Arantzazu Arrospide Elgarresta studied mathematics in the University of the Basque Country (UPV/EHU) and works as a biostatistician in the Research Unit of Integrated Health Organisations in Gipuzkoa. This research unit gives support to four regional hospitals (about 100 beds each one) and all the public Primary Care Health Services in Gipuzkoa.
Acting teacher at the Department of Applied Mathematics, Statistics and Operational Research of the University of the Basque Country (UPV/EHU)
Both young biostatisticians are currently working on several ongoing research projects. They belong to the Health Services Research on Chronic Patients Network (REDISSEC) – among others biostatisticians – and tell us what they think about Biostatistics.
1. Why do you like Biostatistics?
Irantzu Barrio: On one hand I like applying statistics to real problems, data sets and experiments. On the other hand, I like developing methodology which can contribute to get better results and conclusions in each research project. In addition, I feel lucky to work in multidisciplinary teams. This allows me to learn a lot from other areas and constantly improve on mine own, always looking for ways to provide solutions to other researchers needs.
Arantzazu Arrospide: I think Biostatistics is the link between mathematics and the real world, giving us the opportunity to feel part of advances in scientific research.
2. Could you give us some insight in your current field of research?
AA: Our main research line is the application of mathematical modeling the evaluation of public health interventions, especially economic evaluations. Although Markov Chain models are the most common methods for this kind of evaluations we work with discrete event simulation models which permit more flexible and complex modeling.
IB: I’m currently working on my PhD thesis. One of the main objectives of this work is to propose and validate a methodology to categorize continuous predictor variables in clinical prediction model framework. Specifically we have worked on logistic regression models and Survival Models.
3. You have been doing an internship abroad. What was the aim of your stay?
IB: I did an internship in Guimaraes at the University of Minho, Portugal. During my stay, I worked together with Luis Filipe Meira Machado and María Xosé Rodriguez-Alvarez. The aim was to learn more about survival models and extend the methodology developed so far, considering different prediction models.
AA: I did a short stay in the Public Health department of the Erasmus Medical Centre in Rotterdam (Netherlands) last November. The aim of the visit was to discuss the validation of a discrete event simulation model developed to estimate the health effects and costs of the breast cancer screening program in the Basque Country.
4. What did allow you to do that was has not been possible in Spain?
IB: Oh! It’s amazing when you realize you have all your time to work on your research project, one and a unique priority for more than two months. Of course, all the other to do’s did not disappeared from my calendar, only were postponed until my return to Bilbao. And, in addition to that, it was also a privilege to work together with high experienced biostatisticians and to have the opportunity to learn a lot from them.
AA: The research group I visited, internationally known as the MISCAN group, is the only European member of the Cancer Intervention and Surveillance Modeling Network (CISNET) created by the National Cancer Institute in the United States. Their main objective is to include modeling to improve the understanding of the impact of cancer control interventions on population trends in incidence and mortality. These models then can project future trends and help determine optimal control strategies. Currently, Spanish screening programs evaluation is mainly based on the quality indicators recommended by the European Screening Guidelines which do not include a comparison with an hypothetical or estimated control group.
5. Which are the most valuable aspects to highlight during your internship? What aspects do you believe that might be improved?
IB: I would say that my internship was simply perfect. When I came back to Bilbao I just thought time had gone really really fast. I’m just looking forward to go back again.
AA: This group works for and in collaboration with their institutions. They are the main responsible of evaluation of ongoing screening programs, prospective evaluation of screening strategies and leaders for new randomized trials in this topic. This is the reference group in the Netherlands for cancer screening interventions and their institutions consider their conclusions when making important decisions.
6. What do you think of the situation of young biostatisticians in Spain?
AA: When you work in a multidisciplinary research group both methodological and disease specific knowledge are essential and it takes a long time to achieve it. Institutional support is necessary to obtain long term funds that would ensure future benefits in healthcare research based on rigorous and innovative methods.
IB: I think the situations for young biostatisticians and for young people in general is not easy right now. And at least for what I see around me, there is lot of work to do for.
7. What would be the 3 main characteristics or skills you would use to describe a good biostatistician? And the main qualities for a good mentor?
AA: Open minded, perfectionist and enthusiastic. As for the mentor, he/she should be strict, committed and patient.
IB: In my opinion good skills on statistics, probability and mathematics are needed. But at the same time I think it is important to be able to communicate with other researchers such as clinicians, biologists, etc, specially to understand which are their research objectives and be able to translate bio-problems to stat-problems.
For me it is very important to have good feeling and confidence with your mentor. I think that having that, everything else is much easier. On the other hand, if I had to highlight some qualities, I would say that a good mentor would: 1) Contribute with suggestions and ideas 2) Supervise the work done and 3) be a good motivator.
8. Finally, is there any topic you would like to see covered in the blog?
IB: I think the blog is fantastic, there is nothing I missed in it. I would like to congratulate all the organizing team, you are doing such a good job!!! Congratulations!!!
AA: Although it is not considered part of statistical science operational research methods also can be of interest in our researches.
Selected publications (6):
Arrospide, A., C. Forne, M. Rue, N. Tora, J. Mar, and M. Bare. “An Assessment of Existing Models for Individualized Breast Cancer Risk Estimation in a Screening Program in Spain.”. BMC Cancer 13 (2013).
Barrio, I., Arostegui, I., & Quintana, J. M. (2013). Use of generalised additive models to categorise continuous variables in clinical prediction. BMC medical research methodology, 13(1), 83.
Vidal, S., González, N., Barrio, I., Rivas-Ruiz, F., Baré, M., Blasco, J. A., … & Investigación en Resultados y Servicios Sanitarios (IRYSS) COPD Group. (2013). Predictors of hospital admission in exacerbations of chronic obstructive pulmonary disease. The International Journal of Tuberculosis and Lung Disease, 17(12), 1632-1637.
Quintana, J. M., Esteban, C., Barrio, I., Garcia-Gutierrez, S., Gonzalez, N., Arostegui, I., Vidal, S. (2011). The IRYSS-COPD appropriateness study: objectives, methodology, and description of the prospective cohort. BMC health services research, 11(1), 322.
Mar, J., A. Arrospide, and M. Comas. “Budget Impact Analysis of Thrombolysis for Stroke in Spain: A Discrete Event Simulation Model.”. Value Health 13, no. 1 (2010): 69-76.
Rue, M., M. Carles, E. Vilaprinyo, R. Pla, M. Martinez-Alonso, C. Forne, A. Roso, and A. Arrospide. “How to Optimize Population Screening Programs for Breast Cancer Using Mathematical Models.”.