Projects
My research has benefited from solid and sustained competitive support, reflecting the scientific and social relevance of the lines of work I have pursued. I have led and participated in numerous research projects at the international, European, national, and regional levels, both through competitive calls for proposals and through contracts and agreements with public and healthcare institutions. These projects have focused on methodological development in statistics and econometrics applied to health, as well as on the analysis of the effects of climate change, socioeconomic and environmental inequalities, and the dynamics of diseases such as COVID-19. The funding obtained has allowed me to consolidate research teams, strengthen international collaborations, and facilitate the effective transfer of knowledge to public health policies
Competitive Projects
Methodogical Lines
The main methodological lines in which I have worked are presented below. The statistical and econometric methods that I have developed in each of them are applied in the research lines mentioned above.
Multivariate survival analysis: Survival analysis consists of a set of methods that analyse the time until an event of interest occurs. However, there are situations in which subjects may experience several events during the observation period (for example, recurrences of a disease). In these cases, a different methodology from that used in standard survival analysis is required, as it allows the problems caused by the presence of multiple events to be addressed.
Spatiotemporal models: Spatiotemporal models aim to explore, describe, visualise, and analyse data while considering their distribution both in space—usually expressed through the use of geographic coordinates—and in time. These models also consider the interaction between both dimensions, that is, the temporal variation of spatial dependence.
Mixed models: In many fields, data frequently appear in the form of clusters, that is, grouped and related observations. In fact, the two basic situations that produce clustered data structures are hierarchical random processes and repeated measures or longitudinal data. Mixed models aim to study and implement a set of methods to handle and analyse these clusters.
Bayesian statistics: Bayesian statistics comprises a set of techniques in which probability is considered subjective, parameters are treated as random variables, and Bayes’ theorem is used to update uncertainty—that is, to infer the probability that a hypothesis may be true—and therefore to support decision-making.