The BERD Core comprises biostatistics faculty and staff who have methodological expertise in the full spectrum of clinical and translational research including laboratory studies, clinical trials, epidemiological studies, and electronic health records (EHR) based research. They provide consultation and collaboration on study design, protocol and grant development, statistical analysis, and development of novel methodologies. The core also offers a variety of training opportunities in statistics and data science to the Einstein-Montefiore community.
The BERD program goals are to:
- Provide accessible and high-quality expertise and support in biostatistics, bioinformatics, epidemiology, and research design to maximize the rigor, reproducibility, and impact of clinical and translational research, and facilitate research that promotes health equity.
- Develop, implement, and disseminate novel statistical methods and software to advance translational science, with emphasis on innovative clinical trial designs, analyses of high dimensional data from new technologies, and causal inference methods for observational studies.
- Expand biomedical data science capabilities to provide seamless support for the extraction, integration, and analysis of large and complex data sets.
- Provide educational and training opportunities in statistics and data science for researchers and clinical research staff of all levels.
- Strengthen partnerships with other regional and national CTSA BERD groups and the CTSA consortium to share resources, to identify opportunities for inter-institutional collaborations, and to disseminate statistical advances to the broader scientific community.
Services:
- Study design
- Developing clinical and translational research protocols
- Methodological support for grant applications
- Clinical trials methodology
- Population-based and EHR research
- Bioinformatics, statistical genetics, and genomics
- Data analysis
- Novel statistical methodologies
- Biostatistics workshops and courses
Statistical assistance is also available without appointment through the virtual walk-in Statistics Consulting Center that operates every Tuesday between 3 - 5 pm via the following zoom link: https://einsteinmed.zoom.us/j/96403655408
An online biostatistics resource managed by the BERD core. This resource includes guidelines for preparing a data set, statistical tools, links to statistics software, statistics training opportunities, and other statistics resources.
Leadership:
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![Mimi Kim, Sc.D.]()
Director, Biostatics, Epidemiology & Research Design Core
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![Melissa J. Fazzari, Ph.D., M.S.]()
Our Impact
Recent BERD methodology publications:
Fazzari MJ, Kim MY. Subgroup discovery in non-inferiority trials. Stat Med. 2021 Oct 30;40(24):5174-5187. doi: 10.1002/sim.9118. Epub 2021 Jun 22. PMID: 34155676.
Kim M, Wang C, Xue X. Assessing the influence of treatment nonadherence on noninferiority trials using the tipping point approach. Stat Med. 2019 Feb 20;38(4):650-659. doi: 10.1002/sim.7999. Epub 2018 Oct 28. PMID: 30368844.
Xue X, Qi Q, Sotres-Alvarez D, Roesch SC, Llabre MM, Bainter SA, Mossavar-Rahmani Y, Kaplan R, Wang T. Modeling daily and weekly moderate and vigorous physical activity using zero-inflated mixture Poisson distribution. Stat Med. 2020 Dec 30;39(30):4687-4703. doi: 10.1002/sim.8748. Epub 2020 Sep 18. PMID: 32949036; PMCID: PMC8521567.
Kim RS, Shankar V. Prevalence estimation by joint use of big data and health survey: a demonstration study using electronic health records in New York city. BMC Med Res Methodol. 2020 Apr 6;20(1):77. doi: 10.1186/s12874-020-00956-6. PMID: 32252642; PMCID: PMC7137316.
Kim RS, Shankar V. Prevalence estimation by joint use of big data and health survey: a demonstration study using electronic health records in New York city. BMC Med Res Methodol. 2020 Apr 6;20(1):77. doi: 10.1186/s12874-020-00956-6. PMID: 32252642; PMCID: PMC7137316.
Liu Y, Wang T, Zhou B, Zheng D. Robust integration of multiple single-cell RNA sequencing datasets using a single reference space. Nat Biotechnol. 2021 Jul;39(7):877-884. doi: 10.1038/s41587-021-00859-x. Epub 2021 Mar 25. PMID: 33767393; PMCID: PMC8456427.
Zhu X, Li X, Xu R, Wang T. An iterative approach to detect pleiotropy and perform Mendelian Randomization analysis using GWAS summary statistics. Bioinformatics. 2021 Jun 16;37(10):1390-1400. doi: 10.1093/bioinformatics/btaa985. PubMed PMID: 33226062; PubMed Central PMCID: PMC8208738
Lee, S., Bagiella, E., Vaughan, R., Govindarajulu, U., Christos, P., Esserman, D., Zhong, H., Kim, M. (2022) COVID-19 Pandemic as a Change Agent in the Structure and Practice of Statistical Consulting Centers, The American Statistician, DOI: 10.1080/00031305.2021.2023045
Xue X, Cai J, Qi Q, Carlson J, Mossavar-Rahmani Y, Kaplan R, Wang T. A new measure to quatify sedentary behavior using accelerometer data: application to the Hispanic Community Health Study/Study of Latinos. Statistical Methods in Medical Research. 2021 in press.