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.

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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.

Resultados da pesquisa

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  • PublicaçãoAcesso Aberto
    Estimation of the transition probabilities conditional on covariates with repeated measures: A joint modeling approach
    2024-06-07 - Soutinho, Gustavo; Meira-Machado, Luís
    In 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.