Biostatistics

Biostatistics - Stethoscope
Mimi Kim, Sc.D.
Division HEAD: Mimi Kim, Sc.D.
mimi.kim@einsteinmed.org

The mission of the Division of Biostatistics in the Department of Epidemiology and Population Health is to (1) develop and apply innovative statistical and computational approaches to advance biomedical research; (2) collaborate with investigators in a broad range of fields including cancer, cardiovascular disease, neurology, and infectious diseases, and (3) provide educational and training opportunities in quantitative methods to the next generation of physicians and scientists.

The Division of Biostatistics was established in 2003 and currently includes over 20 doctoral and masters level statisticians. Biostatistics faculty receive grants from the National Institutes of Health, National Science Foundation, Centers for Disease Controls, and other federal funding agencies to conduct methodological research in clinical trials, epidemiology, experimental design, survival analysis, diagnostic test evaluation, imaging studies, and statistical genetics and genomics. Other areas of interest include missing data methods, casual inference, and comparative effectiveness research.

ICTR Biostatistics Resource Einstein Cancer Center Biostatistics Resource

Biostatistics News

Dr. Charlie Hall has been elected as Chair-Elect 2020 for the ASA Section on Statistics in Epidemiology.

Dr. Melissa Fazzari joined the Division of Biostatistics as Associate Professor. She was previously Director of Biostatistics at NYU Winthrop Hospital

Dr. Cuiling Wang received a R21 grant from the National Institute on Aging to develop statistical approaches to correct bias in estimating risk of Alzheimer's disease and cognitive and mobility decline using auxiliary information.

Dr. Tao Wang received a R21 grant from the National Heart, Lung, and Blood Institute to develop and apply genome-wide mendelian randomization methods to examine the relationship between obesity and lung cancer.

Biostatistics - News

Biostatistics Resources

Biostatistics - Tablet

The Division of Biostatistics offers biostatistics consulting and collaboration to enhance the quality and rigor of scientific research conducted by investigators at Einstein and Montefiore. Statistical support is available from the NIH funded Biostatistics, Epidemiology and Research Design Resource (BERD) of the Institute for Clinical and Translational Research, the NCI funded Biostatistics Shared Resource (BSR) of the Albert Einstein Cancer Center, and the Walk-in Statistical Consulting Centers that are available on both the East and West campuses.

Institute for Clinical and Translational Research
Biostatistics, Epidemiology and Research Design Resource (BERD)

The objective of the BERD Core in the Institute for Clinical Translational Research is to provide statistical and epidemiologic expertise on the design, conduct, analysis, and reporting of clinical and translational studies; provide support for new research initiatives and the development of protocols and applications for peer-reviewed funding; and identify new problems requiring the development of novel clinical and translational statistical methods. To arrange a consultation with a BERD statistician, submit the online consultation request form at the following link, click here.

Albert Einstein Cancer Center Biostatistics Shared Resource

The Biostatistics Shared Resource (BSR) of the Albert Einstein Cancer Center includes faculty and staff who collaborate with Einstein cancer investigators on basic science, clinical, translational, observational and clinical research. More information about obtaining statistical support on a cancer project is available here.

Walk-in Statistical Consulting Center

Investigators can visit the weekly walk-in statistical consulting centers to meet with a statistician without appointment and obtain quick advice on their projects. The walk-in center hours are Einstein campus: Tuesday afternoons from 3 – 5 pm in Belfer 1303 (see Dr. Mimi Kim);  Montefiore campus: First and third Thursday of every month from 2- 4 pm, 3411 Wayne Avenue Room 824 (see Dr. Jaeun Choi).

Courses

11 lectures, Fall (online)

Course description: This course provides an introduction to Clinical Research. The student will learn how clinical research studies are designed, how to analyze and interpret statistical comparisons, and will develop skills in critically reading the clinical research literature. The course consists of 11 lectures and includes the following topics. Study Design I: The Research Question; Observational Studies; Study Design II: Clinical Trials; Biostatistics I: Descriptive and Analytic Statistics - Univariate Techniques; Biostatistics II: Analytic statistics - Multivariate Techniques; Biostatistics III: Survival Analysis; Research Ethics; Comparative Effectiveness Research; Confounding and Casual Inference in Observational Studies; Healthcare and Research Informatics; Genetic/Genomic Issues in Clinical and Translational Research; Cost-Effectiveness Analysis. The course will culminate with a Final Exam. This course is open to all faculty, staff, and students at Einstein and Montefiore.

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14 lectures; Winter/Spring

Course description: This seminar course aims to introduce students to clinical research with a focus on epidemiology and study design. The course uses an introductory clinical research text, along with a critical assessment of papers from the scientific (clinical and epidemiologic) literature, in order to learn about study designs: their strengths and weaknesses and how such studies are conducted. Topics to be covered include: basic epidemiology, measures of association, basic statistics, cohort studies, case control studies, clinical trials, causal inference, and research ethics. Follows up on concepts introduced in the Clinical Research 101 lecture series, and provides a deeper dive into study design and interpretation. This course is open to all faculty, staff, and students at Einstein and Montefiore.

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Spring

Course description: This 4-week course will meet three times a week for combined lecture/lab sessions to intro-duce the basic concepts and methods of biostatistics. Concepts include: fundamentals of probability; foundations of statistical inference, confidence intervals, hypothesis tests, and sample size and power calculations. Students will gain familiarity with the freely-available statistical software, R, to explore and analyze data. This course is for students enrolled in the Graduate Programs in the Biomedical Sciences.

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Spring

Course description: This 2-week course will meet three times a week for combined lecture/lab sessions to introduce the basic concepts and methods of regression analysis. Topics include linear regression, the analysis of two-way tables, One-way and Two-way ANOVA, permutation tests, logistic regression. Introduces and employs the freely-available statistical software, R, to explore and analyze data. This course is pre-requisite for Quantitative Skills for the Biomedical Researcher III. This course is for students enrolled in the Graduate Programs in the Biomedical Sciences.

Spring

Course description: This 2-week course will cover the statistical principles that are pertinent to the study of big –omic data sets being collected in biology. Students will learn about current statistical approaches, issues related to experimental design and reproducible research, and important case studies that illuminate some of the challenges of analyzing big data. This course is the third module of the Quantitative Skills for the Biomedical Researcher series, and builds upon the material covered in the first two modules. As part of the assessment, students will gain practical experience by conducting a mini big data research project while working in small teams. It is expected that students will have completed Quantitative Skills for the Biomedical Researcher I and II, or have acquired this material through other means (please consult the course leader if in doubt). Programming skills in R is mandatory. This course is for students enrolled in the Graduate Programs in the Biomedical Sciences.

Course description: The overall objective of this course is to learn the definitions and meaning of P-values, confidence intervals, hypothesis testing and type I and type II errors; identify the appropriate statistical methods to test for associations between study variables and to know and be able to assess the assumptions of each method; interpret statistical analyses of their own and those presented in other papers; evaluate the findings of analyses with regard to potential clinical implications; evaluate the limitations and strengths of analytic findings. This course is for students enrolled in the Clinical Research Training Program.

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Biostatistics II builds on the knowledge of univariate, bivariate and multivariate analyses learned in the “Summer Intensive” course and expands on concepts related to multiple linear regression. Both the lecture and the lab will focus on multiple linear regression model building, interpretation of results, diagnostic tests of assumptions, assessing for interaction, and statistical adjustment for confounding. This course is for students enrolled in the Clinical Research Training Program.

Biostatistics III consists of a total of 14 lectures/labs which will be taught in two 7-week modules. The first module will cover logistic regression and the second module will cover survival analysis. The course objectives are to learn the basics and applications of logistic regression in assessing associations between exposure/explanatory variables and a dichotomous outcome variable; learn fundamental methods in analyzing time to event data using survival analysis, especially Cox proportional hazards modeling; use STATA software to conduct both logistic regression and survival analysis and to be able to interpret the statistical output related to these modeling techniques. This course is for students enrolled in the Clinical Research Training Program.

This course presents modern approaches to the analysis of longitudinal data. Topics include design of longitudinal studies, generalized linear models for correlated data (including generalized estimating equations, generalized linear mixed effects model), computational issues and methods for fitting models, and missing data issues. STATA statistical software will be used in the data analysis component of this course where the students will learn how analyze and interpret linear models for repeated measure continuous and discrete data. This course is for students enrolled in the Clinical Research Training Program.

Division Contact

Maureen De Louise
718.430.4003
718.430.8780

Division Faculty

Division Chief

Mimi Kim, Sc.D.

Mimi Kim, Sc.D.

Professor, Department of Epidemiology & Population Health (Biostatistics)

Harold and Muriel Block Chair in Epidemiology & Population Health

Division Head, Department of Epidemiology & Population Health Division of Biostatistics

Director, Biostatistics Epidemiology and Research Design Core of the Institute for Clinical and Translational Research

Director, Quantitative Sciences in Biomedical Research Center

718.430.2017

Areas of Research: Clinical trials; misclassification and measurement error; survival analysis, cancer, rheumatology, infectious diseases

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Full-Time Faculty

Primary Faculty
Jaeun Choi, Ph.D.

Jaeun Choi, Ph.D.

Assistant Professor, Department of Epidemiology & Population Health (Biostatistics)

718.430.3452

Areas of Research: Survival analysis, longitudinal analysis, correlated responses, causal inference, comparative effectiveness research, clinical trials, pediatric research, mental health research, cancer research

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Ryung S. Kim, Ph.D.

Ryung S. Kim, Ph.D.

Associate Professor, Department of Epidemiology & Population Health (Biostatistics)

347.886.0027

Areas of Research: big data such as electronic health records, studies nested in cohorts, complex survey data, statistical genomics, evaluation of community health programs

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Jee Young Moon, Ph.D.

Jee Young Moon, Ph.D.

Assistant Professor, Department of Epidemiology & Population Health (Biostatistics)

718.430.3558

Areas of Research: Gut microbiome, GWAS, Statistical Genetics, Mendelian Randomization, Causal Inference

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Wenzhu Bi Mowrey, Ph.D.

Wenzhu Bi Mowrey, Ph.D.

Assistant Professor, Department of Epidemiology & Population Health (Biostatistics)

718.430.2765

Areas of Research: Neurogimaging data including PET, MRI, fMRI, DTI, EEG, MEG and optical imaging; sparse clustering; dimension reduction of high dimensional data; survival and longitudinal data analysis.

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Kith Pradhan, Ph.D.

Kith Pradhan, Ph.D.

Assistant Professor, Department of Epidemiology & Population Health (Biostatistics)

Areas of Research: Dr. Pradhan is a biostatistician who collaborates with investigators in the Albert Einstein Cancer Center. His main interests include analysis methodologies in next generation sequencing and high performance computing.

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Shankar Viswanathan, Dr.P.H.

Shankar Viswanathan, Dr.P.H.

Assistant Professor, Department of Epidemiology & Population Health (Biostatistics)

718.430.3762

Areas of Research: Multivariate Survival Analysis, Longitudinal Data Analysis, Observer agreement, Missing and Measurement error issues, Clinical Trials, Oncology, Injury prevention, Global Health, Obesity and Nutritional Epidemiology

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Tao Wang, M.D., Ph.D.

Tao Wang, M.D., Ph.D.

Professor, Department of Epidemiology & Population Health (Biostatistics)

Director, Department of Epidemiology & Population Health Genetic and Genomics Data Analysis Unit Division of Biostatistics

718.430.4007

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Xianhong Xie, Ph.D.

Xianhong Xie, Ph.D.

Research Assistant Professor, Department of Epidemiology & Population Health (Biostatistics)

718.430.3625

Areas of Research: Biostatistics, Epidemiology methods, Longitudinal data analysis, Missing data

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Xiaonan (Nan) Xue, Ph.D.

Xiaonan (Nan) Xue, Ph.D.

Professor, Department of Epidemiology & Population Health (Biostatistics)

Director, Biostatistics Shared Resource Albert Einstein Cancer Center

718.430.2431

Areas of Research: statistical method on the analysis of longitudinal data, categorical data and time to event data, application of statistical methods to epidemiological studies, screening trials and evaluation of prediction models for disease

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Qian K. Ye, Ph.D.

Qian K. Ye, Ph.D.

Associate Professor, Department of Epidemiology & Population Health (Biostatistics)

Associate Professor, Department of Systems & Computational Biology

718.430.2590

Areas of Research: Statistical Genetics, Computational Genomics, Mathematical Statistics, Experimental Design, Autism

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