The mission of the QSU is to facilitate cutting-edge scientific studies initiated by Stanford investigators by providing expertise in biostatistics and informatics, to mentor and educate clinical investigators in research methods, and to mentor data scientists so that they can reach their full potential. The QSU achieves its mission through an interdisciplinary collaborative approach where QSU members become fully integrated into individual research teams.
More About the QSU:
The QSU was created in response to an increasing demand for statistical support and data coordination. QSU members are statistical scientists with expertise in missing data, prediction modeling, statistical computing, database creation, and software development. The QSU currently collaborates on over 100 large-scale scientific projects. We believe that to further science, it is important to facilitate the flow of data to investigators as efficiently as possible. We are also committed to providing sound analyses in a timely fashion. Finally, we believe that interdisciplinary research is best achived by full integration of all investigators. Our members become an integral part of each research team in which we are involved.
Our specific goals are to:
- Design studies that optimize the ease of interpreting results
- Provide high quality data analysis using modern statistical techniques
- Develop new or adapt old methods for optimal analysis as the need arises
- Securely house and track data in a HIPAA-compliant and IRB-compliant manner
- Create user-friendly publicly available software for recommended methods
- Interpret results
- Disseminate findings
- Mentor clinical investigators in research methods
Stanford has a rich history of doing high quality science. We are excited to be part of that process.
Stanford Date Science Resources (DASHER):
The QSU is part of a larger collection of data science resources within the Stanford School of Medicine. To learn about all these data science resources and to initiate a consultation, please visit the Stanford Data Science Resources web portal.
Gardner CD, Trepanowski JF, Del Gobbo LC, Hauser ME, Ridgon J, Ioannidis JPA, Desai M, King AC.
Collins MK, Ding VY, Ball RL, Dolce DL, Henderson JM, Halpern CH.
Heit JJ, Ball RL, Telischak NA, Do HM, Dodd RL, Steinberg GK, Chang SD, Wintermark M and Marks MP.