Soutinho, Gustavo
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Soutinho
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Gustavo
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Gustavo
Biografia
Gustavo Domingos da Costa Coelho Soutinho
Docente do Departamento de Ciência e Tecnologia da Universidade Portucalense.
Identificadores
0918-604C-2C04
57195326662
GSE-1063-2022Projetos de investigação
Unidades organizacionais
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.
16 resultados
Resultados da pesquisa
A mostrar 1 - 10 de 16
Publicação Acesso Restrito Imputation of the response variable in survival analysis with Interval-Censored Data2025-07-28 - Soutinho, Gustavo; Meira-Machado, LuísHandling interval-censored data in survival analysis presents signi cant challenges, as the exact time to the event is only known to fall within prede ned intervals. Common imputation strategies, such as those that use the lower bound, upper bound, or midpoint of the interval, often fail to capture the inherent uncertainty in the data, leading to biased or imprecise estimates. Prior studies have demonstrated the limitations of these approaches, particularly in accurately estimating survival probabilities and hazard ratios. To tackle these issues, we propose the Scaled Linear Redistribution Method, a new imputation technique aimed at overcoming the limitations of existing methods. The method redistributes imputed values within the interval, keeping their variation and basic statistical behavior. While our approach has not yet been implemented, it represents a promising direction for future research. We plan to evaluate its performance through a comprehensive simulation study, comparing its performance to that of traditional imputation methods and the Turnbull estimator, a widely used nonparametric method for interval-censored data.Publicação Acesso Restrito 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.Publicação Acesso Restrito 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.Publicação Acesso Aberto Beyond Kaplan-Meier: A comprehensive R Package for Interval-Censored Survival Analysis using Turnbull’s Approach2025-07-28 - Azevedo, Marta; Soutinho, Gustavo; Meira-Machado, LuísInterval-censored data frequently arise in survival analysis when the exact time of an event is unknown but is known to occur within a specific time interval. Traditional methods like the Kaplan-Meier estimator are inadequate for such data, necessitating specialized approaches. This paper presents an R library designed to handle interval-censored data, emphasizing the use of Turnbull’s estimator for nonparametric survival estimation. The package offers flexible functionalities, including the calculation of survival estimates, the generation of both static and interactive plots, and the construction of bootstrap-based confidence bands. Additionally, the library provides users with detailed outputs such as Turnbull intervals and their corresponding weights, which are instrumental in understanding the survival distribution and serve as an analogue to Kaplan-Meier weights in right-censored contexts. These weights enable the extension of survival analysis methods to more complex models, including multi-state frameworks. The practical utility of the library is demonstrated using real-world datasets, highlighting its potential to support advanced survival analysis and foster the development of new estimators beyond traditional survival probabilities.Publicação Acesso Aberto Estimation of the bivariate distribution function for interval censored data [comunicação oral]2025-10-23 - Soutinho, Gustavo; Azevedo, Marta; Meira-Machado, LuísAnalyzing time-to-event data is crucial across fields like medicine, engineering, and social sciences to understand underlying processes and support decisionmaking. A common issue is interval censoring, where events are known to occur within specific intervals, but their exact timing is unknown. In some studies, individuals may experience multiple events, and the time between them—known as gap times—is of particular interest. While much research focuses on right-censored event times, few studies address scenarios where one or both events are interval censored. This paper introduces new estimation methods that are based on the Turnbull estimator of survival, aimed at addressing the gap in literature regarding intervalcensored events. Specifically, we explore the possibility of comparing the new estimators with methods based on the imputation of the event time. These imputation methods include estimating the event time as the midpoint of the interval, the point to the right of the interval, and the point to the left of the interval. Through empirical evaluation and simulation studies, we aim to provide insights into the relative performance and suitability of these estimation approaches for analyzing gap times with interval-censored data.Publicação Acesso Aberto 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.Publicação Acesso Aberto Estimation in a three-state model with interval-censored data [abstract]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.Publicação Acesso Aberto 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.Publicação Acesso Aberto 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.Publicação Acesso Restrito Pre-Smoothing methods for transition probabilities in Complex Non-Markovian Multi-State Models2025-07-01 - Soutinho, Gustavo; Meira-Machado, LuísMulti-state models are essential tools in longitudinal data analysis, enabling the estimation of transition probabilities that provide predictive insights into clinical outcomes across stages of disease progression or recovery. Conventional approaches to inference in these models often rely on the Markov assumption, which simplifies computation but may not hold in complex real-world settings. To address this limitation, we extend the landmark Aalen-Johansen estimator by incorporating presmoothing techniques, offering a robust alternative for estimating transition probabilities in non-Markovian multi-state models, including those with multiple states and reversible transitions. The proposed method effectively reduces estimation variability and mitigates biases arising from the selection of arbitrary landmark times. Through empirical evaluation using three real-world datasets with distinct multi-state structures, we demonstrate that the presmoothed estimator achieves enhanced precision and stability, particularly in the presence of high noise or small sample sizes. To facilitate its application, we provide an R package, presmoothedTP, which implements all the proposed methods.