Statistical Methods for Big Data
Big Data” are being promoted as a revolutionary development in the future of health research. They are increasingly available in different fields, from molecular to environmental epidemiology to pharmaco-epidemiology to public policy. Different applications encounter different challenges, for example high-dimensionality of populations, exposures, time points, or locations, or of a combination of these. Distinctions between “made big data” (such as those derived from -omics platforms) and “found big data” (such as those obtained from linkage across electronic health records and administrative databases) may be useful to identify and address the challenges posed by these novel sources of information.
This symposium will aim to discuss these features and the challenges that they pose for statistical methods and future directions of research. Speakers from different methodological perspectives will present examples across a wide spectrum of applications.
- Elizabeth Williamson (LSHTM)
- Stephen Evans (LSHTM)
- Pietro Ferrari (IARC, Lyon)
- Joel Schwartz (Harvard T.H. Chan School of Public Health)
- Jas Sekhon (University of California, Berkeley)
Followed by a panel discussion and drinks reception.