Bivariate random effects metaanalysis of roc curves article pdf available in medical decision making 285. Tufts evidencebased practice center, tufts medical center boston, ma. This is the first bivariate gwas metaanalysis to demonstrate genetic factors with pleiotropic effects on bone mineral density and lean mass. Pdf bivariate randomeffects metaanalysis models for diagnostic. Study a study b study c study d summary effect size and 95% confidence interval fixedeffect model 1. Metaanalysis of research on the effectiveness of juvenile drug courts 2. Often the advantage of a multivariate randomeffects metaanalysis lies in its ability to use the betweenstudy correlation of the multiple endpoints of interest. A summary roc curve was drawn on the basis of the betweenstudy variance matrix in the roc space where the individual studies were plotted. Because of this heterogeneity, random effects models including the hierarchical summary receiver operating characteristic model and bivariate random effects metaanalysis on sensitivities and specificities 6,8,10, which are identical in some situations, have been recommended 11,12,15. Accuracy of body mass index in predicting preeclampsia.
Bivariate random effects models for metaanalysis of comparative studies with binary outcomes. The second goal is to extend this method to a bivariate random effects model, in which the effects in both groups are supposed random. Bivariate randomeffects model for metaanalysis a bivariate randomeffects model is a special case of a generalized linear mixed model. Ben dwamena asked earlier this month about using bivariate random effects modeling in metaanalysis of accuracy indexes of diagnostic tests. There are two popular statistical models for metaanalysis, the fixedeffect model and the randomeffects model. Original article micrornas level as an initial screening. Bayesian randomeffects metaanalysis using the bayesmeta. Methods in this paper we assess maximum likelihood estimation of a general normal model and a generalised model for bivariate randomeffects metaanalysis brma.
A double simex approach for bivariate randomeffects meta. If more than one measurement is made on each observation, multivariate analysis is applied. General bivariate normal rng consequently, if we want to generate a bivariate normal random variable with x. To ensure a valid joint probability density function for p1, p2 and avoid. Metaanalysis of research on the effectiveness of juvenile. They can be approached with separate, independent metaanalyses, or they can be analyzed jointly, in a single model. Richard d riley, keith r abrams, alexander j sutton, paul c lambert and john r thompson, bivariate randomeffects metaanalysis and the estimation of betweenstudy correlation, bmc medical research methodology, 10. Study a study b study c study d summary effect size and 95% confidence interval randomeffects model 1. Because of this heterogeneity, random effects models including the hierarchical summary receiver operating characteristic model 4 and bivariate. Interpretation of random effects metaanalyses the bmj. In this article, we consider a bivariate generalized linear mixed effects model as a general framework for the metaanalysis of diagnostic tests when a gold standard reference test is available.
Summary estimates of treatment effect from random effects metaanalysis give only the average effect across all studies. The approach preserves the twodimensional nature of the original data taking into account the betweenstudies correlation of. Bivariate randomeffects metaanalysis and the estimation of betweenstudy correlation richard d riley, 1 keith r abrams, 2 alexander j sutton, 2 paul c lambert, 2 and john r thompson 2 1 centre for medical statistics and health evaluation, school of health sciences, university of liverpool, shelleys cottage, brownlow street, liverpool, l69 3gs, uk. Comparison of low and standarddose ct for the diagnosis. Haitao chu,1 lei nie,2, yong chen,3 yi huang4 and wei sun5. To compare the sensitivity and specificity between two. Of 154 studies, nine studies investigating a total of 2957 patients were included.
A number of different, and apparently divergent, methods for metaanalysis of diagnostic studies have been proposed, including two alternative approaches that are statistically rigorous and allow for betweenstudy variability. Traditionally, metaanalysis models combine summary measures of a single quantitative endpoint, taken from different studies, to produce a. Randomness in statistical models usually arises as a result of random sampling of units in data collection. Riley centre for medical statistics and health evaluation, faculty of medicine, university of liverpool, shelleys cottage, brownlow street, liverpool, england l69 3gs richard. Summary receiver operating characteristic curves are deduced for the proposed model through quantile regression techniques and different. Bivariate random effects models for metaanalysis of. To ensure a valid joint probability density function. We used a bivariate randomeffects model to obtain summary estimates of sensitivity and specificity and corresponding 95% cis. Recently, multivariate random effects models for metaanalyses have. Models that include both fixed and random effects may be called mixedeffects models or just mixed models. Aim this metaanalysis assesses the performances of two ifobts compared with an established gfobt using colonoscopy as the gold standard.
Recently, multivariate random effects models for metaanalyses have become. We then extracted the sensitivity and specificity of individual studies to. Bivariate random effects metaanalysis of roc curves. Multivariate metaanalysis has been quite widely used, including application to genetic associations, surrogate endpoints 7,8, psychological findings and prognostic markers. The bivariate random effects model has been advocated for the metaanalysis of diagnostic accuracy despite scarce information regarding its statistical performance for noncomparative categorical outcomes. Multivariate metaanalysis mma, which jointly analyzes multiple and possibly correlated outcomes, has recently received a great deal of attention jackson et al. Methods we mobilized a bivariate and a hierarchical summary receiver operating characteristic hsroc model. Testing small study effects in multivariate metaanalysis. If all studies in the analysis were equally precise we could simply compute the mean of the effect sizes.
However, to the best of our knowledge, statistical tests that quantify the evidence of sse for multivariate. A new stata command, mvmeta, performs maximum likelihood, restricted maximum likelihood, or methodofmoments estimation of randomeffects multivariate metaanalysis models. Introduction to bivariate analysis when one measurement is made on each observation, univariate analysis is applied. A mixed effect model for bivariate metaanalysis of diagnostic test.
Multivariate metaanalysis combines estimates of several related parameters over several studies. Statistical methods in medical research bivariate random. Metaregression was used to perform statistical comparisons of lowdose ct and standarddose ct. For the situation where each study presents one pair of sensitivity and specificity with corresponding standard errors, the bivariate metaanalysis approach has become a well established method 1, 2, 14. Multivariate metaanalysis is increasingly utilised in biomedical. An empirical assessment of bivariate methods for metaanalysis of test accuracy prepared for. Four staggered simulation experiments using a fullfactorial design were conducted to assess such performance over a wide range of scenarios. When using some transformations such as the complementary loglog. Richard d riley, keith r abrams, alexander j sutton, paul c lambert and john r thompson, bivariate random effects metaanalysis and the estimation of betweenstudy correlation, bmc medical research methodology, 10.
In this way inference can be made about the relationship between improvement and baseline effect. Inclusion of prediction intervals, which estimate the likely effect in an individual setting, could make it easier to apply the results to clinical practice metaanalysis is used to synthesise quantitative information from related studies and produce results that summarise a. Empirical bayes estimates generated in a hierarchical summary roc analysis agreed closely with those of a full bayesian analysis. Bayesian randome ects metaanalysis using the bayesmeta r package christian r over university medical center g ottingen abstract the randome ects or normalnormal hierarchical model is commonly utilized in a wide range of metaanalysis applications. Each study supplies summary measures, y ij, and associated standard errors, s ij, for each endpoint. Department of health and human services 540 gaither road rockville, md 20850. We then compare the results of bivariate approach to separate univariate analyses, and test the sensitivity of. An alternative model for bivariate randomeffects metaanalysis when the withinstudy correlations are unknown.
Methods for the absolute risk difference and relative risk. Some randomeffects models have been proposed to execute a metaanalysis of roccurve data, but these models are not often used in practice. A multivariate randomeffects metaanalysis must incorporate and estimate the betweenstudy correlation. This allows a comparison of the bivariate and univariate approaches when we first set out the framework for a bivariate random effects metaanalysis, which has been reported in riley et al. For instance, for diagnostic studies reitsma et al. Metaanalytic interval estimation for bivariate correlations. Fiestas navarretea and robert benamouzigb background current literature evidences higher accuracy. Pdf download for bivariate random effects models for metaanalysis of. Here we explain the key concepts, methods, and assumptions of multivariate and network metaanalysis, building on previous articles in the bmj. Consider a metaanalysis of n diagnostic accuracy studies, each of them providing information as a twobytwo table reporting the number of true positives, true negatives, false positives and false negatives, denoted by n 11i,n 00i,n 10i and n 01i, respectively.
Empirical and simulationbased comparison of univariate. To establish the effects of juvenile drug courts, future evaluations should use random assignment or rigorous matching procedures to ensure the equivalence of groups in juvenile drug court and comparison conditions. These parameters can, for example, refer to multiple outcomes or comparisons between more than two groups. Tutorial on multivariate metaanalysis computational. Bivariate randomeffects metaanalysis and the estimation of. Bivariate randomeffects metaanalysis and the estimation of betweenstudy correlation. Metaanalysis of receiver operating characteristic roccurve data is often done with fixedeffects models, which suffer many shortcomings. Pdf bivariate random effects metaanalysis of roc curves. I was interested in using bivariate random effects regression to metaanalyze simultaneously sensitivity and specificity logit transforms as correlated heterogeneous outcomes versus a number of covariates such as study quality, sample size. Methods for the absolute risk difference and relative risk haitao chu,1 lei nie,2, yong chen,3 yi huang4 and wei sun5 abstract multivariate metaanalysis is increasingly utilised in biomedical research to combine data of multiple. Bivariate random effects metaanalysis of diagnostic.
In randomeffects models, some of these systematic effects are considered random. Bivariate randomeffect metaanalysis and the estimation of betweenstudy correlation article pdf available in bmc medical research methodology 71. Riley rd, thompson jr, abrams kr 2008 an alternative model for bivariate randomeffects metaanalysis when the withinstudy correlations are unknown. Fixedeffect versus randomeffects models metaanalysis. A bivariate metaanalysis of diagnostic test studies is a special type of metaanalysis that summarises the results from separately performed diagnostic test studies while keeping the twodimensionality of the data van houwelingen et al. Empirical and simulationbased comparison of univariate and multivariate metaanalysis for binary outcomes structured abstract background. An alternative model for bivariate randomeffects metaanalysis when the withinstudy correlations are unknown richard d. A basic introduction to fixedeffect and randomeffects models for. In this section, we focus on bivariate analysis, where exactly two measurements are made on each observation. Performance measures of the bivariate random effects model. Bivariate randomeffects metaanalysis and the estimation. Bivariate randomeffects metaanalysis models for diagnostic test accuracy studies using arcsinebased transformations.
Bivariate randomeffects formulation for metaanalysis. Accuracy of body mass index in predicting pre eclampsia. The bivariate randomeffects metaanalysis model was applied to obtain the follow ing pooled parameters with corresponding 95% confidence intervals cis. Many questions in evidencebased medicine involve multiple outcomes.