Research - Laboratory/Non-Laboratory, Staff/Administrative
Research Associate, Cancer Prevention and Control Program's Modeling Core - Georgetown University Medical Center
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Research Associate, Cancer Prevention and Control Program's Modeling Core- Georgetown University Medical Center
We are seeking to hire a Research Associate, who will assist the Director of Comparative Effectiveness and Modeling Research with projects in the Cancer Prevention and Control Program's Modeling Core. Such projects include several large NIH grants focused on modeling policy options for cancer screening, diagnosis and treatment in the US population over time. The Modeling Core also has projects evaluating genomic applications in breast cancer treatment and exploring the impact of provider, patient, and system factors on development of cost-effective strategies for the delivery of community personalized cancer care. Moreover, investigators have recently been funded under a very large $2.4 million NIH grant for several new projects beginning next fall which are of increasing complexities and will require very specialized statistical, epidemiologic, and modeling expertise to create new model input parameters on cancer recurrence and progression, assumptions on DCIS, polygenic risk, emerging imaging technologies, and information on genomic applications. The new grants involve a large number of international and national collaborations and large, complex, diverse data sets. The Research Associate will obtain and manage data, conduct analyses, make inferences, produce reports, coordinate projects, overseeing tasks and enforcing timelines and deliverables. The Research Associate will develop data inputs for the national policy simulation models through literature reviews, data abstraction, analysis and synthesis of results for publication. The results of this research may be used to influence clinical practice guidelines, conduct comparative effectiveness analysis and support cost-effectiveness research.
This position reports to the Director of Comparative Effectiveness and Modeling Research, who oversees research by Drs. Mandelblatt and Chang who work in the Modeling Core. This also involves interacting with multiple national and international research teams (i.e., via CISNET). The individual in this position will be responsible for:
complex and multifaceted research analyses and data management for the two above-mentioned PIs
documenting all activities and data needed for six modeling teams at both Georgetown and around the country
conducting literature searches and abstracting and synthesizing data
preparing graphs and tables of results for presentation
preparing data requests
conducting data management and analysis
assisting with maintaining the project website
writing reports, meeting minutes, and other documents as assigned
coordinating conference calls and meetings
assisting with new grant preparation.
The impact of this position on research is paramount to the projects' ability to complete the multi-investigator research as planned.
This body of research includes very high profile publications that have been and will continue to be used to inform US screening and treatment guidelines (e.g., breast cancer guidelines of the US Preventive Services Task Force), so this position is critical to coordinating these projects and completing the work.
An individual trained at a minimum of the Masters level in epidemiology, biostatistics, modeling, cost- and comparative-effectiveness or health services/policy research, plus 1-2 years of experience. This can be a MPH, MS, or MHS level, but doctoral students encouraged to apply.
Computer literate in such applications as MSWord, PowerPoint, RefMan, desktop publishing
Experience working with health data and large national datasets such as SEER
Proficiency in SAS and/or STATA, SAS programming or the equivalent work experience with SAS
Strong statistical analysis skills essential
Experience writing scientific papers and performing literature reviews
Detail orientation and organization essential
Ability to interact with large teams of collaborators
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