Multivariate Meta-anaylsis of Diagnostic Tests

诊断测试的多变量荟萃分析

• 批准号: 8676516
• 负责人: Yong Chen
• 金额: $ 4.46万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-04-01 至 2016-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8676516
• 关键词: Multivariate; Meta; anaylsis; Diagnostic; Tests

DESCRIPTION (provided by applicant): Comparative effectiveness research (CER) 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 practic via meta-analysis. Meta-analysis of diagnostic tests presents many additional statistical challenges compared to traditional meta-analysis of randomized clinical trials. In particular, diagnostic accuracy cannot be adequately summarized by a single measure; paired measures are typically used, for example, most popularly sensitivity (Se) and specificity (Sp), or alternatively positive and negative predictive values; and either of the paired measures is typically correlated. Furthermore, those diagnostic accuracy measures may depend on disease prevalence and in many studies, the reference standard is also subject to measurement error. In response to PAR-10-168, the overall goal of this proposal is to develop cutting-edge multivariate meta- analysis methods of diagnostic tests, and to integrate them into publicly available, easy-to-use R software package meta to enhance the consistency, applicability, and generalizability. In this proposal, we will focus on: (1) developing multivariate methods and software to efficiently detect and adjust for publication bias and other sample size effects in meta-analysis of diagnostic tests; and (2) developing network meta-analysis framework and software to simultaneously compare multiple diagnostic tests. 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 CER program by providing state-of-the art methods implemented in user-friendly software using the WinBUGS and R statistical languages that will be made freely available to the public.

描述（由申请人提供）：比较有效性研究（CER）基本上依赖于对临床结局的准确评估。评估工具的数量不断增加，以及成本的快速上升，使得越来越需要通过荟萃分析对临床实践中的诊断测试进行科学严格的比较。与传统的随机临床试验荟萃分析相比，诊断试验的荟萃分析提出了许多额外的统计挑战。具体地，诊断准确性不能通过单个测量充分地总结;通常使用成对测量，例如，最普遍的灵敏度（Se）和特异性（Sp），或者备选地阳性和阴性预测值;并且成对测量中的任一个通常是相关的。此外，这些诊断准确性措施可能取决于疾病的患病率，在许多研究中，参考标准也会出现测量误差。 针对PAR-10-168，本提案的总体目标是开发诊断测试的尖端多元Meta分析方法，并将其集成到公开可用的、易于使用的R软件包Meta中，以增强一致性、适用性和通用性。在本研究中，我们将重点关注：（1）开发多变量方法和软件，以有效地检测和调整诊断试验荟萃分析中的发表偏倚和其他样本量效应;（2）开发网络荟萃分析框架和软件，以同时比较多个诊断试验。我们建议通过真实的数据应用和模拟对这些方法的优缺点进行实证评估。所提出的统计方法将广泛适用于比较诊断试验的荟萃分析。它将通过促进诊断 各种癌症、心血管疾病、传染病和其他疾病。这两个目标的完成将直接有利于CER计划，通过使用WinBUGS和R统计语言提供最先进的方法，这些方法将免费提供给公众。