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A composite likelihood method for bivariate meta-analysis in diagnostic systematic reviews.

ABSTRACT: Diagnostic systematic review is a vital step in the evaluation of diagnostic technologies. In many applications, it involves pooling pairs of sensitivity and specificity of a dichotomized diagnostic test from multiple studies. We propose a composite likelihood (CL) method for bivariate meta-analysis in diagnostic systematic reviews. This method provides an alternative way to make inference on diagnostic measures such as sensitivity, specificity, likelihood ratios, and diagnostic odds ratio. Its main advantages over the standard likelihood method are the avoidance of the nonconvergence problem, which is nontrivial when the number of studies is relatively small, the computational simplicity, and some robustness to model misspecifications. Simulation studies show that the CL method maintains high relative efficiency compared to that of the standard likelihood method. We illustrate our method in a diagnostic review of the performance of contemporary diagnostic imaging technologies for detecting metastases in patients with melanoma.

SUBMITTER: Chen Y

PROVIDER: S-EPMC4466215 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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A composite likelihood method for bivariate meta-analysis in diagnostic systematic reviews.

Chen Yong Y Liu Yulun Y Ning Jing J Nie Lei L Zhu Hongjian H Chu Haitao H

Statistical methods in medical research 20141214 2

Diagnostic systematic review is a vital step in the evaluation of diagnostic technologies. In many applications, it involves pooling pairs of sensitivity and specificity of a dichotomized diagnostic test from multiple studies. We propose a composite likelihood (CL) method for bivariate meta-analysis in diagnostic systematic reviews. This method provides an alternative way to make inference on diagnostic measures such as sensitivity, specificity, likelihood ratios, and diagnostic odds ratio. Its ...[more]

PMID: 25512146

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Project description:Evidence-based decision making is a hallmark of effective veterinary clinical practice. Scoping reviews, systematic reviews, and meta-analyses all are methods intended to provide transparent and replicable ways of summarizing a body of research to address an important clinical or public health issue. As these methods increasingly are being used by researchers and read by practitioners, it is important to understand the distinction between these techniques and to understand what research questions they can, and cannot, address. This review provides an overview of scoping reviews, systematic reviews, and meta-analysis, including a discussion of the method and uses. A sample dataset and coding to conduct a simple meta-analysis in the statistical program R also are provided. Scoping reviews are a descriptive approach, designed to chart the literature around a particular topic. The approach involves an extensive literature search, following by a structured mapping, or charting, of the literature. The results of scoping reviews can help to inform future research by identifying gaps in the existing literature and also can be used to identify areas where there may be a sufficient depth of literature to warrant a systematic review. Systematic reviews are intended to address a specific question by identifying and summarizing all of the available research that has addressed the review question. Questions types that can be addressed by a systematic review include prevalence/incidence questions, and questions related to etiology, intervention efficacy, and diagnostic test accuracy. The systematic review process follows structured steps with multiple reviewers working in parallel to reduce the potential for bias. An extensive literature search is undertaken and, for each relevant study identified by the search, a formal extraction of data, including the effect size, and assessment of the risk of bias is performed. The results from multiple studies can be combined using meta-analysis. Meta-analysis provides a summary effect size, and allows heterogeneity of effect among studies to be quantified and explored. These evidence synthesis approaches can provide scientific input to evidence-based clinical decision-making for veterinarians and regulatory bodies, and also can be useful for identifying gaps in the literature to enhance the efficiency of future research in a topic area.

Project description:Multivariate meta-analysis, which jointly analyzes multiple and possibly correlated outcomes in a single analysis, is becoming increasingly popular in recent years. An attractive feature of the multivariate meta-analysis is its ability to account for the dependence between multiple estimates from the same study. However, standard inference procedures for multivariate meta-analysis require the knowledge of within-study correlations, which are usually unavailable. This limits standard inference approaches in practice. Riley et al proposed a working model and an overall synthesis correlation parameter to account for the marginal correlation between outcomes, where the only data needed are those required for a separate univariate random-effects meta-analysis. As within-study correlations are not required, the Riley method is applicable to a wide variety of evidence synthesis situations. However, the standard variance estimator of the Riley method is not entirely correct under many important settings. As a consequence, the coverage of a function of pooled estimates may not reach the nominal level even when the number of studies in the multivariate meta-analysis is large. In this paper, we improve the Riley method by proposing a robust variance estimator, which is asymptotically correct even when the model is misspecified (ie, when the likelihood function is incorrect). Simulation studies of a bivariate meta-analysis, in a variety of settings, show a function of pooled estimates has improved performance when using the proposed robust variance estimator. In terms of individual pooled estimates themselves, the standard variance estimator and robust variance estimator give similar results to the original method, with appropriate coverage. The proposed robust variance estimator performs well when the number of studies is relatively large. Therefore, we recommend the use of the robust method for meta-analyses with a relatively large number of studies (eg, m?50). When the sample size is relatively small, we recommend the use of the robust method under the working independence assumption. We illustrate the proposed method through 2 meta-analyses.

Dataset Information

A composite likelihood method for bivariate meta-analysis in diagnostic systematic reviews.

Publications

A composite likelihood method for bivariate meta-analysis in diagnostic systematic reviews.

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