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Clelia Di Serio, PhD - "Big data and biomedicine, from reproducibility to causal interpretation. A statistical challenge"

University Centre for Statistics in the Biomedical Sciences (CUSSB), Vita-Salute San Raffaele University, Milan – Italy
When Feb 13, 2019
from 12:00 PM to 01:30 PM
Where Tigem, Vesuvius Auditorium
Contact Name
Contact Phone 081-19230659
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Short CV

Reproducibility of science nowadays is one of the major challenge for modern biomedical research. Indeed, dealing with big data from OMICS and NGS technologies or data from electronic medical records (EMR) leads to the crucial question: how to translate “big data” in “big information”? Issues like rapid evolution of technologies or low representativeness of sample from big surveys make very often results not comparable with other studies. Literally reproducibility is the ability of an entire experiment or study to be replicated. The definition looks straightforward but it curries lots of implications.  First one should consider that replicability is not synonym of repeatability. Repeatability means that an experiment can be repeated under same conditions and under same measurement procedure and using same analytic methods. Replicability has much to do with “generalizability” of results that are also expected to be replicable by independent data, analytical methods, laboratories and technology, thus in presence of different sources of variation both with small samples (basic research) or large sample (epidemiological and clinical studies). Thus, the strict connection with statistical inference paradigm is immediately seen. Since medical studies  - both in basic and clinical research - are commonly used to quantify effects of risks factors on simple or complex outcomes that should be of interest for general health and personalized cures, it is easily seen how replication is of critical importance where results can inform substantial policy decisions: However, general consideration about sustainability of research concerning time, expense, and rapid evolution of biomedical technology make very commonly impossible to fully replicate studies. Approaching issues on replicability in a fully statistical perspective may help in introducing corrections in statistical terms through appropriate statistical procedures, thus reducing costs of repeating experiments and studies. In this contribution we will consider statistics as acting in two different main directions:  first as a tool to “evaluate replicability” of a study when change in the biomedical techniques could dramatically affect comparability of longitudinal data; second as a method to “induce replicability “. Two examples are shown from gene therapy data and a big observational study on prostate cancer.

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