Beyond the treatment effect: Evaluating the effects of patient preferences in randomised trials. (February 2017)

• Record Type: Journal Article
• Authors: Walter, SD; Turner, R; Macaskill, P; McCaffery, KJ; Irwig, L
• Is Part Of: Statistical methods in medical research. Volume 26:Number 1(2017)
• Page Start: 489
• Page End: 507
• Publication Date: 2017-02
• Subjects: Randomised trials -- trial designs -- treatment preferences -- selection effect -- preference effect; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280214550516
• Languages: English
• ISSNs: 0962-2802

Abstract

The treatments under comparison in a randomised trial should ideally have equal value and acceptability – a position of equipoise – to study participants. However, it is unlikely that true equipoise exists in practice, because at least some participants may have preferences for one treatment or the other, for a variety of reasons. These preferences may be related to study outcomes, and hence affect the estimation of the treatment effect. Furthermore, the effects of preferences can sometimes be substantial, and may even be larger than the direct effect of treatment. Preference effects are of interest in their own right, but they cannot be assessed in the standard parallel group design for a randomised trial. In this paper, we describe a model to represent the impact of preferences on trial outcomes, in addition to the usual treatment effect. In particular, we describe how outcomes might differ between participants who would choose one treatment or the other, if they were free to do so. Additionally, we investigate the difference in outcomes depending on whether or not a participant receives his or her preferred treatment, which we characterise through a so-called preference effect. We then discuss several study designs that have been proposed to measure and exploit data on preferences, and which constitute alternatives to the conventional parallel group design. Based on the model framework, we determine which of the various preference effects can or cannot be estimated with each design. We also illustrate these ideas with some examples of preference designs from the literature.