arXiv subjects

Antonio Gasparrini

Publications and source records attributed to Antonio Gasparrini.

3 recordsLinked to original sources

arXiv2026

A constrained iteratively reweighted least squares framework for generalised linear models

Many applications of generalised linear models (GLMs) can be improved by applying constraints that impose assumptions on the associations and improve consistency of the estimators. Yet, there are still barriers to the implementation and practical application of constrained GLMs. We present a general framework for fitting GLMs subject to linear constraints on the coefficients that offers original and interesting features. First, estimation is performed using constrained iteratively reweighted least squares (CIRLS), offering fast and stable algorithms with excellent convergence performance. Second, the development includes advanced inferential procedures based on truncated multivariate normal distribution and corrected degrees of freedom that account for the constrained nature of the estimation problem. Extensive simulation studies indicate good inferential and computational properties, even in the case of slightly overconstrained models. Third, the proposed methods are fully implemented in the 'cirls' library for the R software, embedding constrained estimation in standard regression routines with simple usage and syntax. Two real-data case studies provide examples of applications for constrained dose-response estimation and compositional data analysis. The CIRLS framework and software offer a unified approach for various constrained estimation problems across a wide range of research areas.

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arXiv2026

Spatially varying distributed lag non-linear models using Laplacian P-splines

Although distributed lag non-linear models (DLNMs) are commonly used to quantify delayed and non-linear exposure-response relationships, most existing applications assume that these relationships are constant across space. However, in many geographical and environmental studies, local characteristics vary substantially across areas, making a spatially varying effect more realistic. Extending DLNMs to allow for spatial heterogeneity remains challenging, and only a limited number of modelling strategies have been proposed in literature. The most popular extension is a two-stage meta-analysis approach, which requires sufficiently large sample sizes at each location. Therefore, its usefulness is limited when working with sparse count data in small area data analyses. Although a number of alternative one-stage approaches have been introduced, their computational burden restricts their applicability in real-life data applications. In this paper, we introduce a computationally efficient Bayesian one-stage spatially-varying DLNM for count data. We define four model variants, differing in the assumed spatial dependence structure and the flexibility of the DLNM spline specification. To address the computational burden typically associated with these flexible models, we use Laplace approximations, offering an efficient alternative to classically used Markov Chain Monte Carlo (MCMC) approaches. Model comparison criteria are provided to facilitate the selection of a suitable model in a real-life data application. The proposed methods are evaluated through simulation studies, and their practical usefulness is illustrated through a real-life data application, investigating the temperature-mortality relationship in every municipality of Sicily, Italy.

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arXiv2026

Distributed lag non-linear models with spatial effect modification using Laplacian P-splines

Distributed lag non-linear models (DLNMs) are a popular approach to flexibly model the effect of time-delayed exposures. Classical DLNMs specify a common exposure-lag-response relationship across geographical areas. However, this relationship might be altered by an effect modifier that differs between spatial units. Although some methods have been proposed to account for effect modification, their applicability is context-dependent. For example, a meta-analysis can account for heterogeneity between groups, but this technique requires sufficiently large study groups. This limitation is particularly relevant when working with count data, where small numbers of events are often encountered. In this paper, we review existing methods that allow for spatial effect modification for count-based outcomes and propose a Bayesian DLNM alternative method that accounts for the modifier through flexible interaction effects. Through the use of Laplacian P-splines, we provide a computationally fast estimation procedure by avoiding the use of classical Markov Chain Monte Carlo (MCMC) approaches. The performance of the different methods is evaluated through simulation studies. Moreover, the practical applicability of our proposed method is showcased through a data application, containing daily temperature and mortality count data in 87 Italian cities.

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