Title: NHLBI Biostatistics Workshop on Recent Advances and Challenges in Statistical Methods
Date: September 26-27, 2016 (starts at 8am)
Available on NIH Videocast: https://videocast.nih.gov/summary.asp?live=19948&bhcp=1 (Day 1) https://videocast.nih.gov/summary.asp?live=19952&bhcp=1 (Day 2)
This workshop has two main objectives: (a) to assess recent developments in statistical methods relevant to NHLBI studies; (b) to identify the major challenges and important issues related to these statistical and analytical methods. Given the rapid development of new technology and the growing need to analyze massive and complex (“big”)data, this workshop will especially focus on novel statistical models, computational issues for large data sets, and efficient and effective study designs. This workshop will bring together leading experts in biostatistics and big data, clinical trials, statistical genetics, statistical computing, and database specialists to present the recently developed statistical and analytical methods and to discuss the applications of these methods to NHLBI studies. The group will also identify the gaps in knowledge and recommend future methodological research directions which will meet the specific needs of future NHLBI studies. This workshop will be an excellent opportunity for statisticians, researchers and investigators who encountered these statistical and analytical issues to collaborate and develop novel methodological tools which can be applied to future NHLBI studies.
See the attached program for a full list of speakers, including Drs. Rob Califf, Daniela Witten, Marie Davidian, Xihong Lin, and Dave DeMets.
For more information, see the conference website: https://www.nhlbi.nih.gov/news/events/nhlbi-biostatistics-workshop-recent-advances-and-challenges-statistical-methods
Sponsored by: Office of Biostatistics Research, National Heart, Lung and Blood Institute, National Institutes of Health. Support for videocast is provided by the NIH/OD Office of the Associate Director for Data Science and the Big Data to Knowledge Program.