Improving Meta-Analysis of Diagnostic Test Studies

描述(由申请人提供)： 诊断测试在操作上被定义为使用来自测试或程序的信息，相对于患者病史或临床直觉提供的信息，可以提高正确诊断疾病或疾病严重程度的机会，在医疗保健的重点和成本中占据越来越大的份额。随着医疗技术进步的速度加快，关于新程序诊断能力的研究继续激增。由于测试新诊断技术影响的临床试验数量远远少于评估新医疗方法的数量，大多数诊断测试研究都会相对于黄金标准来检验新技术的准确性。不可避免的是，这些程序要在多项研究中进行测试，可能会出现相互矛盾的结果。由于研究的观察性，以及经常缺乏测试解释的标准，主观判断可能会导致研究之间的异质性。虽然异质性在治疗效果研究的荟萃分析中受到越来越多的重视，但在诊断试验的荟萃分析中却没有得到充分的考虑。这些仍然依赖于只解释测试性能的某些方面的衡量标准和固定效果模型，这些模型意味着在不同类型的环境中具有不同数量的操作专业知识的不同类型的患者具有统一的性能。此外，许多最常用的分析方法只适用于特殊类型的研究数据总结，特别是关于单一敏感性和特异值的报告，尽管这种总结可能是对原始结论的简化。目前尚不清楚这种用法是因为需要标准化报告的不一致的性能衡量标准，还是因为没有意识到需要更复杂的分析，还是因为为了分析方便而简化测试报告的研究方案。分析技术还依赖于大样本量的有效性；小样本量的性能尚未得到评估。因此，申请人建议对诊断测试文献进行研究，以评估正在进行的元分析的质量和充分性，以评估测试的准确性，检查异质性对结论的影响，并确定分析此类数据的最佳方法。申请人将首先评估和比较已发表的诊断测试Meta分析中用于分析测试准确性数据的不同模型：1)从Medline自1990年以来发表的所有诊断测试的Meta分析中提取结果和协变量信息；2)开发和扩展贝叶斯多水平随机效应回归模型，并开发和修改软件以实现用于分析诊断测试数据的新的和现有的模型；以及3)应用和比较在收集的Meta分析基础上开发的模型。接下来，研究人员将通过以下几个方面评估Meta分析在诊断测试研究中总结信息的情况：4)收集30个Meta分析中包含的所有研究；5)将从研究中提取的结果和协变量与30个Meta分析中报告的结果和协变量进行比较；以及6)使用从研究中收集的信息更新30个Meta分析，并确定结论的任何变化。这些任务将有助于制定改进诊断测试荟萃分析的建议。

DESCRIPTION (provided by applicant): Diagnostic testing, operatively defined as the use of information from a test or procedure that can improve the chances of correct diagnosis of a disease or disease severity relative to the information provided by patient history or clinical intuition, occupies an increasing share of the focus and costs of healthcare. As the rate of improvements in medical technology accelerates, studies of the diagnostic capability of new procedures continue to proliferate. Because the number of clinical trials to test the impact of new diagnostic technology is far less than the number to evaluate new medical treatments, most diagnostic test studies examine the accuracy of the new technology relative to a gold standard. Inevitably, procedures are tested in multiple studies and conflicting results may arise. Because of the observational nature of the studies and the frequent lack of standards for test interpretation, subjective judgement may introduce heterogeneity between studies. While heterogeneity has received increasing emphasis in the meta-analysis of treatment efficacy studies, it has been inadequately considered in meta-analyses of diagnostic tests. These continue to rely on measures that explain only certain aspects of test performance and fixed effects models that imply uniform performance across different types of patients in different types of settings with different amounts of operational expertise. Moreover, many of the analytic methods that are most frequently used apply only to special types of study data summaries, particularly reports of single sensitivity and specificity values, although such summaries may be simplifications of the original conclusions. It is unclear whether this usage arises from a need to standardize inconsistently reported measures of performance, from a failure to appreciate the need for more sophisticated analysis or from study protocols that simplify test reporting for analytic convenience. The analytic techniques also rely on large sample sizes for their validity; performance with small sample sizes has not been evaluated. As a result, the applicant proposes to undertake a study of the diagnostic test literature to assess the quality and adequacy of the meta-analyses being performed to evaluate test accuracy, to examine the impact of heterogeneity on conclusions and to determine the best methods for analyzing such data. The applicant will first evaluate and compare different models for analyzing test accuracy data in published diagnostic test meta-analyses by: 1) abstracting outcome and covariate information from all meta-analyses of diagnostic tests listed in Medline as published since 1990; 2) developing and extending Bayesian multilevel random effects regression models and developing and modifying software to implement new and existing models for analyzing diagnostic test data; and 3) applying and comparing models developed on the collected meta-analyses. Next investigators will evaluate how well the meta-analyses summarize the information in the diagnostic test studies they comprise by: 4) collecting all studies included in 30 of the meta-analyses; 5) comparing the outcomes and covariates extracted from the studies with those reported in the 30 meta-analyses; and 6) updating the 30 meta-analyses using information collected from the studies and determining any changes in conclusions. These tasks will contribute to the development of recommendations for improving diagnostic test meta-analyses.