Practical methods for ordinal data meta-analysis in stroke

当进行随机试验以观察治疗是否有效时，这些通常具有顺序尺度的结果测量（例如高，中等，低或无疼痛），而不是二元测量（例如死亡或存活）或连续测量（例如血压的精确测量）。有太多的试验进行了任何人阅读所有的报告，他们的结果，所以试验组的结果是总结使用系统评价。在这些评论中，有序尺度往往被分析，如果他们是二元的？因此，高、中、低程度的疼痛可能被组合在一起，并且疼痛量表被分析为任何疼痛与没有疼痛。这相当于扔掉了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，我们希望通过评估使用该方法所需的足够数据的频率，可用数据满足方法工作所需的任何统计规则的频率，结果的容易理解程度以及它们显示的治疗效果的方式的细节来研究每种方法的实用性。如果我们能够利用有序量表中的额外统计功效，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，更少的资金。我们的目标是以中风为例进行这项工作。顺序结局量表在卒中试验中非常常见，并且有几种量表在许多试验中一直使用。因此，将有大量的数据供我们使用，这些数据的结果对联合收割机的临床意义是有意义的。此外，中风试验中的顺序量表分析提供了一些有趣的例子，治疗效果的工作方式不同（？杀死还是治愈？与改善所有患者的治疗相比，治疗效果如何？结果系统），我们希望看到有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。该登记册非常全面，结构合理，因此我们可以轻松找到与任何一项试验相关的所有信息。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。

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.