Statistics have entered the life sciences and they are here to stay. This science emerged in the XVIII century by the hand of the mathematicians Thomas Bayes and Pierre Simon Laplace and was used by Gregor Mendel to demonstrate his theory on genetic inheritance. Since the 90s with the appearance of large genomic studies, statistics has become a fundamental tool for the analysis and interpretation of biomedical data. "Today it is difficult to find a scientific article in the field of biomedicine that does not include statistical methodology" explains David Rossell, who performed his postdoctoral training at the Department of Biostatistics in one of the best cancer research centres in the US, namely the MD Anderson Cancer Center. Rossell is now head of the Biostatistics and Bioinformatics Core Facility at the Institute for Research in Biomedicine (IRB Barcelona).
Dr. Rossell, together with the scientist Donald Berry, from the aforementioned North American centre, and the ICREA researcher Omiros Papaspiliopoulos, from the Department of Economics at the Pompeu Fabra University, are co-organizers of the Barcelona Biomed Conference "Bayesian Methods in Biostatistics and Bioinformatics", which is supported by the BBVA Foundation. Brought together at the "Institut d'Estudis Catalans" from Monday 17 December till Wednesday 19 December, approximately twenty biostatisticians mainly from the US and Europe, where this science is well established, will explain to a multidisciplinary audience the virtues of statistics for biomedical applications. They will also present the current state of the discipline, the fields to which it is contributing, and the main obstacles to overcome in order to improve the interpretation of data.
The capacity to obtain and store colossal amounts of biomedical data requires professionals to analyse and interpret them correctly. "Our work consists of making the data talk, extracting relevant information from the huge amount of background noise generated by biology", explains Rossell. "Biostatistics is like a radar for biomedicine: it allows biologists to navigate in an immense ocean of information with only a small amount of light, saving them from inappropriate experiments and helping them to design consistent ones".
Biostatistics applied to clinical assays
One of the application fields for statistics is clinical assays. The North American Donald Berry is professor in the Department of Biostatistics at the University of Texas M. D. Anderson Cancer Center, where he has played a role in the use of methods to develop innovative, adaptive clinical trials. His research objectives include reducing the number of patients participating in clinical assays before the commercialisation of treatments. Traditionally, in a clinical trial half the patients receive the experimental treatment and the other half a standard treatment or placebo. Dr. Berry studies experimental designs that, on the one hand, assign a greater number of patients to treatments with greater therapeutic benefits and, on the other hand, avoid unnecessary experimentation with patients. "It is not merely a question of cost saving but also of patient ethics, because if we sell hope to participants in clinical trials then hope must be given and biostatistics is proving to be useful also for this purpose".
Clinical trial design and innovative experiments are the focus of the first of three main sections of the conference. The second is how biostatistics scrutinizes huge amounts of data and integrates the information from diverse sources in a context in which thousands of variables are measured but in a small number of patients. "This is one of the greatest challenges ahead for statistics. How to better look for the needle in the haystack and how to increase the degree of certainty of the results", says Rossell, whose interest lies in developing theoretical models and methodological applications in this field of research. The third and last section of the conference centres the experts on computation, that is to say, in putting into practice the mathematical models drawn up by statisticians. Again, the difficulty lies in finding effective strategies to perform calculations with huge amounts of data.