Courses | PhD in Biostatistics

Students are required to complete the 9 credits of required courses and 21 credits of elective courses.

Students have the flexibility to choose from the electives to tailor the curriculum toward their interests. The recommended electives are listed below. 

In addition, students will need to take courses to complete the cognate requirement, bioinformatics requirement, consulting & data analysis requirement, and research ethics requirement. More information on the courses needed for each requirement is below.

View the current 2018-2019 Class Schedule.

CHECKLIST

   3 Required Courses (9 credits)

   21 credits of Electives

   6 credits of Courses in Cognate Area

   3 credits of Bioinformatics Course

   1 credit of Consulting & Data Analysis

   0 credit of Research Ethics Course

 

Required Courses

The PhD program is built around the following core courses that provide students with a solid grounding in advanced statistical theory and methodology.

3 Credits | Fall Semester
Course Instructors: Fan, Ruzong

This is a course for PhD students with advanced knowledge of probability, statistics, and mathematics. The class covers both advanced probability theory and basic theory of stochastic processes to facilitate research of biostatistics and biomedical sciences. For probability theory, the following topics will be taught: measures, integration, probability, large sample theory of random variables. For stochastic processes, an introduction of martingales and point processes with applications to survival analysis will be taught.

3 Credits | Fall Semester
Course Instructors: Makambi, Kepher

This course takes an advanced approach to statistical inference with emphasis on theory and foundations. Topics covered include UMVUE, variance bounds and information inequalities, U-statistics; Bayes decisions and estimators, invariance, MLE, quasi- and conditional likelihoods, and asymptotic relative efficient estimation; empirical likelihoods, density estimation and semi-parametric methods, M-, L-, R-estimation, jackknife and bootstrap; UMP tests, UMP unbiased and similar tests, UMP invariant tests, likelihood ratio tests, asymptotic tests based on the likelihood, Bayes tests, tests in nonparametric models; asymptotic confidence sets, bootstrap confidence sets and simultaneous confidence intervals.

3 Credits | Fall Semester
Course Instructors: Ahn, Jaeil

The course will cover statistical methods for analyzing non-normally distributed data such as proportion, count, and rate data using generalized linear models. The course will cover topics relating to estimation, inference, deviance, diagnosis using both the frequentist and Bayesian framework. The applications include two-way tables; multi-factor, multivariate-responses, variable selection, repeated measurement experiments.

Other Requirements

Review each other requirements in order to select courses needed to fulfill the requirement.

A cognate area will be defined as an area of specialized study within the PhD degree, in a health-related field. This gives students an opportunity to gain knowledge and expertise in a biomedical research field other than statistics/biostatistics. Students must take a minimum of 6 credits in that cognate area.

Cognate Courses: Epidemiology, Global Health, Tumor Biology, Biochemistry/Biotechnology, Neuroscience, Environmental Biology, and Health Physics & Radiation Protection

Non-Cognate Courses: Mathematics, Statistics, Biostatistics, Computer science, Econometrics, and Psychometrics

This requirement may be satisfied by completing a Bioinformatics course among the elective courses.

Students must acquire experience in the planning of experiments, analyzing data, reporting results and establish a collaborative interaction with investigators.

BIST 655: Consulting Lab
1 Credits | Spring Semester
Course Instructor:  Korostyshevskiy, Valeriy

This requirement is met by completing a course in responsible conduct of research.

Elective Courses


BIST 625: Statistical Computing
3 Credits | Spring Semester
Course Instructor: Zhong, Simon

BIST 630: Bayesian Inference
3 Credits | Spring Semester
Course Instructor: Yuan, Ao

BIST 635: Longitudinal Data Analysis
3 Credits | Spring Semester
Course Instructor: Wu, Colin

BIST 640: Causal Inference
3 Credits | Fall Semester
Course Instructor: Luta, George

BIST 645: Advanced Survival Analysis
3 Credits | Fall Semester
Course Instructor: Fang, Hongbin

BIST 650: Semiparametric Inference
3 Credits | Fall Semester
Course Instructor: Yuan, Ao

BIST 655: Statistical Genetics
3 Credits | Spring Semester
Course Instructor: Fan, Ruzong

BIST 660: Deep Learning & Artificial Intelligence
3 Credits | Spring Semester
Course Instructor: Li, James