Kahan Brennan C, Feagan Brian, Jairath Vipul
Pragmatic Clinical Trials Unit, Queen Mary University of London, 58 Turner St, London, E1 2AB, UK.
Robarts Clinical Trials, London, ON, Canada.
Trials. 2017 Jun 8;18(1):266. doi: 10.1186/s13063-017-1995-3.
Incorrect classification of outcomes in clinical trials can lead to biased estimates of treatment effect and reduced power. Ensuring appropriate adjudication methods to minimize outcome misclassification is therefore essential. While there are many reported adjudication approaches, there is little consensus over which approach is best.
Under the assumption of non-differential assessment (i.e. that misclassification rates are the same in each treatment arm, as would typically be the case when outcome assessors are blinded), we use simulation and theoretical results to address four different questions about outcome adjudication: (a) How many assessors should be used? (b) When is it better to use onsite or central assessment? (c) Should central assessors adjudicate all outcomes, or only suspected events? (d) Should central assessment with multiple assessors be done independently or through group consensus?
No one adjudication approach performs optimally in all settings. The optimal approach depends on the misclassification rates of site and central assessors, and the correlation between assessors. We found: (a) there will generally be little incremental benefit to using more than three assessors and, for outcomes with very high correlation between assessors, using one assessor is sufficient; (b) when choosing between site and central assessors, the assessor with the smallest misclassification rate should be chosen; when these rates are unknown, a combination of one site assessor and two central assessors will provide good results across a range of scenarios; (c) having central assessors adjudicate only suspected events will typically increase bias, and should be avoided, unless the threshold for sending outcomes for central assessment is extremely low; (d) central assessors can adjudicate either independently or in a group, and the preferred option should be dictated by whichever is expected to have the lowest misclassification rate.
Outcome adjudication is of critical importance to ensure validity of trial results, although no one approach is optimal across all settings. Investigators should choose the best strategy based on the specific characteristics of their trial. Regardless of the adjudication strategy chosen, assessors should be qualified and receive appropriate training.
临床试验中结果分类错误会导致治疗效果估计出现偏差并降低检验效能。因此,确保采用适当的判定方法以尽量减少结果分类错误至关重要。虽然有许多已报道的判定方法,但对于哪种方法最佳几乎没有共识。
在非差异性评估的假设下(即每个治疗组的错误分类率相同,当结果评估者处于盲态时通常就是这种情况),我们使用模拟和理论结果来解决关于结果判定的四个不同问题:(a)应该使用多少评估者?(b)何时采用现场评估或集中评估更好?(c)集中评估者应该判定所有结果,还是仅判定可疑事件?(d)多名评估者进行的集中评估应该独立进行还是通过小组共识进行?
没有一种判定方法在所有情况下都能达到最优效果。最优方法取决于现场评估者和集中评估者的错误分类率以及评估者之间的相关性。我们发现:(a)使用超过三名评估者通常几乎没有额外益处,对于评估者之间相关性非常高的结果,使用一名评估者就足够了;(b)在现场评估者和集中评估者之间进行选择时,应选择错误分类率最小的评估者;当这些比率未知时,一名现场评估者和两名集中评估者的组合在一系列情况下都将产生良好结果;(c)让集中评估者仅判定可疑事件通常会增加偏差,应予以避免,除非将结果送交集中评估的阈值极低;(d)集中评估者可以独立或分组进行判定,首选选项应由预期错误分类率最低的方式决定。
结果判定对于确保试验结果的有效性至关重要,尽管没有一种方法在所有情况下都是最优的。研究者应根据其试验的具体特征选择最佳策略。无论选择何种判定策略,评估者都应具备资质并接受适当培训。