MS - Biostatistics Concentration

Biostatistics is one of the primary skills needed for the development and practice of public health – the methods of this discipline are appropriate for quantifying the possible effects of risk factors and health interventions on individuals and groups. The MS with a concentration in Biostatistics is a two-year program that provides training in clinical trials, epidemiologic methodology, implementation science, statistical genetics, and mathematical models for infectious diseases.

Biostatistics students may choose from three pathways:

• Biostatistics Standard Pathway
• Biostatistics Implementation and Prevention Science Methods Pathway
• Data Science Pathway

Applicants apply to the MS in Biostatistics and if admitted will have until the spring term of their first year to choose either the Standard Pathway or the Implementation and Prevention Science Methods Pathway.

Students wishing to choose the Data Science Pathway must choose at the start of the first-year Fall term. In contrast to the more general MPH degree, the MS degree emphasizes the mastery of biostatistical skills from the beginning of the plan of study. While graduates of this program may apply to the PhD degree program, the MS degree is itself quite marketable as a terminal degree. Part-time enrollment is permitted.

Please note that you may apply to both the MPH and the MS program simultaneously, but they do require separate applications.

The length of study for the MS in Biostatistics (all pathways) is two years. Full-time or part-time enrollment is permissible.

This program requires General GRE test scores. Please send them to institution code 3987 (there is no department code).

Students with strong quantitative skills from all undergraduate majors are welcome to apply. Students should have, at a minimum, previous college-level coursework in multivariable calculus (a.k.a., calculus 3) and linear algebra. Experience with introductory statistics and statistical programming language (e.g., R, SAS, python) is helpful, but not required.

Master of Science students in Biostatistics at the Yale School of Public Health enage with the field through summer internships and capstone or thesis projects. These immersive experiences provide robust opportunities for the real-world application of public health knowledge and skills and support your career goals. Many students report inspiring, life changing experiences from their time around the world and in local settings.

Opportunities for research abound at Yale. Although a master’s thesis is optional for MS students in biostatistics, many students use their summer internship to do research that culminates in a thesis. Capstone projects also provide experience to work with faculty labs and research teams to access data and formulate their analysis. In addition, many students work as research assistants at the Yale School of Public Health, Yale School of Medicine, Yale New Haven Hospital or the VA. Many of these projects culminate in peer review publications.

• Acumen
• Adaptive Biotechnologies
• Ainylam Pharmaceuticals
• Alibaba Group ---Management Executive Council
• American International Group,Inc.
• Analysis Group
• Bain and Company in Shanghai
• Bank of Communications - NY
• Bank of NY Mellon Corporation
• Bayer-Monsanto, Woodland, CA
• Boehringer Ingelheim
• Bristol-Myers Squibb
• CARDET in Cyprus
• CB Sciences
• Celegene
• Center for Outcomes Research and Evaluation - CORE - Yale New Haven Hospital
• Center for Perinatal, Pediatric and Environmental Epi at Yale
• Citadle, LLC
• closedloop.ai
• Digitas in Boston
• Dr. Emma Zang (Sociology)
• Dr. Forrest Crawford lab
• Dr. Heping Zhang lab
• Dr. Hongyu Zhao Lab
• Dr. Josh Warren
• Dr. Maria Ciarleglio
• Dr. Shi-Yi Wang
• Dr. Wei Wei
• Dr. Yize Zhao
• Ernst and Young
• Facebook, NY
• FDA
• Fulcrum Analytics
• Genetech
• GNS Healthcare,Inc.
• Goldenson Center for Actuarial Research- Uconn
• Goldman Sachs, NY
• Guotai Junan Securities Co., Ltd
• Health Services Advisory Group, Phoenix
• Mayo Clinic/Genetech - California
• McKinsey and Company
• Medical Consulting Corporation/China Merchants Securities/GF Securities
• Microsoft Research
• Mid-Atlantic Permante Med Group
• Millenuium/Takeda
• Mirae Asset Investment Management in Shanghai
• Netease - Hangzhou China
• NIH/Child Health and Human Dev
• Orient Securities
• People's Bank/Risk Mgmt
• Pfizer
• Pulse8, Inc. startup in MD
• Randstad
• Regeneron
• Remedy Partners - startup
• Rosenthal Collins group/Chicago
• Staff Strategic Holdings
• The Speree Institute
• Travelers Insurance
• United Nations/Data Science
• Univ. of Wisconsin/Madison
• VA Hospital - West Haven
• Verus Analytics LLC, NY
• WuXI NextCODE Genomics USA
• Yale Center for Analytical Sciences (YCAS)
• Yale Center for Asthma and Airway Diseases
• Yale - CORE
• Yale Dept. of Psychiatry and Genetics
• Yale Internal Medicine/Geriatrics/Red CAP
• Yale Internal Medicine/Pulmonary & Pulmonary, Critial Care & Sleep Medicine Ctr
• Yale New Haven Health
• Pulmonary, Critical Care and Sleep Medicine (PCCSM) Center at Yale
• Yale Program on Aging
• Yale Stress Center
• Yale School of Management
• Yale Dept Psychiatry
• Yale School of Medicine

• Longitudinal trajectories of nicotine product use in adolescents in the Population Assessment of Tobacco and Health Study
• A direction-varying change point model for estimating the impact of a point source
• A Bayesian Design for Trials with Multiple Indications under Multisource Exchangeability Assumptions
• SNP calling from single cell RNA sequencing data
• Different Language Model Comparisons in Pain Score Prediction
• Power Calculation for Cluster Randomized Trials with Binary Outcomes
• Testing Composite Endpoints in Cardiovascular Clinical Trials
• Estimating Individualized Co-expression Networks from Single Cell RNA Sequencing Data
• A Bayesian Approach to Correcting the Attenuation Bias of Regression Using Polygenic Risk Score
• A Simulation Study on the Missing Covariate Indicator Method
• Evaluating the accuracy of reconstructing prey-predator networks from camera-trap data: an agent-based simulation study
• Empirical Bayes Estimation of Power and Sample Size for Biomarker Study
• Social Determinants of Health and Treatment Outcomes among Patients with Heart Failure with reduced Ejection Fraction (HFrEF): Ensemble Learning using Large Language Models"
  
• Shape constrained differential analysis for vaccine signature identification
  
• A hierarchical Bayesian time series method for the offline evaluation of bandit algorithms
  
• Advancing Protein Co-Expression Network Analysis: From Traditional Similarity
  
• Metrics to Language Model-Based Clustering"
  
• Impact of Covariate Adjustment in Randomized Clinical Trials with Binary Outcomes
  
• Robust mediation analysis in public health: an infinitesimal jackknife approach to identify influential observations in BRFSS data
  
• Evaluation of Survival Outcomes of Single and Dual Chamber ICD for primary prevention of sudden cardiac death
  
• Anticipation effect in stepped wedge cluster randomized trials
  
• Automated Data Harmonization in Cardiovascular Health: BuildinganAIdriven Data Ecosystem for Risk Prediction of Cognitive Impairment and Dementia__
• Enhancing Precision Medicine through Integrated Gene Regulatory Network Analysis