Open AccessAccounting for perception, placebo and unmasking effects in estimating treatment effects in randomised clinical trials
Author(s) -
Farid Jamshidian,
Alan Hubbard,
Nicholas P. Jewell
Publication year - 2011
Publication title -
statistical methods in medical research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.952
H-Index - 85
eISSN - 1477-0334
pISSN - 0962-2802
DOI - 10.1177/0962280211413449
Subject(s) - placebo , causal inference , context (archaeology) , proxy (statistics) , clinical trial , treatment effect , medicine , psychological intervention , inference , masking (illustration) , perception , psychology , computer science , alternative medicine , machine learning , artificial intelligence , psychiatry , visual arts , biology , traditional medicine , art , paleontology , pathology , neuroscience
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.