arXiv subjects

Marco Alfò

Publications and source records attributed to Marco Alfò.

8 recordsLinked to original sources

arXiv2026

A Dynamic Latent Space Model for Healthcare Mobility Networks: the Italian National Health Service case

Healthcare mobility -- patients seeking treatment outside their territory of residence -- represents a major source of inequality and financial imbalance in decentralised health systems. In Italy, persistent north-south asymmetries in patient flows among Local Health Authorities (ASLs) have reinforced existing disparities within the National Health Service; yet the structural organisation and temporal dynamics of these flows remain poorly understood at the sub-regional level. We propose a Bayesian dynamic latent space model for directed weighted networks with a hurdle negative binomial likelihood, and apply it to administrative discharge records on mobility for hip replacement procedures among 109 Italian ASLs over 2018-2024. The model jointly addresses excess zeros, overdispersion and network dependence, while capturing directional heterogeneity through multiplicative sender and receiver effects and controlling for differences in territorial size via an appropriate exposure term. Applied to Italian mobility data, the model reveals the evolving geometry of the healthcare system, quantifies the disruption induced by the COVID-19 pandemic, and uncovers structural asymmetries in outward propensity and ASLs attractiveness. The framework provides a flexible tool for the statistical analysis of dynamic healthcare mobility networks with direct relevance to the monitoring and evaluation of territorial healthcare provision.

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arXiv2025

Dynamic Prediction in Mixture Cure Models: A Model-Based Landmarking Approach

Mixture cure models are widely used in survival analysis when a portion of patients is considered cured and is no longer at risk for the event of interest. In clinical settings, dynamic survival prediction is particularly important to refine prognosis by incorporating updated patient information over time. Landmarking methods have emerged as a flexible approach for this purpose, as they allow to summarize longitudinal covariates up to a given landmark time and to use these summaries in subsequent prediction. For mixture cure models, the only landmarking strategy available in the literature relies on the last observation carried forward (LOCF) method to summarize longitudinal dynamics up to the landmark time. However, LOCF discards most of the longitudinal information, does not correct for measurement error, and may rely on outdated values if observation times are far apart. To overcome these limitations, we propose a sequential approach that integrates model-based landmarking within a mixture cure model. Initially, longitudinal covariates are modeled using (generalized) linear mixed models, from which individual-specific random effects are predicted. The predicted random effects are then incorporated as covariates into a Cox proportional hazards cure model. We investigated the performance of the proposed approach under different cure fractions, sample sizes, and longitudinal data structures through an extensive simulation study. The results show that the model-based strategy provides more refined predictions compared to LOCF, even when the model is misspecified in favour of the LOCF approach. Finally, we illustrate our method using a real-world dataset on renal transplant patients.

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arXiv2024

A multiple imputation approach to distinguish curative from life-prolonging effects in the presence of missing covariates

Medical advances have increased cancer survival rates and the possibility of finding a cure. Hence, it is crucial to evaluate the impact of treatments both in terms of cure and prolongation of survival. To achieve this, we may use a Cox proportional hazards (PH) cure model. However, a significant challenge in applying such a model is the potential presence of partially observed covariates. We aim to refine the methods for imputing partially observed covariates based on multiple imputation and fully conditional specification (FCS) approaches. To be more specific, we consider a general case in which different covariate vectors are used to model the probability of cure and the survival of patients who are not cured. We investigated the performance of the multiple imputation procedure based on the exact conditional distribution and an approximate imputation model, which helps to draw imputed values at a lower computational cost. To assess the effectiveness of these approaches, we compare them with a complete case analysis and an analysis that includes all available covariates in modelling both cure probabilities and the survival of the uncured. We discuss the application of these techniques to a real-world dataset from the BO06 clinical trial on osteosarcoma.

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arXiv2024

A flexible parametric approach to synthetic patients generation using health data

Enhancing reproducibility and data accessibility is essential to scientific research. However, ensuring data privacy while achieving these goals is challenging, especially in the medical field, where sensitive data are often commonplace. One possible solution is to use synthetic data that mimic real-world datasets. This approach may help to streamline therapy evaluation and enable quicker access to innovative treatments. We propose using a method based on sequential conditional regressions, such as in a fully conditional specification (FCS) approach, along with flexible parametric survival models to accurately replicate covariate patterns and survival times. To make our approach available to a wide audience of users, we have developed user-friendly functions in R and Python to implement it. We also provide an example application to registry data on patients affected by Creutzfeld-Jacob disease. The results show the potentialities of the proposed method in mirroring observed multivariate distributions and survival outcomes.

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arXiv2021

Model-based clustering for multidimensional social networks

Social network data are relational data recorded among a group of actors, interacting in different contexts. Often, the same set of actors can be characterized by multiple social relations, captured by a multidimensional network. A common situation is that of colleagues working in the same institution, whose social interactions can be defined on professional and personal levels. In addition, individuals in a network tend to interact more frequently with similar others, naturally creating communities. Latent space models for network data are useful to recover clustering of the actors, as they allow to represent similarities between them by their positions and relative distances in an interpretable low dimensional social space. We propose the infinite latent position cluster model for multidimensional network data, which enables model-based clustering of actors interacting across multiple social dimensions. The model is based on a Bayesian nonparametric framework, that allows to perform automatic inference on the clustering allocations, the number of clusters, and the latent social space. The method is tested on simulated data experiments, and it is employed to investigate the presence of communities in two multidimensional networks recording relationships of different types among colleagues.

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arXiv2019

Modelling heterogeneity in Latent Space Models for Multidimensional Networks

Multidimensional network data can have different levels of complexity, as nodes may be characterized by heterogeneous individual-specific features, which may vary across the networks. This paper introduces a class of models for multidimensional network data, where different levels of heterogeneity within and between networks can be considered. The proposed framework is developed in the family of latent space models, and it aims to distinguish symmetric relations between the nodes and node-specific features. Model parameters are estimated via a Markov Chain Monte Carlo algorithm. Simulated data and an application to a real example, on fruits import/export data, are used to illustrate and comment on the performance of the proposed models.

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arXiv2018

Latent Space Modeling of Multidimensional Networks with Application to the Exchange of Votes in Eurovision Song Contest

The Eurovision Song Contest is a popular TV singing competition held annually among country members of the European Broadcasting Union. In this competition, each member can be both contestant and jury, as it can participate with a song and/or vote for other countries' tunes. Throughout the years, the voting system has repeatedly been accused of being biased by the presence of tactical voting, according to which votes would represent strategic interests rather than actual musical preferences of the voting countries. In this work, we develop a latent space model to investigate the presence of a latent structure underlying the exchange of votes. Focusing on the period from 1998 to 2015, we represent the vote exchange as a multivariate network: each edition is a network, where countries are the nodes and two countries are linked by an edge if one voted for the other. The different networks are taken to be independent replicates of a common latent space capturing the overall relationships among the countries. Proximity denotes similarity, and countries close in the latent space are assumed to be more likely to exchange votes. Therefore, if the exchange of votes depends on the similarity between countries, the quality of the competing songs might not be a relevant factor in the determination of the voting preferences, and this would suggest the presence of bias. A Bayesian hierarchical modelling approach is employed to model the probability of a connection between any two countries as a function of their distance in the latent space, and of network-specific parameters and edge-specific covariates. The inferred latent space is found to be relevant in the determination of edge probabilities, however, the positions of the countries in such space only partially correspond to their actual geographical positions.

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arXiv2017

A bi-dimensional finite mixture model for longitudinal data subject to dropout

In longitudinal studies, subjects may be lost to follow-up, or miss some of the planned visits, leading to incomplete response sequences. When the probability of non-response, conditional on the available covariates and the observed responses, still depends on unobserved outcomes, the dropout mechanism is said to be non ignorable. A common objective is to build a reliable association structure to account for dependence between the longitudinal and the dropout processes. Starting from the existing literature, we introduce a random coefficient based dropout model where the association between outcomes is modeled through discrete latent effects. These effects are outcome-specific and account for heterogeneity in the univariate profiles. Dependence between profiles is introduced by using a bi-dimensional representation for the corresponding distribution. In this way, we define a flexible latent class structure which allows to efficiently describe both dependence within the two margins of interest and dependence between them. By using this representation we show that, unlike standard (unidimensional) finite mixture models, the non ignorable dropout model properly nests its ignorable counterpart. We detail the proposed modeling approach by analyzing data from a longitudinal study on the dynamics of cognitive functioning in the elderly. Further, the effects of assumptions about non ignorability of the dropout process on model parameter estimates are (locally) investigated using the index of (local) sensitivity to non-ignorability.

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