arXiv subjects

Konstantinos Vamvourellis

Publications and source records attributed to Konstantinos Vamvourellis.

3 recordsLinked to original sources

arXiv2026

Bayesian Benefit-Risk Assessment with Dependent Outcomes via Latent Factor Models

Approving and assessing new drugs is complex because multiple criteria must be considered simultaneously. A common approach is benefit-risk analysis, often conducted within a Bayesian framework to account for uncertainty and combine data with expert judgement, typically through multi-criteria decision analysis (MCDA) scores. This requires models that accommodate mixed and potentially correlated outcomes; latent factor models provide a natural framework. We develop a coherent Bayesian framework for benefit-risk analysis that addresses these challenges and supports sequential decision-making. We extend structured factor models to mixed outcomes and introduce a principled approach for selecting among competing specifications that combines model fit with out-of-sample predictive performance. We then develop a sequential estimation framework that updates MCDA scores as new data become available, allowing treatment comparisons to evolve over time. This supports early stopping when conclusions are clear and permits dynamic treatment allocation aligned with study objectives. To make this feasible, we develop tailored sequential Monte Carlo methods adapted to the model structure. The methodology is illustrated using data on patients with type II diabetes treated with Metformin, Rosiglitazone, and their combination.

stat.ME↗

arXiv2022

Model Assessment for a Generalised Bayesian Structural Equation Model

The paper proposes a novel model assessment paradigm aiming to address shortcoming of posterior predictive $p-$values, which provide the default metric of fit for Bayesian structural equation modelling (BSEM). The model framework of the paper focuses on the approximate zero approach, according to which parameters that would before set to zero (e.g. factor loadings) are now formulated to be approximate zero via informative priors (Muthen and Asparouhov, 2012). The introduced model assessment procedure monitors the out-of-sample predictive performance of the fitted model, and together with a list of guidelines we provide, one can investigate whether the hypothesised model is supported by the data. We incorporate scoring rules and cross-validation to supplement existing model assessment metrics for Bayesian SEM. The proposed tools can be applied to models for both categorical and continuous data. The modelling of categorical and non-normally distributed continuous data is facilitated with the introduction of an item-individual random effect that can also be used for outlier detection. We study the performance of the proposed methodology via simulations. The factor model for continuous and binary data is fitted to data on the `Big-5' personality scale and the Fagerstrom test for nicotine dependence respectively.

stat.ME↗

arXiv2022

Sequential Bayesian Inference for Factor Analysis

We develop an efficient Bayesian sequential inference framework for factor analysis models observed via various data types, such as continuous, binary and ordinal data. In the continuous data case, where it is possible to marginalise over the latent factors, the proposed methodology tailors the Iterated Batch Importance Sampling (IBIS) of Chopin (2002) to handle such models and we incorporate Hamiltonian Markov Chain Monte Carlo. For binary and ordinal data, we develop an efficient IBIS scheme to handle the parameter and latent factors, combining with Laplace or Variational Bayes approximations. The methodology can be used in the context of sequential hypothesis testing via Bayes factors, which are known to have advantages over traditional null hypothesis testing. Moreover, the developed sequential framework offers multiple benefits even in non-sequential cases, by providing posterior distribution, model evidence and scoring rules (under the prequential framework) in one go, and by offering a more robust alternative computational scheme to Markov Chain Monte Carlo that can be useful in problematic target distributions.

stat.ME↗