Clarke M J, Stewart L A
Clinical Trial Service Unit, Radcliffe Infirmary, Oxford, UK.
J Eval Clin Pract. 1995 Nov;1(2):119-26. doi: 10.1111/j.1365-2753.1995.tb00017.x.
If the relative effectiveness of different treatments that might be used in clinical practice is to be evaluated reliably, it is very important that the evaluation is carried out in an appropriate manner. This is especially true where the differences between treatments are expected to be moderate, and so easily obscured by the play of chance or systematic bias. Although such differences are often of considerable clinical importance, they can be difficult to assess and require a large amount of randomized evidence. This evidence can be obtained through prospective randomized controlled trials, meta-analysis of results from past randomized trials, or ideally a combination of the two, with prospective trials contributing to future meta-analyses. Whichever technique is adopted, all possible biases must be minimized through the collection of as much randomized evidence as possible. In meta-analyses, this is best achieved by ensuring that all relevant trials, and all randomized participants in these trials, are included in the analysis. The gold standard for this might be a meta-analysis of individual patient data, in which details for each participant in every trial are collected and analysed centrally. This approach requires considerable time and effort. However, it will add to the analyses that can be performed and will remove many of the problems associated with a reliance on published data alone and some of the problems that can arise from the use of aggregate data. This paper sets out some of the reasons for this and some of the techniques used for individual patient data-based meta-analysis.
如果要可靠地评估临床实践中可能使用的不同治疗方法的相对有效性，以适当的方式进行评估非常重要。在预期治疗方法之间的差异适中，因此很容易被随机因素或系统偏差掩盖的情况下尤其如此。尽管这些差异通常具有相当大的临床重要性，但它们可能难以评估，并且需要大量的随机证据。这种证据可以通过前瞻性随机对照试验、对过去随机试验结果的荟萃分析，或者理想情况下两者结合来获得，前瞻性试验为未来的荟萃分析做出贡献。无论采用哪种技术，都必须通过收集尽可能多的随机证据来尽量减少所有可能的偏差。在荟萃分析中，最好通过确保所有相关试验以及这些试验中的所有随机参与者都纳入分析来实现这一点。对此的金标准可能是个体患者数据的荟萃分析，其中收集每个试验中每个参与者的详细信息并进行集中分析。这种方法需要大量的时间和精力。然而，它将增加可进行的分析，并消除许多仅依赖已发表数据所带来的问题以及使用汇总数据可能产生的一些问题。本文阐述了其中的一些原因以及用于基于个体患者数据的荟萃分析的一些技术。
