MS Degree Requirements

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The M.S. in Biostatistics requires a minimum of 15 course units (13 courses plus a Master’s Thesis). The M.S. in Biostatistics requires the student to complete the following courses. All “a” courses are offered in the fall term, “b” courses are offered in the spring term.

DEGREE REQUIREMENTS

Course Units

BIS 525a,b, Seminar in Biostatistics and Journal Club

0

BIS 540a, Fundamentals of Clinical Trials                                                           

1

BIS 623a, Applied Regression Analysis                                                

1

BIS 625a, Categorical Data Analysis                                                   

1

BIS 628b, Longitudinal and Multilevel Data Analysis                          

1

BIS 630b, Applied Survival Analysis                                                    

1

STAT 541a, Probability Theory                                                              

1

STAT 542b, Theory of Statistics                                                                        

1

CDE 508a, Principles of Epidemiology I *

1

BIS 650a,b, Master’s Thesis Research                                                                 

2

BIS 678a, Statistical Consulting

1

BIS 679a, Advanced Statistical Programming in SAS and R

1

BIS 681b, Statistical Consulting Lab

1

BIS 695c, Summer Internship in Biostatistical Research                           

0

EPH 600b, Research Ethics and Responsibility                                    

0

(BIS 695c and EPH 600b, do not count towards the 13 course units)

 

Students must choose two Biostatistics Electives from the following courses:

 

BIS 557a, Computational Statistics

1

BIS 567a, Bayesian Statistics

1

BIS 643b, Theory of Survival Analysis

1

BIS 646b, Nonparametric Statistical Methods and their Applications

1

BIS 651b, Spatial Statistics in Public Health

1

BIS 691b, Theory of Generalized Linear Models

1

All MS Biostatistics students will be required to take a Professional Skills Seminar (Dates and times to be announced) 

*Students entering the program with an MPH or relevant graduate degree may be exempt from this requirement.

rev. 8.29.2016

Master's Thesis

In the second year of the MS in the Biostatistics track, the student is required to execute a program of independent research under the direction of a faculty member. This project will usually fall into one of these main areas: development of a new statistical theory or methodology, a computer-based simulation study to illustrate properties of an existing method, or the analysis of a real dataset.

The student is required to prepare a written thesis. The thesis is written under the supervision of a Biostatistics faculty member. Upon completion of the thesis, the student will make an oral presentation of the results.

Recent Thesis Topics

2016

 

  • Analysis of ART Drug Change on Advanced AIDS Patients Clinical Outcomes
  • Analysis of Hospice Use in Determining Medical Expenditures in the SEERMedicare Data
  • A Method for Improving Goodness of Fit in Multi-Directional Kinematic Data Collected via Planar Robots
  • Unsupervised Pathway Based Clustering Analysis on Gene Expression Data
  • Improving Disease Risk Prediction Through Integrating Tunctional Annotation 
  • Quantification of Relative Selection Importance of Gene Mutations in Cholangiocarcinoma Subtypes using Cancer Selection Intensity Model-Averaged Clustering (CSI-MAC)
  • Spatial-Temporal Models for Human Lyme Disease in Connecticut
  • Exposure, Hazard, and the Temporal Dynamics of Diffusion on Social Networks
  • Longitudinal Analysis of Serial Measurements of Prostate-Specific Antigen (PSA)
  • Semi-supervised Learning: a New Method and Applications in Biomedical Studies
  • Penalized Regression Models for Genetic Risk Prediction using GWAS Data
  • Gender Difference in the Efficacy of Smoking Cessation Therapies 

2015

  • Classification of risk factors for mortality and morbidity after pulmonary resection
  • Evaluating the Effectiveness of Individual Placement and Support (IPS) Model of Supported Employment in Observational Studies
  • Incorporating ENCODE data in the analysis of genome wide association data to improve replication rates
  • Modeling nonignorable dropouts in longitudinal studies to alleviate attrition bias: An illusion of instrumentl variable approach