Improving Meta-Analysis of Diagnostic Test Studies

描述（由申请人提供）： 诊断测试，操作上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，占据了医疗保健的重点和成本的越来越大的份额。 随着医疗技术进步的速度加快，对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，因此大多数诊断测试研究都会检查新技术相对于金标准的准确性。 不可避免的是，程序在多项研究中进行测试，可能会出现相互矛盾的结果。 由于研究的观察性质和经常缺乏测试解释的标准，主观判断可能会在研究之间引入异质性。 虽然异质性在治疗有效性研究的荟萃分析中越来越受到重视，但在诊断试验的荟萃分析中没有充分考虑异质性。 这些仍然依赖于仅解释测试性能的某些方面的测量和固定效应模型，该模型意味着在不同类型的设置中具有不同操作专业知识的不同类型的患者之间的性能一致。 此外，许多最常用的分析方法仅适用于特殊类型的研究数据总结，特别是单一灵敏度和特异性值的报告，尽管此类总结可能是原始结论的简化。 目前尚不清楚这种用法是否源于需要标准化不一致的报告性能指标，未能认识到需要更复杂的分析或简化测试报告以便于分析的研究方案。 分析技术的有效性也依赖于大样本量;小样本量的性能尚未评估。 因此，申请人建议对诊断试验文献进行研究，以评估为评价试验准确性而进行的荟萃分析的质量和充分性，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。 申请人将首先通过以下方式评价和比较用于分析已发表的诊断试验荟萃分析中的试验准确性数据的不同模型：1）从自1990年以来发表的Medline中列出的所有诊断试验荟萃分析中提取结局和协变量信息;（二）开发和扩展贝叶斯多层次随机效应回归模型，开发和修改软件，以实现新的和现有的模型，用于分析诊断测试数据;应用和比较基于所收集的荟萃分析开发的模型。 接下来，研究者将通过以下方式评估荟萃分析对诊断试验研究信息的总结情况：4）收集30项荟萃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项荟萃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项荟萃分析，并确定结论中的任何变化。 这些任务将有助于制定改进诊断试验荟萃分析的建议。

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.