Adjusting for bias in unblinded randomized controlled trials

作者：Schmidt A. F.^*; Groenwold R. H. H.

来源：Statistical Methods in Medical Research, 2018, 27(8): 2413-2427.

DOI：10.1177/0962280216680652

• Statistics
• randomized controlled trials
• Monte Carlo method
• bias
• treatment effectiveness
• instrumental variable

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.

• 出版日期2018-8