Beyond the intention-to-treat in comparative effectiveness research

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.

Figure 1

Simplified causal diagram for an RCT with assigned treatment Z, received treatment A, and outcome Y. U represents the unmeasured common causes of A and Y. An “as treated” analysis of the A-Y association will be confounded unless all prognostic factors L are adjusted for.

Figure 2

Simplified causal diagram for an RCT with assigned treatment Z, received treatment A, and outcome Y. U represents the unmeasured common causes of A and Y, and S an indicator for selection into the “per protocol” population. The Z-Y association in the “per protocol” population (a restriction represented by the box around S), will be affected by selection bias unless all prognostic factors L are adjusted for.