommend that all randomized clinical trials with substantial lack of adherence or loss to follow-up are analyzed using different methods. These include an intention-to-treat analysis to estimate the effect of assigned treatment and 'as treated' and 'per protocol' analyses to estimate the effect of treatment after appropriate adjustment via inverse probability weighting or g-estimation.

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