TheCenter for Biostatistics in AIDS Research(CBAR), an organization within the Harvard T. H. Chan School of Public Health, is responsible for the design, monitoring and statistical analysis of clinical trials and observational studies for several major national and international NIH-funded clinical research networks. Successful applicants will join over 100 statisticians and epidemiologists including Harvard faculty, research scientists and staff. Investigators within CBAR have provided statistical leadership and support for clinical research networks for more than twenty five years. Results from studies have helped to establish the paradigm for the treatment and prevention of HIV and other infectious diseases, and contributed to international treatment and prevention guidelines.
CBAR currently has openings for two Biostatistician I/II to act as a protocol statistician with either the International Maternal Pediatric Adolescent AIDS Clinical Trial Group (IMPAACT) network or the AIDS Clinical Trial Group (ACTG). The Biostatistician I position is suitable for a new graduate with a Master's degree in biostatistics or a related field with limited clinical trials work experience. CBAR provides a strong program for growth and professional development for Master's-level Biostatisticians including a defined career path and opportunities for continuing education and attendance of professional meetings.
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About Harvard T.H. Chan School of Public Health - CBAR
Founded in 1995, The Center for Biostatistics in AIDS Research (CBAR) within the Department of Biostatistics at the Harvard T.H. Chan School of Public Health is responsible for the design, monitoring, and analysis of numerous federally funded clinical trials of treatment and prevention trials for HIV infection and other infectious diseases, both within the US and internationally.
Our current re...search focuses on methods for patient-focused outcome measures that integrate efficacy and safety outcomes, for competing risks data, for personalizing treatment, and for comparison of technologies (e.g., point of care versus gold standard technologies). We also have a particular focus on the development and application of sophisticated epidemiological models for evaluating treatment benefits accounting for confounding by indication, such as the use of marginal structural models, mediation analysis, and other causal inference approaches.