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  • Overview
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      • Biomarker and Biorepository Core
      • Biostatistics, Epidemiology & Research Design Core
      • Clinical Research Center
      • Clinical Research Resource
      • Community Engagement Consultation & Collaboration Core
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      • Health Research Implementation Core
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    • Ph.D. in Clinical Investigation (PCI)

      The Ph.D. in Clinical Investigation provides rigorous advanced training that prepares you for an indepenedent research career in clinical and translational science.

      • The Einstein-Montefiore PCI track can prepare you to conduct research that will improve the health and welfare of society using clinical and translational research methodology.
      • PCI is offered for Ph.D. students enrolled in Einstein's graduate division and for M.D./Ph.D. students in Einstein's Medical Scientist Training Program (MSTP).

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      MS in Clinical Research Methods (CRTP)

      The Master of Science (MS) in Clinical Research Methods is attained through the Clinical Research Training Program (CRTP). This intensive two-year program is designed for those pursuing a career in investigator-initiated, hypothesis-driven clinical research. CRTP scholars are drawn from all of Einstein's medical specialties and subspecialties, including those interested in clinical investigation across the entire translational research spectrum, from mechanistic studies to population-based and health services research. The comprehensive program combines didactic learning and coursework with a mentored research experience.

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Biostatistics, Epidemiology & Research Design Core

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
Access Services

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

BERD House

BERD House

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.

Visit the BERD House

Leadership:

  • Mimi Kim, Sc.D.

    Mimi Kim, Sc.D.

    Director, Biostatics, Epidemiology & Research Design Core

  • Melissa J. Fazzari, Ph.D., M.S.

    Melissa J. Fazzari, Ph.D., M.S.

    Associate Director

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.

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CITE the CTSA

KL2 scholars cite grant KL2 TR002558, as follows:
This research was supported by NIH/National Center for Advancing Translational Science (NCATS) Einstein-Montefiore CTSA Grant Number TR002558.

TL1 trainees cite grant TL1 TR002557, as follows:
This research was supported by NIH/National Center for Advancing Translational Science (NCATS) Einstein-Montefiore CTSA Grant Number TL1TR002557.

All other researchers cite grant 1UM1TR004400, as follows:
The research described was supported by NIH/National Center for Advancing Translational Science (NCATS) Einstein-Montefiore CTSA Grant Number 1UM1TR004400.

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