Beyond the intention-to-treat in comparative effectiveness research
Year: 2011
Type: article
Abstract: 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.
Source: Clinical Trials
Authors: Miguel A Hernán, Sonia Hernández-Díaz
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2136833093