The País Vasco node, led by the researcher Irantzu Barrio Beraza (University of the Basque Country UPV/EHU), is formed by 18 members. Its main lines of research focus on the modeling of health-related quality of life, development and validation of predictive models in biosanitary, experimental and social sciences, as well as the development of statistical methodology in the modeling of data obtained from complex designs.

Researchers participating in the node

Principal Investigator: Irantzu Barrio Beraza (Universidad del País Vasco UPV/EHU)

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Main lines of investigation

The study of alternative proposals for statistical modeling of patient-perceived outcomes, such as health-related quality of life, is one of the areas of great interest whose use has proven to be highly valued in different areas of Medicine (Epidemiology). Some of the extensions that we propose to analyze within this area are: a) beta-binomial regression model; b) mixed-effects beta-binomial regression model that allows for any possible correlation structure in the data by random effects; c) multidimensional beta-binomial regression model; development of joint models for longitudinal variables with beta-binomial distribution.

Clinical prediction models play a fundamental role in decision making for hospital clinical practice. The development and validation of predictive models with a high predictive and discriminative capacity would allow medical practitioners to make decisions with a good level of certainty. The development of predictive models is based on statistical modeling, where the characteristics of the response variable, the relationship between predictors and response, longitudinal data and the treatment of non-random data losses are factors that determine the type of models to be used and their validity. The development of reliable and useful clinical prediction models requires prior experience in the statistical modeling of outcomes, both crude outcomes such as mortality or survival, and patient-perceived outcomes, and in the statistical treatment of loss to follow-up in longitudinal studies. Moreover, the real success of the development of predictive models lies in their applicability in clinical practice, for which it is necessary to implement them in computer tools with an intuitive graphical interface that is easy to use and manage, so that the results are obtained automatically and transparently for the clinician and can be used reliably in daily decision making.

Complex survey data are increasingly used today, especially in the context of official statistics. In this framework, complex survey data are usually obtained by sampling the population of interest for the survey, following some particular complex sampling design. This sampling process can be carried out in one or more sampling stages, for which the combination of techniques such as stratification and clustering is a very common practice. One of the most special features of complex sampling data, as compared to data obtained from simple random sampling, is that each individual in the population has a probability (other than 0) of being included in the sample. In this context, the application of usual statistical methods is not trivial, and therefore, it is necessary to develop new approaches that take into account the complex design from which the data have been obtained.

In the analysis of the genetic basis of infectious diseases of viral etiology, it is important to develop tools for the detection and analysis of polymorphisms that can be used as markers for disease progression. Data analysis and statistical modeling in this context requires the treatment of small samples, due to the low frequency of occurrence of some genes.

Mathematical modeling of the natural history of neurological diseases by means of stochastic simulation models with discrete events is a current topic of great interest in epidemiology. Knowing the appropriate distribution for each variable and calculating its parameters from the primary data poses difficulties due to the very nature of the variable. The validity of the models depends not only on the original data, but also on the distributions applied and their parameterization. In this sense, our work aimed at the mathematical representation of diseases requires the use of statistics so that the models meet the objective of translating epidemiological data into mathematical language.

Among the current lines of research of this node are:

  • Development of regression models based on the beta-binomial distribution for multidimensional and longitudinal modeling of patient-perceived outcomes (PRO).
  • Development of a theoretical framework for joint modeling of PRO and time-to-event data based on non-exponential family distributions, especially the beta-binomial distribution.
  • Development of predictive models of clinical course, mortality, survival or changes in health-related quality of life. Creation of technological tools for use in hospital clinical practice.
  • Proposal of variable selection for the development of predictive models with data from complex design surveys.
  • Development and comparison of scoring methods for questionnaires.
  • Discriminative capacity in survival models.
  • Analysis and evaluation of patient-centered strategies, interventions and clinical outcomes.
  • Detection of genetic markers for viral disease progression using statistical modeling methods.
  • Mathematical modeling of the natural history of neurological diseases using stochastic simulation models with discrete events.

We believe it is relevant to mention that, in addition to the methodological objectives that we have previously mentioned in each of the areas of interest in this node, many of these objectives have arisen as a result of research in the different areas of knowledge that justify them and that, in our opinion, should imply an interest in transferring this knowledge to society in general and to the different fields of application of Biostatistics in particular. Therefore, our main objectives are as follows:

(a) Dissemination of statistical techniques developed through publications of a general nature or those containing applications of the proposed methodology in the different fields of Medicine.

(b) Dissemination of statistical techniques developed through summer courses or general courses in hospitals or medical institutions.

(c) Organization of conferences for the dissemination of developed statistical techniques oriented to a non-specialized public.

Development of software that can be used in a friendly way by all those professionals of Medicine and Biology interested in the application of the developed statistical techniques.

Institutions present in the node

Biostatistics consulting activities

Varios miembros del equipo investigador tienen firmados convenios de colaboración estables en materia de asesoría en bioestadística con organismos públicos, a saber:

  • Convenio de Colaboración entre el Departamento de Matemática Aplicada y Estadística e Investigación Operativa de la UPV/EHU y el Hospital de Galdakao de Osakidetza-Servicio Vasco de Salud para el asesoramiento en metodología estadística. Desde Enero de 1997 se renueva anualmente (en vigor).

También se han firmado contratos puntuales de apoyo a la investigación en bioestadística a través de la OTRI con diferentes instituciones, a saber:

  • Delegación Territorial de Sanidad de Guipúzcoa
  • LEA ARTIBAI Ikastetxea S. C., Área de Tecnología de los Alimentos
  • Fundación Vasca de Innovación e Investigación Sanitarias (BIOEF)
  • Hospital de Basurto del Servicio Vasco de Salud / Osakidetza
  • Hospital Galdakao-Usansolo del Servicio Vasco de Salud / Osakidetza
  • Hospital de Cruces del Servicio Vasco de Salud / Osakidetza
  • Euskal Estatistika Erakundea / Instituto Vasco de Estadística  EUSTAT.

Además, existen una colaboración activa de los miembros del equipo investigador con grupos de investigación de departamentos de la Universidad del País Vasco (UPV/EHU), mediante la coautoría de los trabajos resultantes, a saber:

  • Genómica y Sanidad Animal.
  • Biología Vegetal y Ecología.
  • Zoología y Biología Celular Animal.
  • Inmunología, Microbiología y Parasitología.
  • Química Analítica.

Finalmente, algunos miembros participan asiduamente en los procesos de selección de personal en el área de bioestadística,  a través de la Fundación Vasca de Innovación e Investigación Sanitarias (BIOEF) o de los hospitales del Servicio Vasco de Salud, y como miembros/asesores de las comisiones de evaluación de la Oferta de Empleo Público realizada por el Servicio Vasco de Salud para la cobertura de plazas de apoyo a la investigación.

Entities and groups with which it collaborates

Servicio Vasco de Salud / Osakidetza

Fundación Vasca de Innovación e Investigación Sanitarias (BIOEF)

Instituto de Investigación en Servicios de Salud Kronikgune

Basque Center for Applied Mathematics – BCAM

Euskal Estatistika Erakundea / Instituto Vasco de Estadística EUSTAT

Grupo consolidado de investigación “Econometrics Research Group” de la Universidad del País Vasco (UPV/EHU)

Grupo consolidado “Genómica y Sanidad Animal” de la Universidad del País Vasco (UPV/EHU)

Grupo consolidado de investigación en Modelización matemática y estadística aplicada y optimización (MATHMODE) del Gobierno Vasco

Grupo de investigación en Inferencia Bayesiana de la Universidad Nacional de Colombia (Bogotá, Colombia)

Proyecto “Uso de proyectores en la caracterización en el Modelo Lineal General Mixto” financiado por el Consejo Nacional de Ciencia y Tecnología (CONACYT) y promovido por la Universidad Veracruzana (Veracruz, México)

Red de Investigación en Servicios Sanitarios en Enfermedades Crónicas

Red de Investigación en Cronicidad, Atención Primaria y Promoción de la Salud (RICAPPS)

List of node members

  • Irantzu Barrio Beraza

    Principal InvestigatorComité EjecutivoUniversidad del País Vasco UPV/EHU
  • Amaia Iparragirre Letamendia

    Universidad del País Vasco UPV/EHU
  • Arantzazu Arrospide Elgarresta

    Hospital Alto Deba
  • Begoña Marina Jugo Orrantia

    Universidad del País Vasco UPV/EHU
  • Cristina Galán García Arcicollar

    Basque Center for Applied Mathematics (BCAM)
  • Diana Pérez Valencia

    Basque Center for Applied Mathematics (BCAM)
  • Dorleta García Rodríguez

    Centro Tecnológico AZTI
  • Inmaculada Arostegui Madariaga

    Universidad del País Vasco UPV/EHU
  • Javier Mar Medina

    Hospital Alto Deba
  • José Maria Quintana López

    Hospital Galdakao-Usansolo
  • Joseph E. Cavanaugh

    The University of Iowa (Estados Unidos)
  • Josu Najera Zuloaga

    Universidad del País Vasco UPV/EHU
  • Leire Citores Martínez

    Centro Tecnológico AZTI
  • Lore Zumeta Olaskoaga

    Basque Center for Applied Mathematics (BCAM)
  • Maider Mateo Abad

    Instituto de Investigación Sanitaria Biodonostia
  • Rolando de la Cruz Mesia

    Universidad Adolfo Ibañez (Chile)
  • Urko Aguirre Larracoechea

    Hospital Galdakao-Usansolo
  • Vicente Núñez Antón

    Universidad del País Vasco UPV/EHU