Is intent‐to‐treat analysis always (ever) enough?

Abstract

In a commentary written several years ago [1], Professor D. Rubin and I pointed out that one is usually advised to analyse clinical trials – the preferred modern strategy for empirical evaluation of medical therapies [2, 3] – for significance levels and estimates using only, or at least primarily, the intention-to-treat principle (see, e.g. [4]). We noted, however, that the intention-to-treat estimator (see below) estimates so-called ‘use-effectiveness’, the causal effect on outcome of prescribing the drug, rather than the medically more important ‘method-effectiveness’, the causal effect on outcome of actually taking the drug. We argued that studies should be designed so that they can yield valid estimates of method-effectiveness, although so long as protocols are not followed exactly, such estimates may depend heavily on additional assumptions (scientific models – see section on longitudinal data with dropout, below); ones that go beyond the data at hand. In this paper, I present a conceptual framework for thinking about causal estimands and estimators for both use and method effectiveness, with emphasis on the latter, focusing on the problems posed by deviations from protocol, notably noncompliance and dropout. The paper is a review and exposition, rather than a presentation of original material. Section 2 presents a conceptual framework, setting context, defining causal estimands (population quantities describing causal effects of treatments) for use effectiveness and method effectiveness, how these change in the face of noncompliance, and the problem created by dropout; section 3 discusses estimators of the causal estimands defined in section 2; section 4 considers the effect of dropout on the estimators; and section 5 discusses implications for study design. Although different in some details, the ideas, point of view, and notation presented here are essentially those presented in the statistical literature by D. Rubin and colleagues, based on the Rubin Causal Model. Rather than provide extensive references throughout this work, I limit further citations to important additional works which discuss specific points or present examples, and offer here a minimal set of references which provide background and an entree to the relevant literature [5, 6], and a more complete and technical discussion of noncompliance [1, 7–9], dropout [10, 11], and scientific modelling of drug dose-concentration-response [12, 13].