A mixed effect model for bivariate meta‐analysis of diagnostic test accuracy studies using a copula representation of the random effects distribution

Nikoloulopoulos, Aristidis K.

doi:10.1002/sim.6595

Cited by 36 publications

(103 citation statements)

References 51 publications

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“…Preliminary approaches based on separate univariate meta-analyses for sensitivity and specificity of diagnostic tests, although still diffuse in medical investigations, have been successfully improved by more sophisticated solutions accounting for the correlation between the diagnostic test measures [2–4]. The literature, initially based on least squares regressions [5, 6], now spans hierarchical models [4, 7–9], bivariate copula distributions [10–12], bivariate mixture models [13, 14], nonparametric solutions [15]. In this paper we focus on the bivariate random-effects model [7, 8], as it is currently a well-established and recommended method for meta-analysis of diagnostic accuracy studies.…”

Section: Introductionmentioning

confidence: 99%

A double SIMEX approach for bivariate random-effects meta-analysis of diagnostic accuracy studies

Guolo

2017

BMC Med Res Methodol

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BackgroundBivariate random-effects models represent a widely accepted and recommended approach for meta-analysis of test accuracy studies. Standard likelihood methods routinely used for inference are prone to several drawbacks. Small sample size can give rise to unreliable inferential conclusions and convergence issues make the approach unappealing. This paper suggests a different methodology to address such difficulties.MethodsA SIMEX methodology is proposed. The method is a simulation-based technique originally developed as a correction strategy within the measurement error literature. It suits the meta-analysis framework as the diagnostic accuracy measures provided by each study are prone to measurement error. SIMEX can be straightforwardly adapted to cover different measurement error structures and to deal with covariates. The effortless implementation with standard software is an interesting feature of the method.ResultsExtensive simulation studies highlight the improvement provided by SIMEX over likelihood approach in terms of empirical coverage probabilities of confidence intervals under different scenarios, independently of the sample size and the values of the correlation between sensitivity and specificity. A remarkable amelioration is obtained in case of deviations from the normality assumption for the random-effects distribution. From a computational point of view, the application of SIMEX is shown to be neither involved nor subject to the convergence issues affecting likelihood-based alternatives. Application of the method to a diagnostic review of the performance of transesophageal echocardiography for assessing ascending aorta atherosclerosis enables overcoming limitations of the likelihood procedure.ConclusionsThe SIMEX methodology represents an interesting alternative to likelihood-based procedures for inference in meta-analysis of diagnostic accuracy studies. The approach can provide more accurate inferential conclusions, while avoiding convergence failure and numerical instabilities. The application of the method in the R programming language is possible through the code which is made available and illustrated using the real data example.Electronic supplementary materialThe online version of this article (doi:10.1186/s12874-016-0284-2) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

A double SIMEX approach for bivariate random-effects meta-analysis of diagnostic accuracy studies

Guolo

2017

BMC Med Res Methodol

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“…Vuong () has shown that asymptotically under the null hypothesis H 0 : Δ = 0, that is, Models 1 and 2 have the same parametric densities f (1) and f (2) ,

z_{0} = \sqrt{N} \overset{true‾}{D} / s \overset{H_{0}}{\sim} N (0, 1),

where

s^{2} = \frac{1}{N - 1} {false\sum}_{i = 1}^{N} {((), D_{i} - \overset{D}{true})}^{2}

. For more details we refer the interested reader to Joe () and Nikoloulopoulos ().…”

Section: Application To the German Socio‐economic Panelmentioning

confidence: 99%

Coupling Couples With Copulas: Analysis of Assortative Matching on Risk Attitude

Nikoloulopoulos

Moffatt

2018

Economic Inquiry

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We investigate patterns of assortative matching on risk attitude, using self‐reported (ordinal) data on risk attitudes for males and females within married couples, from the German Socio‐Economic Panel over the period 2004–2012. We apply a novel copula‐based bivariate panel ordinal model. Estimation is in two steps: first, a copula‐based Markov model is used to relate the marginal distribution of the response in different time periods, separately for males and females; second, another copula is used to couple the males' and females' conditional (on the past) distributions. We find positive dependence, both in the middle of the distribution, and in the joint tails, and we interpret this as positive assortative matching (PAM). Hence we reject standard assortative matching theories based on risk‐sharing assumptions, and favor models based on alternative assumptions such as the ability of agents to control income risk. We also find evidence of “assimilation”; that is, PAM appearing to increase with years of marriage. (JEL C33, C51, D81)

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“…An overview of suitable parametric families of copulas for mixed models for diagnostic test accuracy studies was recently given by Nikoloulopoulos (2015). In the following section, a short description of well-known copulas implemented in the package is given.…”

Section: The Hierarchical Modelmentioning

confidence: 99%

“…It is generally known that full parametric specification of a (hierarchical) model, including the specification of an (existing) correlation structure, increases the efficiency of the estimation of the parameters, resulting in smaller standard errors. The use of copula based mixed models within the frequentist framework for meta-analysis of diagnostic test accuracy was recently introduced by Nikoloulopoulos (2015) who evaluated the joint density numerically.…”

Section: Introductionmentioning

confidence: 99%

CopulaDTA: An R Package for Copula-Based Bivariate Beta-Binomial Models for Diagnostic Test Accuracy Studies in a Bayesian Framework

Nyaga¹,

Arbyn²,

Aerts³

2017

J. Stat. Soft.

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The current statistical procedures implemented in statistical software packages for pooling of diagnostic test accuracy data include HSROC regression (Rutter and Gatsonis 2001) and the bivariate random-effects meta-analysis model (BRMA; Reitsma et al. 2005;Arends et al. 2008;Chu and Cole 2006;Riley et al. 2007b). However, these models do not report the overall mean but rather the mean for a central study with random-effect equal to zero and have difficulties estimating the correlation between sensitivity and specificity when the number of studies in the meta-analysis is small and/or when the between-study variance is relatively large (Riley et al. 2007a).This tutorial on advanced statistical methods for meta-analysis of diagnostic accuracy studies discusses and demonstrates Bayesian modeling using the R package CopulaDTA (Nyaga 2017) to fit different models to obtain the meta-analytic parameter estimates. The focus is on the joint modeling of sensitivity and specificity using a copula based bivariate beta distribution. Essentially, we extend the work of Nikoloulopoulos (2015) by: (i) presenting the Bayesian approach which offers the flexibility and ability to perform complex statistical modeling even with small data sets and (ii) including covariate information, and (iii) providing an easy to use code. The statistical methods are illustrated by re-analyzing data of two published meta-analyses.Modeling sensitivity and specificity using the bivariate beta distribution provides marginal as well as study-specific parameter estimates as opposed to using the bivariate normal distribution (e.g., in BRMA) which only yields study-specific parameter estimates. Moreover, copula based models offer greater flexibility in modeling different correlation structures in contrast to the normal distribution which allows for only one correlation structure.

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A mixed effect model for bivariate meta‐analysis of diagnostic test accuracy studies using a copula representation of the random effects distribution

Cited by 36 publications

References 51 publications

A double SIMEX approach for bivariate random-effects meta-analysis of diagnostic accuracy studies

A double SIMEX approach for bivariate random-effects meta-analysis of diagnostic accuracy studies

Coupling Couples With Copulas: Analysis of Assortative Matching on Risk Attitude

CopulaDTA: An R Package for Copula-Based Bivariate Beta-Binomial Models for Diagnostic Test Accuracy Studies in a Bayesian Framework

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