MetaBayesDTA: codeless Bayesian meta-analysis of test accuracy, with or without a gold standard.

BACKGROUND: The statistical models developed for meta-analysis of diagnostic test accuracy studies require specialised knowledge to implement. This is especially true since recent guidelines, such as those in Version 2 of the Cochrane Handbook of Systematic Reviews of Diagnostic Test Accuracy, advocate more sophisticated methods than previously. This paper describes a web-based application - MetaBayesDTA - that makes many advanced analysis methods in this area more accessible. RESULTS: We created the app using R, the Shiny package and Stan. It allows for a broad array of analyses based on the bivariate model including extensions for subgroup analysis, meta-regression and comparative test accuracy evaluation. It also conducts analyses not assuming a perfect reference standard, including allowing for the use of different reference tests. CONCLUSIONS: Due to its user-friendliness and broad array of features, MetaBayesDTA should appeal to researchers with varying levels of expertise. We anticipate that the application will encourage higher levels of uptake of more advanced methods, which ultimately should improve the quality of test accuracy reviews.

Interrater agreement in dementia diagnosis: A systematic review and meta-analysis.

OBJECTIVES: Dementia remains a clinical diagnosis with a degree of subjective assessment and potential for interrater disagreement. We described interrater agreement of clinical dementia diagnosis for various diagnostic criteria. METHODS: We conducted a PROSPERO-registered (CRD42020168245) systematic review and meta-analysis. We searched multiple cross-disciplinary databases from inception until April 2020 for relevant papers, extracted data and described study quality in duplicate. Study quality was assessed using the Guidelines for Reporting Reliability and Agreement Studies. We used random-effects models to obtain summary estimates of interrater agreement using kappa and, where possible, Gwet's AC1/2 coefficients. RESULTS: We found 7577 titles and 22 eligible studies. Meta-analysis was possible for all-cause dementia using the Diagnostic and Statistical Manual of Mental Disorders third edition revised (DSM-III-R) criteria (kappa = 0.66, 95% CI = [0.53,0.78]), Alzheimer's disease using the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer's disease and Related Disorders Association (NINCDS-ADRDA) criteria (kappa = 0.71, 95% CI = [0.65,0.77] for presence/absence and AC2 = 0.61, 95% CI = [0.53,0.70] when distinguishing probable/possible cases), and vascular dementia using the International Classification of Diseases version 10 (ICD-10) criteria kappa = 0.79 (95% CI = [0.70,0.87]). Data was more limited for other criteria and dementia types. AC1/2 coefficients generally indicated higher agreement. One study was rated as high quality. CONCLUSIONS: Diagnostic criteria for clinical dementia may have good but imperfect agreement. This has important implications for clinical practice and research studies, which frequently assume these criteria are perfect tests, such as diagnostic test accuracy studies frequently conducted for biomarkers and neuropsychological tests, and for trials where incident dementia is the outcome.

Predictive and incremental validity of the Static-99, Static-99R, and STABLE-2007 for sexual recidivism: A diagnostic test accuracy network meta-analysis (DTA-NMA).

The Static-99, Static-99R, and STABLE-2007 are internationally well-established instruments for predicting static and dynamic risks of sexual recidivism in individuals convicted of sexual offenses. Previous meta-analyses assessed their predictive and incremental validity, but none has yet compared the two Static versions and the Static-STABLE combinations. Here, we implemented diagnostic test accuracy network meta-analysis (DTA-NMA) to compare all tests and identify optimal cutoffs in one comprehensive analysis. The DTA-NMA included 32 samples comprising 45,224 adult male individuals. More information was available on the Static-99 (22 samples; 34,316 individuals) and the Static-99R (13 samples; 27,243 individuals), compared to the Static-99/STABLE-2007 (three samples; 762 individuals), the Static-99R/STABLE-2007 (two samples; 2,972 individuals), and the STABLE-2007 (three samples; 816 individuals). The primary outcome was the area under the receiver operating characteristic curve (AUC). The secondary outcomes were sensitivity and specificity. Optimal cutoffs were determined using the Youden index. The AUC suggested moderate predictive validity for Static-99 and Static-99R, whereas STABLE-2007 had no predictive value. The optimal cutoff of Static-99R was suggested to have higher specificity than that of Static-99, whereas sensitivity was comparable between instruments. The notion of incremental validity for STABLE-2007 could not be confirmed. This work represents the first meta-analysis to compare Static-99, Static-99R, STABLE-2007, and their combinations in one analysis. Static-99R demonstrated the highest specificity in predicting the risk of sexual recidivism, indicating a potential advantage in detecting true nonrecidivists. The findings are discussed, considering the current recommendations for assessing the risk of sexual recidivism in the criminal justice system. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Meta-analysis of dichotomous and ordinal tests with an imperfect gold standard.

Standard methods for the meta-analysis of medical tests, without assuming a gold standard, are limited to dichotomous data. Multivariate probit models are used to analyse correlated dichotomous data, and can be extended to model ordinal data. Within the context of an imperfect gold standard, they have previously been used for the analysis of dichotomous and ordinal test data from a single study, and for the meta-analysis of dichotomous tests. However, they have not previously been used for the meta-analysis of ordinal tests. In this article, we developed a Bayesian multivariate probit latent class model for the simultaneous meta-analysis of ordinal and dichotomous tests without assuming a gold standard, which also allows one to obtain summary estimates of joint test accuracy. We fitted the models using the software Stan, which uses a state-of-the-art Hamiltonian Monte Carlo algorithm, and we applied the models to a dataset in which studies evaluated the accuracy of tests, and test combinations, for deep vein thrombosis. We demonstrate the issues with dichotomising ordinal test accuracy data in the presence of an imperfect gold standard, before applying and comparing several variations of our proposed model which do not require the data to be dichotomised. The models proposed will allow researchers to more appropriately meta-analyse ordinal and dichotomous tests without a gold standard, potentially leading to less biased estimates of test accuracy. This may lead to a better understanding of which tests, and test combinations, should be used for any given medical condition.