Beyond the intention-to-treat in comparative effectiveness research

Clin Trials. 2012 Feb;9(1):48-55. doi: 10.1177/1740774511420743. Epub 2011 Sep 23.


Background: The intention-to-treat comparison is the primary, if not the only, analytic approach of many randomized clinical trials.

Purpose: To 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 results: In 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.

Limitations: These analyse require untestable assumptions, a dose-response model, and time-varying data on confounders and adherence.

Conclusions: We 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.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Comparative Effectiveness Research*
  • Intention to Treat Analysis / statistics & numerical data*