Beyond the intention-to-treat in comparative effectiveness research

TLDR

Intention‑to‑treat analysis is the default analytic approach in most randomized clinical trials. This review examines the limitations of intention‑to‑treat, as‑treated, and per‑protocol analyses, focusing on issues pertinent to comparative effectiveness research. The authors recommend inverse probability weighting, g‑estimation, and instrumental‑variable techniques—requiring untestable assumptions, dose‑response models, and time‑varying confounder data—to adjust as‑treated and per‑protocol analyses while still reporting intention‑to‑treat estimates. In placebo‑controlled trials, intention‑to‑treat underestimates treatment effects, whereas in active‑comparator trials it can overestimate them, so it does not reliably reflect clinical effectiveness; thus, trials with substantial nonadherence or loss to follow‑up should employ alternative analytic methods.

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.