Statistical Methods and Software for Meta-analysis of Diagnostic Tests

诊断试验Meta分析的统计方法和软件 主要研究员：朱海涛，医学博士，博士。 摘要 比较有效性研究从根本上依赖于对临床结果的准确评估。 越来越多的评估工具，以及费用的迅速上升，产生了 在临床实践中，越来越需要对诊断测试进行科学严格的比较。Meta分析 与传统的荟萃分析相比，诊断测试带来了许多额外的统计学挑战 临床对照试验的荟萃分析等应用。特别是，诊断的准确性不能 通常使用两个衡量标准，最常见的是敏感度和敏感度 特异度，或者正负似然比，两者中的任何一种都是相关的。此外， 诊断准确性参数可能取决于疾病流行率。为回应AHRQ PAR-10-168， 这项提议的总体目标是开发尖端的多元统计方法，并将它们整合在一起 转变为公开可用的、易于使用的软件，以增强 比较诊断试验研究的荟萃分析。在这项提议中，我们假设黄金标准 存在；诊断测试的荟萃分析中不完美的黄金标准偏差的问题是未来的主题 研究。具体地说，我们将重点开发统计方法和相关软件，用于：(1)Meta- 当一些研究使用病例对照设计时，对疾病患病率的诊断试验的分析 一些研究使用队列设计，这在实践中很常见，但方法论上的分支从未 已得到解决；(2)纠正由于抽样偏倚而对诊断测试进行荟萃分析所产生的验证偏差 其中一些人正在接受黄金标准的检验，这可能导致对准确性参数的偏向估计 如果丢失的数据和验证偏差没有得到适当处理，则包括敏感性和特异性。 我们建议通过REAL对这些方法的优点和缺点进行实证评估 数据应用和模拟。拟议的统计方法将广泛适用于元数据 分析比较诊断测试。它将通过促进对各种疾病的诊断来改善公众健康 癌症、心血管疾病、传染病和其他疾病。这两个目标的实现将直接惠及 AHRQ通过提供实施的最先进方法来比较有效性研究计划 使用WinBUGS和R统计语言的用户友好软件，将免费提供给 公开的。

Statistical Methods and Software for Meta-analysis of Diagnostic Tests Principal Investigator: Haitao Chu, M.D., Ph.D. Summary Comparative effectiveness research relies fundamentally on accurate assessment of clinical outcomes. The growing number of assessment instruments, as well as the rapid escalation in the cost has generated the increasing need for scientifically rigorous comparisons of the diagnostic tests in clinical practice. Meta-analysis of diagnostic tests presents many additional statistical challenges compared to traditional meta-analysis applications such as meta-analysis of controlled clinical trials. In particular, diagnostic accuracy cannot be adequately summarized by one measure; two measures are typically used, most often sensitivity and specificity, or alternatively positive and negative likelihood ratios, and either two are correlated. Furthermore, diagnostic accuracy parameters may depend on disease prevalence. In response to AHRQ PAR-10-168, the overall goal of this proposal is to develop cutting-edge multivariate statistical methods, and to integrate them into publicly available, easy-to-use software to enhance the consistency, applicability, and generalizability of the meta-analysis of comparative diagnostic test studies. In this proposal, we assume that a gold standard exists; the problem of imperfect gold standard bias in a meta-analysis of diagnostic tests is a topic for future research. Specifically, we will focus on developing statistical methods and related software for: (1) Meta- analysis of diagnostic tests accounting for disease prevalence when some studies use case-control design and some studies use cohort design, which is common in practice but methodological ramifications have never been addressed; (2) Correcting verification bias from meta-analysis of diagnostic tests due to biased sampling of whom is being tested by the gold standard, which can lead to biased estimation of accuracy parameters including sensitivities and specificities if the missing data and verification bias are not appropriately handled. We propose to perform empirical assessment of the strengths and weaknesses of these methods through real data applications and simulations. The proposed statistical methodology will be broadly applicable to the meta- analysis comparing diagnostic tests. It will improve public health by facilitating the diagnosis of various cancers, cardiovascular, infectious and other diseases. Completion of these two aims will directly benefit the comparative effectiveness research program at AHRQ by providing state-of-the art methods implemented in user-friendly software using WinBUGS and R statistical languages that will be made freely available to the public.