Rationale: The confluence of online social media, smart phones, and sensor technology is giving rise to a tidal wave of digital health interventions that have vast potential for improving health at low cost. As with consumer software, digital interventions are more likely to be effective if user feedback and determination of effectiveness is sought early and often. At present, however, researchers face high costs and difficulty in developing app ideas into prototypes, testing prototypes on real users, and validating the intervention.

Rationale: UCSF and the Benioff Children's Hospital have been selected by the national Children's Oncology Group Consortium (COG) of the National Cancer Institute (NCI) to conduct new Phase I Pediatric Oncology studies. Patients enrolled in these studies who are not admitted to the Pediatric Clinical Research Center (PCRC) Inpatient Unit, will require evening and weekend support for study procedures, including timed blood draws, sample processing and EKGs.

Rational:

CTSI’s Consultation Services Data Management Unit (DMU) is just one of the successful research resources available on campus to help UCSF faculty and staff with his/her research.  Along with many other data management services, the DMU also provides consultation in data processing to help transform research data into a statistically analyzable format.  In our efforts to provide consultation to the UCSF research community, we have realized that some trainees and faculty do not have the programming skills or resources to perform data processing tasks that are necessary to keep their research moving forward.  Complicated data merges and transformations require specific programming expertise to ensure that it is performed correctly.  Data management programmers, who can devote more time and effort to small projects, are needed.  The DMU’s ability to provide these services is very limited.  As a result, we have identified a gap in services that the Data Management Unit is currently able to provide.

Rationale:  Inaccurate sample size estimation leads to research studies that enroll too few or too many study participants. The former can result in failure to demonstrate anticipated effects and the latter to excessive costs, due to overuse of research participants and personnel time. While there are several important components of a good sample size calculation – including a compelling research question, an appropriate outcome variable, and an efficient study design – here we focus on improving the accuracy of the quantitative inputs.