Soutinho, Gustavo
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Soutinho
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Gustavo
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Gustavo
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Gustavo Domingos da Costa Coelho Soutinho
Docente do Departamento de Ciência e Tecnologia da Universidade Portucalense.
0918-604C-2C04
57195326662
GSE-1063-2022Research Projects
Organizational Units
REMIT – Research on Economics, Management and Information Technologies
Centro de investigação que que tem como objetivo principal produzir e disseminar conhecimento teórico e aplicado que possibilite uma maior compreensão das dinâmicas e tendências económicas, empresariais, territoriais e tecnológicas do mundo contemporâneo e dos seus efeitos socioeconómicos. O REMIT adota uma perspetiva multidisciplinar que integra vários domínios científicos: Economia e Gestão; Ciências e Tecnologia; Turismo, Património e Cultura.
Founded in 2017, REMIT – Research on Economics, Management and Information Technologies is a research unit of Portucalense University. Based on a multidisciplinary and interdisciplinary perspective it aims at responding to social challenges through a holistic approach involving a wide range of scientific fields such as Economics, Management, Science, Technology, Tourism, Heritage and Culture.
Grounded on the production of advanced scientific knowledge, REMIT has a special focus on its application to the resolution of real issues and challenges, having as strategic orientations:
- the understanding of local, national and international environment;
- the development of activities oriented to professional practice, namely in the business world.
10 results
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Now showing 1 - 10 of 10
Publication Restricted Access Impact of climate investment policies on citizens’ perception of pollution damage in the European Union2024-12-01 - Ribeiro, Vitor Miguel; Soutinho, Gustavo; Soares, IsabelThis study examines the impact of climate investment policies on citizens’ perception of pollution damage in the European Union, while controlling for various environmental indicators. The primary panel data estimation outcome indicates that, despite substantial public expenditure on climate action, consumers’ perception of pollution improvement remains unchanged or even worsens. This suggests support for the policy ineffectiveness hypothesis. In view of this result, advocating for strengthened policies aimed at bolstering climate action in the European Union may be considered a non-credible threat from a microeconomics standpoint.Publication Restricted Access A fresh look on verti-zontally differentiated peer-to-peer electricity trading platforms with and without service customization2024-12-01 - Ribeiro, Vitor Miguel; Soutinho, Gustavo; Soares, IsabelWe analyze a two-sided market where two platforms compete in electricity intraday trading. These intermediaries are differentiated both vertically and horizontally and engage in price competition to attract agents from both sides of the market, buyers and sellers. Alongside accounting for quality disparities at the intermediary level, we consider the possibility that platforms may choose to customize electricity intraday trading services. Main results demonstrate that equilibrium outcomes depend on the interaction between the strength of indirect network externalities and the degree of quality differentiation between platforms when service customization is absent. Notably, regardless of whether horizontal or vertical dominance prevails, the intensity of indirect network externalities consistently fosters pro-competitive effects in the private equilibrium. Conversely, when platforms opt for service customization, indirect network externalities do not influence equilibrium access prices and profits if the quality discrepancy between platforms is sufficiently high. This suggests that pro-competitive effects vanish under this specific circumstance. Consequently, this research emphasizes the critical role of service customization in peer-to-peer electricity intraday trading systems. If overlooked by regulators, the surplus enjoyed by incumbent operators at the distribution level, typically attributed to natural monopolies, may be transferred to high-quality platforms that customize services.Publication Restricted Access Survapp: A shiny application for survival data analysis2025-01-11 - Silva, Emanuel Vieira Monteiro da; Meira-Machado, Luís Filipe; Soutinho, GustavoThere is a substantial demand for user-friendly graphical interfaces that empower professionals with limited programming knowledge to perform statistical analysis. Although R software is widely used for statistical analysis, it lacks an adequately intuitive graphical interface for individuals without statistical and programming skills. This paper aims to address this gap by introducing an application called Survapp, enabling users, regardless of their computational knowledge, to conduct survival analysis. The development leveraged R software, RStudio, and the Shiny package to create an interactive web app. Survapp incorporates diverse methodologies for analyzing survival data, including Kaplan-Meier, log-rank tests, Cox regression models, parametric accelerated failure time models, decision trees, random forests, and competitive risk analysis (a specific case of multi-state models). Survapp enables users to analyze survival data, offering example databases for various methodologies within the application. However, the primary objective is to allow users to import their own data and conduct their respective analyses in a user-friendly environment. A distinguishing aspect of Survapp is its interface, bridging the gap between complex statistical methods and users with limited statistical and programming expertise.Publication Open Access MSM.app: An interactive Web tool for Survival Analysis and Advanced Multi-State Modeling2024-12-02 - Soutinho, Gustavo; Meira-Machado, LuísThe development of applications aimed at providing interpretable results in a concise and user-friendly manner within the framework of multi-state models represents a promising research avenue, particularly when leveraging open-source tools adaptable to biomedical contexts. This paper introduces MSM.app, an interactive web application constructed using the Shiny package for the R language. MSM.app is structured into two main components, each addressing distinct facets of survival analysis and its extension to intricate multi-state models. The first component allows users to perform classical survival analysis techniques using standard functions from R packages such as survival. The second component supports users in achieving key objectives in multi-state analysis, including the inference of regression models and the estimation of transition probabilities, by integrating with the survidm and mstate R packages. The web application employs dynamic web forms, tables, and graphics, harnessing the capabilities of the Shiny package. This architecture ensures that users, regardless of their level of expertise in informatics, can seamlessly engage in dynamic analyses encompassing critical aspects of multi-state models.Publication Open Access Estimation of the transition probabilities conditional on covariates with repeated measures: A joint modeling approach2024-06-07 - Soutinho, Gustavo; Meira-Machado, LuísIn recent years, there has been a significant urge of interest in longitudinal and survival data modeling. This approach holds particular significance in cancer research, where it enables the evaluation of how longitudinal markers influence the event of interest. This paper aims to introduce practical estimation techniques for transition probabilities, conditional on observed covariates with repeated measurements. This innovation allows us to incorporate the trajectory of longitudinal outcomes into regression models by accommodating time-varying covariates for each individual. The results presented in this study confirm the superior efficiency of the proposed methods, which merge existing approaches for joint modeling of longitudinal and survival data with the landmark approach for estimating transition probabilities. These methods outperform approaches that do not fully account the information provided by longitudinal covariate measurements.Publication Restricted Access Development of a predictive score to discriminate community acquired pneumonia with underlying lung cancer: A retrospective case – control study2024-05-21 - Barbosa-Martins, João; Mendonça, Joana; Carvalho, Nuno; Carvalho, Carolina; Sarmento, Helena; Soutinho, Gustavo; Coutinho, Camila; Cotter, JorgeBackground: A pneumonic infiltrate might hide an occult lung cancer (LC). This awareness depends on each clinician personal experience, turning definitive LC diagnosis challenging and possibly delayed. In this study we aimed to develop a clinical score to better identify those cases. Materials and Methods: We conducted a retrospective case–control study, including previously undiagnosed LC patients admitted in our institution, with a presumptive suspicious of community acquired pneumonia (CAP). Cases were compared with random CAP inpatient controls, using a matched 2:1 ratio. Demographic, clinical, and laboratorial variables were assessed for a possible association with the presence of a CAP with underlying LC (CAP–uLC). Results: Among 535 hospitalized LC patients, 43 cases had a presentation compatible with CAP and were compared with 86 CAP controls. A scoring system was built using 6 independent variables, which positively correlated with CAP–uLC: smoking history (OR: 8.3 [1.9–36.2]; p=0.005); absence of fever (6.5 [2.0–21.5]; p=0.002); sputum with blood (5.9 [1.2–29.9]; p=0.033); platelet count ≥ 232x103/uL (5.8 [1.6–20.6]; p=0.006); putative alternative diagnosis than CAP (4.6 [1.5–14.7]; p=0.009); and duration of symptoms ≥ 10 days (3.7 [1.1–13.0]; p=0.037). Our score presented an AUC of 0.910 (95% CI, 0.852–0.967; p<0.001), a sensitivity of 88.1% and specificity of 84.7%, in predicting the risk of presenting a CAP–uLC, when set to a cutoff of 18. ConclusionWe propose a novel risk score aimed to aid clinicians identifying patients with CAP–uLC in the acute setting, possibly prompting early LC diagnosis.Publication Open Access Presmoothed estimators of the state occupation probabilities in multi-state survival data2024-06-07 - Meira-Machado, Luís; Soutinho, GustavoThe progress of a disease can be analyzed using multistate models. These models focus on two key parameters of interest: the transition hazard and the state occupation probabilities. The state occupation probabilities have been consistently estimated by the Aalen-Johansen estimator. This estimator is particularly well-suited for handling censoring and benefits from the Markov assumption in the underlying stochastic process. In some cases, these estimators may lead to estimators with higher variability. To mitigate this issue we propose alternative estimators that incorporate a preliminary estimation approach. We introduce also practical estimation techniques for the state occupation probabilities, considering covariate measures. We explore the finite sample behavior of the estimators through simulations. An application to breast cancer is included.Publication Open Access Flexible nonparametric estimation of conditional bivariate distributions for recurrent event [comunicação oral]2024-10-07 - Soutinho, Gustavo; Meira-Machado, LuísA main objective in recurrent event analysis is the estimation of the bivariate distribution function. This estimation is crucial across various fields, as it helps to better understand the patterns of recurring events and their underlying dynamics. The term ’gap time’ refers to the interval between consecutive occurrences of an event, and the bivariate distribution func- tion captures the joint probability distribution of two such gap times. Although substantial progress has been made in this area, many existing methods assume independent censoring and overlook the role of covariates. Therefore, this paper aims to introduce nonparametric methods for estimating the bivariate distribution function while accounting for covariates. The goal is to offer more accurate and relevant tools for analyzing recurrent event data, improving the interpretation and insights derived from such analyses.Publication Open Access Estimation in a three-state model with interval-censored data [comunicação oral]2024-12-14 - Soutinho, Gustavo; Meira-Machado, Luís; Azevedo , MartaIn many fields, including medical research, engineering, and the social sciences, analyzing time-to-event data is essential for uncovering underlying processes and facilitating decision-making. A common challenge in this analysis arises when events are confirmed to have taken place within specific time intervals, yet the exact timing within those intervals remains unknown, a phenomenon known as interval censoring. The focus is on a three-state progressive multi-state survival model where the intermediate state and/or the final state may be interval-censored. The primary aim is to estimate state occupation probabilities, which are crucial for understanding the dynamics of state transitions over time. Additionally, the estimation of the bivariate distribution of the gap times is considered. New estimation methods are introduced based on the Turnbull estimator of survival to address the challenges posed by interval-censored events. Imputation-based methods are also explored for estimating event times within the interval, such as using the midpoint, left-point, and right-point of the interval. Findings contribute to filling the gap in the literature regarding interval-censored multi-state models, providing valuable insights for researchers and practitioners dealing with such data.Publication Restricted Access Nonparametric estimation of the conditional bivariate distribution function of censored gap times2024-12-04 - Soutinho, Gustavo; Meira-Machado, LuísA major goal in recurrent events analysis is to estimate the bivariate distribution function. This estimation is crucial across various fields and applications, as it helps clarify the patterns of recurring events and their underlying patterns. ‘Gap time’ refers to the duration between consecutive occurrences of an event, while the bivariate distribution function represents the joint probability distribution of two such gap times. Despite significant advancements in this area, most existing methods assume independent censoring and neglect the impact of covariates. Therefore, the primary aim of this paper is to develop and introduce nonparametric estimation methods for the bivariate distribution function that incorporate covariate measures. This study seeks to provide more precise and applicable tools for analyzing recurrent event data, thereby enhancing the understanding and interpretation of such events in practical scenarios.