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.

Public Health Relevance

The overall goal of this project is to develop statistical methods and related software for multivariate meta- analysis of diagnostic tests. The proposed statistical methodology will be broadly applicable to the statistical analysis and interpretation o complex meta-analyses of diagnostic test studies. It will improve comparative effectiveness research and public health by facilitating the diagnosis and treatment of cancer, cardiovascular, infectious and other diseases.

National Institute of Health (NIH)
Agency for Healthcare Research and Quality (AHRQ)
Small Research Grants (R03)
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Health Care Quality and Effectiveness Research (HQER)
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Kato, Elisabeth
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University of Texas Health Science Center Houston
Biostatistics & Other Math Sci
Schools of Public Health
United States
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Chen, Yong; Liu, Yulun; Chu, Haitao et al. (2017) A simple and robust method for multivariate meta-analysis of diagnostic test accuracy. Stat Med 36:105-121
Hong, Chuan; Ning, Yang; Wei, Peng et al. (2017) A semiparametric model for vQTL mapping. Biometrics 73:571-581
Chen, Yong; Liu, Yulun; Ning, Jing et al. (2017) A composite likelihood method for bivariate meta-analysis in diagnostic systematic reviews. Stat Methods Med Res 26:914-930
Duan, Rui; Cao, Ming; Wu, Yonghui et al. (2016) An Empirical Study for Impacts of Measurement Errors on EHR based Association Studies. AMIA Annu Symp Proc 2016:1764-1773
Chen, Yong; Hong, Chuan; Ning, Yang et al. (2016) Meta-analysis of studies with bivariate binary outcomes: a marginal beta-binomial model approach. Stat Med 35:21-40
Chen, Yong; Cai, Yi; Hong, Chuan et al. (2016) Inference for correlated effect sizes using multiple univariate meta-analyses. Stat Med 35:1405-22
Liu, Yulun; Chen, Yong; Scheet, Paul (2016) A meta-analytic framework for detection of genetic interactions. Genet Epidemiol 40:534-543
Ma, Xiaoye; Chen, Yong; Cole, Stephen R et al. (2016) A hybrid Bayesian hierarchical model combining cohort and case-control studies for meta-analysis of diagnostic tests: Accounting for partial verification bias. Stat Methods Med Res 25:3015-3037
Chen, Yong; Hong, Chuan; Riley, Richard D (2015) An alternative pseudolikelihood method for multivariate random-effects meta-analysis. Stat Med 34:361-80
Liu, Yulun; Chen, Yong; Chu, Haitao (2015) A unification of models for meta-analysis of diagnostic accuracy studies without a gold standard. Biometrics 71:538-47

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