Accounting for perception, placebo and unmasking effects in estimating treatment effects in randomised clinical trials. (June 2014)

• Record Type: Journal Article
• Authors: Jamshidian, Farid; Hubbard, Alan E; Jewell, Nicholas P
• Is Part Of: Statistical methods in medical research. Volume 23:Number 3(2014:Jun.)
• Page Start: 293
• Page End: 307
• Publication Date: 2014-06
• Subjects: Direct causal effects -- G-computation -- maximum likelihood estimation -- pain studies -- perception effect -- placebo effect -- semi-parametric estimation -- targeted maximum likelihood estimation -- unblinding -- unmasking effect; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280211413449
• Languages: English
• ISSNs: 0962-2802

Abstract

There is a rich literature on the role of placebos in experimental design and evaluation of therapeutic agents or interventions. The importance of masking participants, investigators and evaluators to treatment assignment (treatment or placebo) has long been stressed as a key feature of a successful trial design. Nevertheless, there is considerable variability in the technical definition of the placebo effect and the impact of treatment assignments being unmasked. We suggest a formal concept of a 'perception effect' and define unmasking and placebo effects in the context of randomised trials. We employ modern tools from causal inference to derive semi-parametric estimators of such effects. The methods are illustrated on a motivating example from a recent pain trial where the occurrence of treatment-related side effects acts as a proxy for unmasking.