25gmds034 10.3205/25gmds034 urn:nbn:de:0183-25gmds0347 Meeting Abstract Remo Remo Flavia F

Institut für Medizinische Statistik, Informatik und Datenwissenschaften (IMSID), Universitätsklinikum Jena, Jena, Germany

author Schlattmann Schlattmann Peter P

Institut für Medizinische Statistik, Informatik und Datenwissenschaften (IMSID), Universitätsklinikum Jena, Jena, Germany

author Lehmann Lehmann Thomas T

Institut für Medizinische Statistik, Informatik und Datenwissenschaften (IMSID), Universitätsklinikum Jena, Jena, Germany

author German Medical Science GMS Publishing House

Düsseldorf

610 meta-analysis mixture model diagnostic accuracy test study 20251103 engl This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). M0631 034 Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie 70. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS) V: Evidence synthesis, meta-analyses and meta-science Jena 20250907 20250911 Abstr. 172 TextHeterogeneity is a common challenge in meta-analysis, where differences in study design, populations, and measurement methods often violate the assumption of homogeneity, a violation that can substantially impact the validity, precision, and interpretation of pooled effect estimates , . In order to address heterogeneity a bivariate linear mixed model can be used to jointly analyze estimated pairs of sensitivity (Se) and specificity (Sp) from a meta-analysis of diagnostic tests . This bivariate statistical approach does not transform pairs of sensitivity and specificity of individual studies into a single indicator of diagnostic accuracy, but preserves the two-dimensional nature of the data, taking into account any correlation between the two. Chu and Cole (2006) proposed an exact binomial rendition of the bivariate mixed-effects regression model using the binomial distribution for sensitivity and specificity and a bivariate normal distribution for the corresponding random effects . The assumption of a normal distribution for the random effects may be too strong. To overcome this, we propose a semiparametric mixture model based on a nonparametric distribution of the random effects which allows for flexible modeling of heterogeneity across studies, enabling the joint estimation of sensitivity and specificity. Finite mixture models, though not yet standard in meta-analysis, offer a flexible and principled framework for uncovering latent subgroups and capturing multimodal heterogeneity, as demonstrated in related work (see , ). To implement this approach in the context of diagnostic accuracy, we assume a bivariate binomial distribution for sensitivity and specificity, in the sense that marginally each of the two random variables has a binomial distribution. We then consider the corresponding likelihood to characterize the probability distribution function (pdf) of the mixture model, allowing for the joint modeling of these correlated diagnostic measures across latent subpopulations. This leads to a finite mixture of bivariate logistic regression models, which allows for the classification of the studies into latent subgroups with distinct diagnostics profiles. Model parameters are then estimated using the EM algorithm , from which sensitivity and specificity are obtained by applying the logit transformation to the estimated parameters. The method is presented using data from a meta-analysis investigating the diagnostic accuracy of Procalcitonin (PCT) for diagnosis of sepsis in critically ill patients .The authors declare that they have no competing interests.The authors declare that an ethics committee vote is not required. Reitsma JB Glas AS Rutjes AW Scholten RJ Bossuyt PM Zwinderman AH Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews 2005 J Clin Epidemiol 982-90 Reitsma JB, Glas AS, Rutjes AW, Scholten RJ, Bossuyt PM, Zwinderman AH. Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J Clin Epidemiol. 2005 Oct;58(10):982-90. DOI: 10.1016/j.jclinepi.2005.02.022 http://dx.doi.org/10.1016/j.jclinepi.2005.02.022 Chu H Cole SR Bivariate meta-analysis of sensitivity and specificity with sparse data: a generalized linear mixed model approach 2006 J Clin Epidemiol 1331-2 Chu H, Cole SR. Bivariate meta-analysis of sensitivity and specificity with sparse data: a generalized linear mixed model approach. J Clin Epidemiol. 2006 Dec;59(12):1331-2. DOI: 10.1016/j.jclinepi.2006.06.011 http://dx.doi.org/10.1016/j.jclinepi.2006.06.011 Rutter CM Gatsonis CA A hierarchical regression approach to meta-analysis of diagnostic test accuracy evaluations 2001 Stat Med 2865-84 Rutter CM, Gatsonis CA. A hierarchical regression approach to meta-analysis of diagnostic test accuracy evaluations. Stat Med. 2001 Oct 15;20(19):2865-84. DOI: 10.1002/sim.942 http://dx.doi.org/10.1002/sim.942 Stijnen T Hamza TH Ozdemir P Random effects meta-analysis of event outcome in the framework of the generalized linear mixed model with applications in sparse data 2010 Stat Med 3046-67 Stijnen T, Hamza TH, Ozdemir P. Random effects meta-analysis of event outcome in the framework of the generalized linear mixed model with applications in sparse data. Stat Med. 2010 Dec 20;29(29):3046-67. DOI: 10.1002/sim.4040 http://dx.doi.org/10.1002/sim.4040 Thompson SG Sharp SJ Explaining heterogeneity in meta-analysis: a comparison of methods 1999 Stat Med 2693-708 Thompson SG, Sharp SJ. Explaining heterogeneity in meta-analysis: a comparison of methods. Stat Med. 1999 Oct 30;18(20):2693-708. DOI: 10.1002/(sici)1097-0258(19991030)18:20<2693::aid-sim235>3.0.co;2-v http://dx.doi.org/10.1002/(sici)1097-0258(19991030)18:20<2693::aid-sim235>3.0.co;2-v McLachlan GJ Krishnan T 1997 The EM algorithm and Extensions McLachlan GJ, Krishnan T. The EM algorithm and Extensions. John Wiley & Sons; 1997. Wacker C Prkno A Brunkhorst FM Schlattmann P Procalcitonin as a diagnostic marker for sepsis: a systematic review and meta-analysis 2013 Lancet Infect Dis 426-35 Wacker C, Prkno A, Brunkhorst FM, Schlattmann P. Procalcitonin as a diagnostic marker for sepsis: a systematic review and meta-analysis. Lancet Infect Dis. 2013 May;13(5):426-35. DOI: 10.1016/S1473-3099(12)70323-7 http://dx.doi.org/10.1016/S1473-3099(12)70323-7 0 0 0 0