7th Annual Summer Institute in Statistics for Clinical & Epidemiological Research (SISCER)

The Summer Institute in Statistics for Clinical & Epidemiological Research (SISCER) offers introductory and advanced short courses in methods for clinical research and epidemilogy. Participants will find learning opportunities for clinical trials, biomarkers, observational data, precision medicine, and Bayesian biostatistics. Kathleen Kerr serves as the director of SISCER.

Posted: Friday, March 6, 2020

In light of growing concerns around COVID-19, we have postponed opening registration for the Summer Institutes until April 30. The health and safety of Summer Institutes participants is a top priority and we will be working with UW officials and government health agencies to monitor the potential impact of Coronavirus (COVID-19) on this year’s program. We will do our best to avoid cancelling the institutes, but will do so if deemed appropriate.

Details

Dates: July 27-31, 2020

  • 2020 SISCER Module Schedule;

  • 2020 module fees (note fees do not include meals and participants are responsible for their own travel and lodging).

    • Half-day module: General fee is $225 per module ($200 before June 30) and the academic/government/non-profit rate is $185 per module ($160 before June 30). 

    • 1-day module: General fee is $450 per module ($400 before June 30) and the academic/government/non-profit rate is $370 per module ($320 before June 30). 

    • 1.5-day module: General fee is $675 per module ($600 before June 30) and the academic/government/non-profit rate is $555 per module ($480 before June 30). 

    • 2-day module: General fee is $900 per module ($800 before June 30) and the academic/government/non-profit rate is $740 per module ($640 before June 30). 

    • Registrations paid via UW budget number receive ~13% discount. 

  • Sign up for the Summer Institutes email list.

The goal of SISCER is to strengthen the statistical proficiency and career development of scholars from all backgrounds, especially those from groups historically underrepresented in STEM such as racial and ethnic minority groups, low income, first generation college students, veterans, and differently abled and 2SLGBTQ groups.