Practical methods for ordinal data meta-analysis in stroke

卒中有序数据荟萃分析的实用方法

• 批准号: G0901333/1
• 负责人: Steff Lewis
• 金额: $ 14.8万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2010
• 资助国家: 英国
• 起止时间: 2010 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=G0901333%2F1
• 关键词: Practical; methods; ordinal; data; meta

When randomised trials are conducted to see whether treatments work, these frequently have outcome measures that are ordinal scales (such as high, moderate, low or no pain), rather than binary measures (such as dead or alive) or continuous measures (such as an exact measure of blood pressure). There are too many trials performed for anyone to read all of the reports of their results, so the results of groups of trials are summarised using systematic reviews. In these reviews, ordinal scales are often analysed as if they were binary ? so high, medium and low levels of pain might be combined together and the pain scale analysed as any pain versus no pain. This is equivalent to throwing away 30% of the data. However, there are better ways of analysing ordinal scales. We wish to review this methodology. We then wish to investigate how practical each of the methods is by assessing how often sufficient data are presented to use the method, how often the available data fulfil any statistical rules that are needed for the methods to work, how easy to understand the results are, and how much detail they show of the way the treatment effect operates. If we can utilise the extra statistical power held in the ordinal scales, then guideline authors, clinicians and healthcare users will benefit, as they will be able to learn whether treatments are beneficial more quickly, using fewer patients, fewer trials, and less money.We aim to do this work using stroke as an example. Ordinal outcome scales are very common in stroke trials, and there are several scales that are consistently used in many trials. Thus there will be a substantial amount of data for us to use, on outcomes that it makes clinical sense to combine. In addition, the analysis of ordinal scales in stroke trials provides some interesting examples of treatment effects that work in different ways (?kill or cure? treatments as compared with treatments that improve all patients? outcomes systematically), and we wish to see whether ordinal methods can highlight the differences between these. We have easy access to all the original reports of trials in stroke from a local trials register. This register is incredibly comprehensive, and structured so that we can easily find all the information relating to any one trial. Although we will use stroke data, our findings will be usable in other disease areas.

当进行随机试验以观察治疗是否有效时，这些结果测量通常是顺序量表（如高、中、低或无疼痛），而不是二元测量（如死亡或存活）或连续测量（如精确测量血压）。进行了太多的试验，任何人都无法阅读所有的结果报告，因此使用系统评价对试验组的结果进行总结。在这些评论中，序数尺度通常被当作二进制来分析。因此，高，中，低水平的疼痛可能会被结合在一起，疼痛等级被分析为有疼痛与无疼痛。这相当于丢掉了30%的数据。然而，有更好的方法来分析有序尺度。我们希望审查这一方法。然后，我们希望通过评估每一种方法的实用性，评估使用该方法所需的足够数据出现的频率，评估可用数据满足方法工作所需的统计规则的频率，评估结果是否容易理解，以及评估结果显示治疗效果运作方式的细节程度。如果我们能够利用序数量表所拥有的额外统计能力，那么指南作者、临床医生和医疗保健用户将受益，因为他们将能够更快地了解治疗是否有益，使用更少的患者、更少的试验和更少的资金。我们打算以中风为例来做这项工作。顺序结果量表在中风试验中很常见，有几个量表在许多试验中一直使用。因此，将会有大量的数据供我们使用，这些数据的结果在临床上是有意义的。此外，对中风试验中有序量表的分析提供了一些有趣的例子，说明治疗效果以不同的方式起作用(？杀死还是治愈？将治疗与改善所有患者的治疗进行比较？结果系统地)，我们希望看到顺序方法是否可以突出这些之间的差异。我们可以很容易地从当地的试验登记处获得所有中风试验的原始报告。这个登记册非常全面，结构合理，因此我们可以很容易地找到与任何一个试验有关的所有信息。虽然我们将使用中风数据，但我们的发现将可用于其他疾病领域。