title: Adjusting for bias in unblinded randomized controlled trials.
abstract: It may not always be possible to blind participants of a randomized controlled trial for treatment allocation. As a result, estimators of the actual treatment effect may be biased. In this paper, we will extend a novel method, originally introduced in genetic research, for instrumental variable meta-analysis, adjusting for bias due to unblinding of trial participants. Using simulation studies, this novel method, "Egger Correction for non-Adherence", is introduced and compared to the performance of the "intention-to-treat," "as-treated," and conventional "instrumental variable" estimators. Scenarios considered (time-varying) non-adherence, confounding, and between-study heterogeneity. The effect of treatment on a binary endpoint was quantified by means of a risk difference. In all scenarios with unblinded treatment allocation, the Egger Correction for non-Adherence method was the least biased estimator. However, unless the variation in adherence was relatively large, precision was lacking, and power did not surpass 0.50. As a comparison, in a meta-analysis of blinded randomized controlled trials, power of the conventional IV estimator was 1.00 versus at most 0.14 for the Egger Correction for non-Adherence estimator. Due to this lack of precision and power, we suggest to use this method mainly as a sensitivity analysis.
authors.affiliation: 1 Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK.
authors.forename: A F
authors.initials: AF
authors.lastname: Schmidt
authors.affiliation: 2 Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands.
authors.forename: Rhh
authors.initials: R
authors.lastname: Groenwold
mesh_terms: D015982:Bias; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D057194:Intention to Treat Analysis; D016032:Randomized Controlled Trials as Topic
keywords: Monte Carlo method; Statistics; bias; instrumental variable; randomized controlled trials; treatment effectiveness