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.
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.
|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|
|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|
|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|
|Liu, Yulun; Chen, Yong; Scheet, Paul (2016) A meta-analytic framework for detection of genetic interactions. Genet Epidemiol 40:534-543|
|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|
|Chen, Yong; Liu, Yulun; Ning, Jing et al. (2015) A hybrid model for combining case-control and cohort studies in systematic reviews of diagnostic tests. J R Stat Soc Ser C Appl Stat 64:469-489|