arXiv subjects

Enzo Cerullo

Publications and source records attributed to Enzo Cerullo.

4 recordsLinked to original sources

arXiv2025

Latent class multivariate probit and latent trait models for evaluating test accuracy without a gold standard: A simulation study

In the context of an imperfect gold standard, latent class modelling can be used to estimate accuracy of multiple medical tests. However, the conditional independence (CI) assumption is rarely thought to be clinically valid. Two models accommodating conditional dependence are the latent class multivariate probit (LC-MVP) and latent trait models. Despite LC-MVP's greater flexibility - modelling full correlation matrices versus the latent trait's restricted structure - the latent trait has been more widely used. No simulation studies have directly compared these two models. We conducted a comprehensive simulation study comparing both models across five data generating mechanisms: CI, low-heterogeneity (latent trait-generated), and high-heterogeneity (LC-MVP-generated) correlation structures. We evaluated multiple priors, including novel constrained correlation priors using Pinkney's method that preserves prior interpretability. Models were fit using our BayesMVP R package, which achieves GPU-like speed-ups on these inherently serial models. The LC-MVP model demonstrated superior overall performance. Whilst the latent trait model performed acceptably on its own generated data, it failed for high-heterogeneity structures, sometimes performing worse than the CI model. The CI model did badly for most dependent structures. We also found ceiling effects: high sensitivities reduced the importance of correlation recovery, explaining paradoxes where models achieved good performance despite poor correlation recovery. Our results strongly favour LC-MVP for practical applications. The latent trait model's severe consequences under realistic correlation structures make it a more risky choice. However, LC-MVP with custom correlation constraints and priors provides a safer, more flexible framework for test accuracy evaluation without a perfect gold standard.

stat.ME↗

arXiv2025

Ordinal regression for meta-analysis of test accuracy: a flexible approach for utilising all threshold data

Standard (network) meta-analysis methods for medical test accuracy evaluation analyse the data separately for each test threshold - wasting data - unless every study reports all thresholds. Previously proposed "multiple threshold" models either fail to provide threshold-specific summary estimates, or they assume that ordinal tests (e.g., questionnaires) are continuous. We propose two ordinal regression models - ordinal-bivariate and ordinal-HSROC - using an induced-Dirichlet framework for cutpoint parameters, enabling intuitive priors and both fixed-effects and random-effects cutpoints. We conducted a simulation study to evaluate the performance of our proposed models, with the simulated data being based on real anxiety screening data spanning 7, 22, and 64 ordinal categories, with 15%, 40% and 55% missing threshold data. Our proposed ordinal-bivariate model with fixed-effect cutpoints tended to obtain the best RMSE and bias, including when data was generated from a recently proposed continuous-assumption model. For instance - even with 64 categories - continuous models performed 10%-30% worse than our models, contradicting the common assumption that many categories justify treating ordinal tests as continuous. Furthermore, the standard stratified-bivariate approach showed worse performance, especially for tests with higher missingness. We implemented the models in the MetaOrdDTA R package (https://github.com/CerulloE1996/MetaOrdDTA), which provides features such as: Stan estimation, K-fold cross-validation for model selection, meta-regression, network meta-analysis extensions, and visualisation tools including sROC plots with credible/prediction regions. Overall, our simulation study suggests that our proposed models may obtain better accuracy estimates than previous approaches for ordinal tests, even when the number of ordinal categories is very high.

stat.ME↗

arXiv2022

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

Introduction: Despite their applicability, statistical models used for the meta-analysis of test accuracy require specialised knowledge to implement, with the necessary level of expertise having recently increased. This is due to the development and recommendation to use more sophisticated methods; such as those in Version 2 of the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy. This paper describes a web-based application that extends the functionality of previous applications, making many advanced analysis methods more accessible. Methods: We sought to create an extended, stand-alone, Bayesian version of MetaDTA, which (i) has the benefits of previously proposed applications and addresses key limitations of them, (ii) is accessible to researchers who do not have the specific expertise required to fit such models, and (iii) is suitable for experienced analysts. We created the application using Shiny and Stan. Results: We created MetaBayesDTA (https://crsu.shinyapps.io/MetaBayesDTA/), which allows users to conduct meta-analysis of test accuracy, with or without a gold standard. The application addresses several key limitations of other applications. For instance, for the bivariate model, one can conduct subgroup analysis, univariate meta-regression, and comparative test accuracy evaluation. Meanwhile, for the model which does not assume a perfect gold standard, the application can account for the fact that studies use different reference tests. Conclusions: Due to its user-friendliness and broad array of features, MetaBayesDTA should appeal to a wide variety of researchers. We anticipate that the application will encourage wider use of more advanced methods, which ultimately should improve the quality of test accuracy reviews.

stat.AP↗

arXiv2022

Meta-analysis of dichotomous and ordinal tests without a gold standard

Standard methods for the meta-analysis of medical tests without a gold standard are limited to dichotomous data. Multivariate probit models are used to analyze correlated binary data, and can be extended to multivariate ordered probit models to model ordinal data. Within the context of an imperfect gold standard, they have previously been used for the analysis of dichotomous and ordinal tests in a single study, and for the meta-analysis of dichotomous tests. In this paper, we developed a hierarchical, latent class multivariate probit model for the simultaneous meta-analysis of ordinal and dichotomous tests without assuming a gold standard. The model can accommodate a hierarchical partial pooling model on the conditional within-study correlations, enabling one to obtain summary estimates of joint test accuracy. Dichotomous tests use probit regression likelihoods and ordinal tests use ordered probit regression likelihoods. We fitted the models using Stan, which uses a state-of-the-art Hamiltonian Monte Carlo algorithm. We applied the models to a dataset in which studies evaluated the accuracy of tests, and test combinations, for deep vein thrombosis. We first demonstrated the issues with dichotomising test accuracy data a priori without a gold standard by fitting models which dichotomised the ordinal test data, and then we applied models which do not dichotomise the data. Furthermore, we fitted and compared a variety of other models, including those which assumed conditional independence and dependence between tests, and those assuming perfect and an imperfect gold standard.

stat.AP↗