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STATISTICAL ANALYSIS OF POSSIBLE BIAS OF CLINICAL JUDGEMENTS DUE TO OBSERVING AN ON‐THERAPY MARKER VARIABLE
Author(s) -
BOATENG FRANCIS,
SAMPSON ALLAN,
SCHWAB BARRY
Publication year - 1996
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(19960830)15:16<1747::aid-sim342>3.0.co;2-v
Subject(s) - categorical variable , clinical trial , linear discriminant analysis , medicine , variable (mathematics) , statistics , computer science , artificial intelligence , machine learning , mathematics , mathematical analysis
In certain double‐blind clinical trials there is the possibility that certain ‘marker variables’ observable during the trial may in part unblind the trial, even at a subliminal level. At issue is whether or not this potential unblinding biases the investigators clinical efficacy assessments. This issue arose after the completion of three clinical trials that compared tretinoin emollient cream (TEC) 0⋅05 per cent to its vehicle in patients with photodamaged skin. The question raised was whether or not possible ‘subliminal unblinding’ of the investigators and patients, due to the cutaneous irritation associated with topical tretinoin, might have caused a treatment bias in the study. To address this issue, we undertook a reanalysis of these three clinical trials. In doing so, we develop in this paper a statistical modelling approach to address issues of possible bias introduced by the ability to observe such marker variables. The approach utilizes a linear discriminant analysis to introduce an auxiliary categorical variable for the efficacy analysis. A suitable categorical data model permits the estimation of relevant bias effects. We illustrate this approach with data from the three TEC 0⋅05 per cent trials.