中风病因分类的可靠性及其对试验样本量的影响：系统评价、荟萃分析和统计建模

Stroke aetiological classification reliability and effect on trial sample size: systematic review, meta-analysis and statistical modelling.

作者信息

Abdul-Rahim Azmil H, Dickie David Alexander, Selvarajah Johann R, Lees Kennedy R, Quinn Terence J

机构信息

Institute of Neuroscience and Psychology, University of Glasgow, Room 0.07, Office Block, Queen Elizabeth University Hospital, G51 4TF, Glasgow, UK.

Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK.

出版信息

Trials. 2019 Feb 8;20(1):107. doi: 10.1186/s13063-019-3222-x.

DOI:10.1186/s13063-019-3222-x

PMID:30736833

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6368715/

Abstract

BACKGROUND

Inter-observer variability in stroke aetiological classification may have an effect on trial power and estimation of treatment effect. We modelled the effect of misclassification on required sample size in a hypothetical cardioembolic (CE) stroke trial.

METHODS

We performed a systematic review to quantify the reliability (inter-observer variability) of various stroke aetiological classification systems. We then modelled the effect of this misclassification in a hypothetical trial of anticoagulant in CE stroke contaminated by patients with non-cardioembolic (non-CE) stroke aetiology. Rates of misclassification were based on the summary reliability estimates from our systematic review. We randomly sampled data from previous acute trials in CE and non-CE participants, using the Virtual International Stroke Trials Archive. We used bootstrapping to model the effect of varying misclassification rates on sample size required to detect a between-group treatment effect across 5000 permutations. We described outcomes in terms of survival and stroke recurrence censored at 90 days.

RESULTS

From 4655 titles, we found 14 articles describing three stroke classification systems. The inter-observer reliability of the classification systems varied from 'fair' to 'very good' and suggested misclassification rates of 5% and 20% for our modelling. The hypothetical trial, with 80% power and alpha 0.05, was able to show a difference in survival between anticoagulant and antiplatelet in CE with a sample size of 198 in both trial arms. Contamination of both arms with 5% misclassified participants inflated the required sample size to 237 and with 20% misclassification inflated the required sample size to 352, for equivalent trial power. For an outcome of stroke recurrence using the same data, base-case estimated sample size for 80% power and alpha 0.05 was n = 502 in each arm, increasing to 605 at 5% contamination and 973 at 20% contamination.

CONCLUSIONS

Stroke aetiological classification systems suffer from inter-observer variability, and the resulting misclassification may limit trial power.

TRIAL REGISTRATION

Protocol available at reviewregistry540 .

摘要

背景

中风病因分类中观察者间的变异性可能会对试验效能和治疗效果评估产生影响。我们模拟了在一个假设的心脏栓塞性（CE）中风试验中错误分类对所需样本量的影响。

方法

我们进行了一项系统综述，以量化各种中风病因分类系统的可靠性（观察者间变异性）。然后，我们在一个假设的CE中风抗凝试验中模拟了这种错误分类的影响，该试验受到非心脏栓塞性（非CE）中风病因患者的污染。错误分类率基于我们系统综述的汇总可靠性估计。我们使用虚拟国际中风试验档案库，从之前CE和非CE参与者的急性试验中随机抽样数据。我们使用自抽样法来模拟不同错误分类率对在5000次排列中检测组间治疗效果所需样本量的影响。我们根据90天时的生存情况和中风复发情况来描述结果。

结果

从4655个标题中，我们找到了14篇描述三种中风分类系统的文章。分类系统的观察者间可靠性从“一般”到“非常好”不等，并为我们的模型建议了5%和20%的错误分类率。在假设试验中，效能为80%且α为0.05时，两个试验组样本量均为198时，能够显示出CE中风中抗凝治疗和抗血小板治疗在生存方面的差异。两个试验组中混入5%的错误分类参与者会使所需样本量增加到237，混入20%的错误分类参与者会使所需样本量增加到352，以获得相同的试验效能。对于使用相同数据的中风复发结果，在效能为80%且α为0.05时，每个试验组的基础估计样本量为n = 502，在5%的污染率下增加到605，在20%的污染率下增加到973。