DEP24 - Artículos de revista

Disruption of Radiological Surveillance Following a Global Health Crisis in Resected Lung Cancer

2026-01-01T00:00:00Z

Objectives Radiological surveillance after curative-intent lung cancer resection is essential for early detection of recurrence and second primary tumors. Large-scale health emergencies can compromise oncologic follow-up. This study quantifies the impact of a health crisis on radiological surveillance in a national cohort of resected lung cancer patients. Methods A time-segmented observational cohort study was performed using data from the prospective, multicenter GEVATS registry. Surveillance density (CT/month) was evaluated across three predefined periods: pre-pandemic (baseline), state of alarm (maximum healthcare restrictions), and post-alarm (recovery phase). The population at risk was updated for each period. Subgroup analyses during the post-alarm phase assessed prioritization according to neoadjuvant treatment, pathological stage, age, and comorbidity. Results Among 2382 eligible patients, surveillance density declined progressively from the pre-pandemic period (0.157 ± 0.079 CT/month) to the state of alarm (0.098 ± 0.071 CT/month). In the post-alarm phase, density dropped sharply to 0.023 ± 0.018 CT/month (equivalent to one CT every 3.6 years), representing a 76.5% reduction compared with the state-of-alarm period (p < 0.001). This under-surveillance was generalized, with no significant differences by pathological stage (p = 0.084), age (p = 0.564), or comorbidity (p = 0.872). Only prior neoadjuvant therapy was associated with a slightly higher density (p = 0.040). Conclusions A prolonged health crisis resulted in a profound and persistent reduction in radiological surveillance after lung cancer resection, without evidence of risk-based prioritization. These findings support the need for contingency frameworks within clinical guidelines to preserve continuity of oncologic follow-up during future health emergencies.

GRASP algorithms for the unrelated parallel machines scheduling problem with additional resources during processing and setups

2022-01-01T00:00:00Z

This paper addresses an unrelated parallel machines scheduling problem with the need of additional resources during the processing of the jobs, as well as during the setups that machines need between the processing of any two jobs. This problem is highly complex, and therefore in this paper we propose several constructive heuristics to solve it. To improve the performance of these heuristics, we propose several variations, including randomisation with different probability distributions and a local search phase, having this way GRASP algorithms. The results of extensive experiments over randomly generated instances show several findings on the different parameters that characterise our constructive algorithms. In particular, we highlight the fact that non-uniform probability distributions might be advisable for choosing elements of a restricted candidate list in GRASP algorithms.

A note on the regularity of optimal-transport-based center-outward distribution and quantile functions

2020-01-01T00:00:00Z

We provide sufficient conditions under wich the center-outward distribution and quantile func- tions introduced in Chernozhukov et al. (2017) and Hallin (2017) are homeomorphisms, thereby extending a recent result by Figalli [12]. Our approach relies on Cafarelli’s classical regularity theory for the solutions of the Monge-Amp`ere equation, but has to deal with difficulties related with the unboundedness at the origin of the density of the spherical uniform reference measure. Our conditions are satisfied by probabillities on Euclidean space with a general (bounded or un- bounded) convex support which are not covered in [12]. We provide some additional results about center-outward distribution and quantile functions, including the fact that quantile sets exhibit some weak form of convexity.

On approximate validation of models: a Kolmogorov–Smirnov-based approach

2019-01-01T00:00:00Z

Classical tests of fit typically reject a model for large enough real data samples. In contrast, often in statistical practice, a model offers a good description of the data even though it is not the ‘true’ random generator. We consider a more flexible approach based on contamination neighbourhoods: using trimming methods and the Kolmogorov metric, we introduce a functional statistic measuring departures from a contaminated model. We show how the plug-in estimator allows testing of fit for the (slightly) contaminated model vs sensible deviations from it, with uniformly exponentially small type I and type II error probabilities. We also address the asymptotic behaviour of the estimator showing that, under suitable regularity conditions, it asymptotically behaves as the supremum of a Gaussian process. As an application, we explore methods of comparison between descriptive models based on the paradigm of model falseness. We also include some connections of our approach with the false discovery rate setting, showing competitive behaviour when estimating the contamination level, and being applicable in a wider framework.

An improved central limit theorem and fast convergence rates for entropic transportation costs

2023-01-01T00:00:00Z

We prove a central limit theorem for the entropic transportation cost between subgaussian probability measures, centered at the population cost. This is the first result which allows for asymptotically valid inference for entropic optimal transport between measures which are not necessarily discrete. In the compactly supported case, we complement these results with new, faster, convergence rates for the expected entropic transportation cost between empirical measures. Our proof is based on strengthening convergence results for dual solutions to the entropic optimal transport problem.

Central limit theorems for general transportation costs

2024-01-01T00:00:00Z

We consider the problem of optimal transportation with general cost between an empirical measure and a general target probability on Rd , with d ≥ 1. We provide results on asymptotic stability of optimal transport potentials under minimal regularity assumptions on the costs or the underlying probability. This stability is combined with a refined linearization technique based on the sequential compactness of the closed unit ball in L2(P ) for the weak topology and the strong convergence of Cesàro means along subsequences. As a result we obtain a CLT for the transportation cost under sharp smoothness and moment assumptions, giving a positive answer to a conjecture in (Ann. Probab. 47 (2019) 926–951) for the quadratic costs.

Central limit theorems for semi-discrete Wasserstein distances

2024-01-01T00:00:00Z

We prove a Central Limit Theorem for the empirical optimal transport cost, √nmn+m{Tc(Pn,Qm)−Tc(P,Q)}, in the semi-discrete case, i.e when the distribution P is supported in N points, but without assumptions on Q. We show that the asymptotic distribution is the sup of a centered Gaussian process, which is Gaussian under some additional conditions on the probability Q and on the cost. Such results imply the central limit theorem for the p-Wassertein distance, for p≥1. This means that, for fixed N, the curse of dimensionality is avoided. To better understand the influence of such N, we provide bounds of E|Wpp(P,Qm)−Wpp(P,Q)| depending on m and N. Finally, the semi-discrete framework provides a control on the second derivative of the dual formulation, which yields the first central limit theorem for the optimal transport potentials and Laguerre cells. The results are supported by simulations that help to visualize the given limits and bounds. We analyse also the cases where classical bootstrap works.

Unrelated parallel machine scheduling problem with setup times and additional resources: an enhanced metaheuristic to address resource-related infeasibilities

2026-01-01T00:00:00Z

Efficient scheduling tools are essential for managing production environments where both machine availability and additional resource constraints play a significant role. This paper addresses the Unrelated Parallel Machine scheduling problem with setup times and additional resources in the Setups (UPMSR-S), an NP-hard problem that models real-world production settings where setups require limited resources, such as personnel or specialized equipment. We propose an enhanced algorithm designed to better handle resource-related infeasibilities and consistently outperform state-of-the-art methods. This is demonstrated through an extensive computational campaign on 1,000 benchmark instances, with improvements in Relative Percentage Deviation (RPD) exceeding 70% for several instance sizes. The proposed approach is well suited to large production environments involving setup and resource constraints, showing strong performance in challenging scheduling settings. Statistical analysis confirms that the method is highly effective across a wide range of instance sizes and scenarios, with particularly strong performance as the number of jobs and machines increases.

Distribution and quantile functions, ranks and signs in dimension d: A measure transportation approach

2021-01-01T00:00:00Z

Unlike the real line, the real space Rd , for d ≥ 2, is not canonically ordered. As a consequence, such fundamental univariate concepts as quantile and distribution functions and their empirical counterparts, involving ranks and signs, do not canonically extend to the multivariate context. Palliating that lack of a canonical ordering has been an open problem for more than half a century, generating an abundant literature and motivating, among others, the development of statistical depth and copula-based methods. We show that, unlike the many definitions proposed in the literature, the measure transportation-based ranks and signs introduced in Chernozhukov, Galichon, Hallin and Henry (Ann. Statist. 45 (2017) 223–256) enjoy all the properties that make univariate ranks a successful tool for semiparametric inference. Related with those ranks, we propose a new center-outward definition of multivariate distribution and quantile functions, along with their empirical counterparts, for which we establish a Glivenko–Cantelli result. Our approach is based on McCann (Duke Math. J. 80 (1995) 309–323) and our results do not require any moment assumptions. The resulting ranks and signs are shown to be strictly distribution-free and essentially maximal ancillary in the sense of Basu (Sankhya 21 (1959) 247–256) which, in semiparametric models involving noise with unspecified density, can be interpreted as a finite-sample form of semiparametric efficiency. Although constituting a sufficient summary of the sample, empirical center-outward distribution functions are defined at observed values only. A continuous extension to the entire d-dimensional space, yielding smooth empirical quantile contours and sign curves while preserving the essential monotonicity and Glivenko–Cantelli features of the concept, is provided. A numerical study of the resulting empirical quantile contours is conducted.

Nonparametric multiple-output center-outward quantile regression

2025-01-01T00:00:00Z

Building on recent measure-transportation-based concepts of multivariate quantiles, we are considering the problem of nonparametric multiple-output quantile regression. Our approach defines nested conditional center-outward quantile regression contours and regions with given conditional probability content, the graphs of which constitute nested center-outward quantile regression tubes with given unconditional prob- ability content; these (conditional and unconditional) probability contents do not depend on the underlying distribution—an essential property of quantile concepts. Empirical counterparts of these concepts are constructed, yielding interpretable empirical contours, regions, and tubes which are shown to consistently reconstruct (in the Pompeiu-Hausdorff topology) their population versions. Our method is entirely non- parametric and performs well in simulations—with possible heteroscedasticity and nonlinear trends. Its potential as a data-analytic tool is illustrated on some real datasets. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Minimizing the benefit risk in a target benefit stochastic pension plan

2025-01-01T00:00:00Z

In this paper, we study the optimal management of a target benefit pension plan. The fund manager adjusts the benefit to guarantee the plan stability. The fund can be invested in a rissless asset and a risky asset where the uncertainty comes from Brownian motion process. The manager minimizes the quadratic deviations between benefit and terminal fund with respect to their target values. A weighting factor included in the model indicates the importance of minimizing the deviation of the terminal fund. A stochastic control problem is considered and solved by the programming dynamic approach. Optimal benefit and investment strategies are analytically found and analyzed, both in finite and infinite horizon. An interesting particular case that receives special attention is when the contribution and the targets have an exponential form.

A defined benefit pension plan model with stochastic salary and heterogeneous discounting

2023-01-01T00:00:00Z

We study the time-consistent investment and contribution policies in a defined benefit stochastic pension fund where the manager discounts the instantaneous utility over a finite planning horizon and the final function at constant but different instantaneous rates of time preference. This difference, which can be motivated for some uncertainties affecting payoffs at the end of the planning horizon, will induce a variable bias between the relative valuation of the final function and the previous payoffs and will lead the manager to show time-inconsistent preferences. Both the benefits and the contribution rate are proportional to the total wage of the workers that we suppose is stochastic. The aim is to maximize a CRRA utility function of the net benefit relative to salary in a bounded horizon and to maximize a CRRA final utility of the fund level relative to the salary. The problem is solved by means of dynamic programming techniques, and main results are illustrated numerically.

Data envelopment analysis efficiency in the public sector using provider and customer opinion: An application to the Spanish health system

2022-01-01T00:00:00Z

Measuring the relative efficiency of a finite fixed set of service-producing units (hospitals, state services, libraries, banks,...) is an important purpose of Data Envelopment Analysis (DEA). We illustrate an innovative way to measure this efficiency using stochastic indexes of the quality from these services. The indexes obtained from the opinion-satisfaction of the customers are estimators, from the statistical view point, of the quality of the service received (outputs); while, the quality of the offered service is estimated with opinion-satisfaction indexes of service providers (inputs). The estimation of these indicators is only possible by asking a customer and provider sample, in each service, through surveys. The technical efficiency score, obtained using the classic DEA models and estimated quality indicators, is an estimator of the unknown population efficiency that would be obtained if in each one of the services, interviews from all their customers and all their providers were available. With the object of achieving the best precision in the estimate, we propose results to determine the sample size of customers and providers needed so that with their answers can achieve a fixed accuracy in the estimation of the population efficiency of these service-producing units through the use of a novel one bootstrap confidence interval. Using this bootstrap methodology and quality opinion indexes obtained from two surveys, one of doctors and another of patients, we analyze the efficiency in the health care system of Spain.

An alternative statistical approach to estimate the level of airtightness of existing residential buildings: Influencing factors from measured data

2026-01-01T00:00:00Z

Estimating the level of airtightness of a building can offer valuable information for energy performance simulation tools or decision-making during retrofitting processes. However, it remains a challenge given the great variability of the variables involved, the complexity of addressing some of these variables, and some contextspecific features. Based on previous research in this direction, this paper proposes an alternative predictive model based on Generalized Linear Models (GLIM) and validated using cross-validation that involves 13 main effects and 4 interactions. This leads to a substantial enhancement in predictive capacity, accounting for nearly 50% of the response variability. A detailed set of variables fully described offers the opportunity to transcend region-specific applicability and opens a window for other populations. The model provides more reliable estimates of airtightness and expands its applicability to a broader range of construction conditions, while maintaining the statistical significance of its predictors and achieving a satisfactory fit.

Diffusion Models for Tabular Data Imputation and Synthetic Data Generation

2025-01-01T00:00:00Z

Data imputation and data generation have important applications across many domains where incomplete or missing data can hinder accurate analysis and decision-making. Diffusion models have emerged as powerful generative models capable of capturing complex data distributions across various data modalities such as image, audio, and time series. Recently, they have been also adapted to generate tabular data. In this article, we propose a diffusion model for tabular data that introduces three key enhancements: (1) a conditioning attention mechanism, (2) an encoder-decoder transformer as the denoising network, and (3) dynamic masking. The conditioning attention mechanism is designed to improve the model’s ability to capture the relationship between the condition and synthetic data. The transformer layers help model interactions within the condition (encoder) or synthetic data (decoder), while dynamic masking enables our model to efficiently handle both missing data imputation and synthetic data generation tasks within a unified framework. We conduct a comprehensive evaluation by comparing the performance of diffusion models with transformer conditioning against state-of-the-art techniques such as Variational Autoencoders, Generative Adversarial Networks, and Diffusion Models, on benchmark datasets. Our evaluation focuses on the assessment of the generated samples with respect to three important criteria, namely: (1) machine learning efficiency, (2) statistical similarity, and (3) privacy risk mitigation. For the task of data imputation, we consider the efficiency of the generated samples across different levels of missing features. The results demonstrate average superior machine learning efficiency and statistical accuracy compared to the baselines, while maintaining privacy risks at a comparable level, particularly showing increased performance in datasets with a large number of features. By conditioning the data generation on a desired target variable, the model can mitigate systemic biases, generate augmented datasets to address data imbalance issues, and improve data quality for subsequent analysis. This has significant implications for domains such as healthcare and finance, where accurate, unbiased, and privacy-preserving data are critical for informed decision-making and fair model outcomes.

Visualizing cancer and survivorship with generative AI?—an exploration of breast, prostate, and pancreatic cancer imagery

2025-01-01T00:00:00Z

Purpose Generative Artificial Intelligence (GAI) is transforming visual communication in the context of cancer survivorship, presenting opportunities to innovate advocacy while also posing risks for social representation. This study explores how GAI visualizes cancer and survivorship, focusing on its ability to reflect diverse experiences and its limitations. Methods We analyzed 262 images generated by Dall-E and Stable Diffusion using prompts related to breast, prostate, and pancreatic cancer. A mixed-methods approach examines how GAI utilizes cancer signifiers, visualizes the impact of cancer on individuals, and represents people with cancer. Results GAI frequently reproduces cancer tropes, such as prescriptive positivity, and fails to depict medical treatments or embodied experiences unless explicitly prompted. AI-generated images predominantly featured White, female subjects, particularly in breast cancer contexts, reflecting broader biases in public discourse. While GAI tools can produce inclusive visuals, achieving this requires users to have nuanced knowledge of cancer and survivorship, limiting accessibility for lay GAI users. Conclusions GAI can support cancer communication but risks perpetuating stereotypes and excluding less visible experiences of cancer. Our findings offer practical insights to support the design of advocacy materials and campaigns, particularly through improved prompt literacy and inclusive image generation strategies. Implications for Cancer Survivors Inclusive and respectful visual representation is critical for capturing the diverse realities of cancer survivorship, which in turn affects the wellbeing of cancer survivors and carers. Collaborative efforts among researchers, advocates, and GAI developers are necessary to improve datasets and foster accessible tools, ensuring that GAI supports rather than undermines cancer survivorship advocacy.