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Yale University, Yale School of Public Health
300 George Street
New Haven, Ct. 06510
To provide expertise in the design, analysis, and interpretation of ongoing and planned observational studies, clinical trials, and basic research in a highly productive Cardiology Research Center.
1. Evaluates and analyzes data using accepted statistical and biostatistical techniques. 2. Investigates, analyzes, and evaluates complex statistical and programming problems. Determines proper methodology, testing standards, and evaluation processes for research projects. Recommends and develops statistical approaches for use in analyses. 3. Prepares analysis plans and writes detailed specifications for analysis files, consistency checks, tables, and figures; communicates with clients regarding statistical analysis issues. 4. In collaboration with research investigators, contributes to the design of research studies, develops analytical plans, conducts statistical analysis and interpret the results. 5. Ensures the integrity of databases used in analyses through development of essential data cleaning and checks, and data back-ups. 6. Plans statistical programming activities and schedules to provide investigators with time frames for projects. 7. Recommends and develops statistical approaches by testing and prototyping. 8. Organizes and creates documents and tables related to datasets; communicates with data sources about data accuracy and data dictionary. 9. May perform other duties as assigned.
Master’s Degree in Biostatistics, Statistics or relevant field. Two years of experience; or equivalent combination of education and experience.
Ability to communicate well, both orally and in writing, and to work well with investigators having differing clinical and epidemiologic backgrounds (i.e., physicians with little to no understanding of biostatistics). Ability to work independently and creatively in developing testable hypotheses.
Ability to set working priorities among a number of conflicting demands and work concurrently on several projects.
In depth working knowledge one major statistical package (i.e., SAS, R, STATA) and willingness to learn others (i.e., SPSS). Demonstrated ability in longitudinal modeling using both categorical and continuous outcomes.
High level proficiency with models typically used in biomedical research such as generalized linear models with random effects, mixed models, survival models, approaches to missing data, etc.
Preferred Education, Experience and
PhD in Biostatistics, Statistics or relevant field with three years of experience in medicine or biological sciences, is strongly preferred.