Multivariate Meta-anaylsis of Diagnostic Tests

诊断测试的多变量荟萃分析

基本信息

批准号：
9170404
负责人：
Yong Chen
金额：
$ 3.68万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-09-01 至 2016-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9170404
关键词：
Multivariate Meta anaylsis Diagnostic Tests

项目摘要

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）从根本上依赖于对临床结果的准确评估。评估工具的数量越来越多，成本的快速升级产生了对通过荟萃分析中临床实践中对诊断测试进行科学严格比较的越来越多的需求。与随机临床试验的传统荟萃分析相比，诊断测试的荟萃分析提出了许多其他统计挑战。特别是，诊断准确性不能通过单个措施充分总结；通常使用配对度量，例如，最普遍的灵敏度（SE）和特异性（SP），或者是正面和负预测值；并且两个配对措施通常相关。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，参考标准也遇到了测量误差。为了响应PAR-10-168，该提案的总体目标是开发诊断测试的最先进的多元元分析方法，并将其集成到公开可用的，易于使用的R软件包元件中，以增强一致性，适用性和普遍性。在此提案中，我们将重点介绍：（1）开发多元方法和软件，以有效地检测和调整出版偏差和其他样本量效应，并在诊断测试的荟萃分析中；（2）开发网络荟萃分析框架和软件，以同时比较多个诊断测试。我们建议通过实际数据应用和模拟对这些方法的优势和缺点进行经验评估。所提出的统计方法将广泛适用于比较诊断测试的荟萃分析。它将通过促进诊断来改善公共卫生各种癌症，心血管，传染性和其他疾病。完成这两个目标的完成将直接通过使用Winbugs和R统计语言在用户友好的软件中实施的最先进的方法直接受益于CER计划，这些方法将自由地公开提供。