Graduate Degree

The Doctor of Philosophy degree in Biostatistics provides students with the quantitative skills needed for the development, evaluation and application of novel methods for the analysis of modern biomedical data.

In addition to meeting the requirements of the university, and of the College of Human Medicine, students must meet the requirements specified below.

Admission

For admission to the doctoral degree in biostatistics on regular status, the student must:

1. have a master’s degree in biostatistics, statistics, or related field;
2. submit Graduate Record Examination (GRE)scores, or MCAT scores;
3. provide TOEFL scores if their native language is other than English;
4. provide three letters of recommendation;
5. provide a statement of purpose;
6. provide official transcripts.

Applicants with strong academic records who are in the process of completing a master of science may be admitted on a provisional basis. The first 33 credits applied towards the completion of a master of science may not be counted toward the Ph.D. in Biostatistics.

Applicants who are admitted without a master’s degree will be required to complete collateral course work to make up deficiencies. Collateral course work will not count towards the fulfillment of degree requirements. It is strongly recommended that applicants have taken course work in multivariate calculus, advanced undergraduate linear algebra and probability, and numerical computing.

Requirements for the Doctor of Philosophy Degree in Biostatistics

The doctoral degree program offers three concentration areas: design and analysis of medical studies; big data and statistical genetics; and ad biometry, a flexible option for students with diverse interests. The concentration is selected in consultation with a faculty advisor and guidance committee.

Students must:

1.	Complete all of the following courses (13 credits):
	EPI	810	Introductory Epidemiology						3
	EPI	828	Seminar in Responsible Conduct of Research						1
	EPI	860	Advanced Inference for Biostatistics						3
	STT	867	Linear Model Methodology						3
	STT	868	Mixed Models: Theory, Methods and Applications						3
2.	Complete one of the following concentrations:
	Design and Analysis of Medical Studies
	1.	One of the following courses (3 credits):
		EPI	858	Clinical Trial I					3
		EPI	952	Duration and Severity Analysis					3
		Or
		STT	847	Analysis of Survival Data					3
	2.	Complete 11 credits of elective course work:
		ANS	814	Advanced Statistics for Biologists					4
		CSE	331	Algorithms and Data Structures					3
		CSE	480	Database Systems					3
		CSE	482	Big Data Analysis					3
		CSE	847	Machine Learning					3
		CSE	881	Data Mining					3
		EC	821A	Cross Section and Panel Data Econometrics I					3
		EC	821	Cross Section and Panel Data Econometrics II					3
		EPI	812	Causal Inference in Epidemiology					3
		EPI	855	Biostatistical Modeling in Genomic Data Analysis					3
		EPI	880	Selected Topics in Biostatistics					3
		EPI	920	Advanced Methods in Epidemiology and Applied Statistics					3
		EPI	950	Advanced Biostatistical Methods in Epidemiology					3
		EPI	952	Duration and Severity Analysis					3
		EPI	953	Analytical Strategies for Observational Studies					3
		EPI	990	Independent Study					3
		STT	801	Design of Experiments					3
		STT	825	Sample Surveys					3
		STT	855	Statistical Genetics					3
		STT	861	Theory of Probability and Statistics I					3
		STT	862	Theory of Probability and Statistics II					3
		STT	873	Statistical Learning and Data Mining					3
		STT	874	Introduction to Bayesian Analysis					3
		Additional courses may be chosen with advisor approval.
	Big Data and Statistical Genetics
	1.	One of the following courses:
		EPI	855	Biostatistical Modeling in Genomic Data Analysis					3
		Or
		STT	855	Statistical Genetics					3
		CSE	231	Introduction to Programming I					3
		Or
		CSE	232	Introduction to Programming II					4
		STT	456	Actuarial Models II					3
	2.	Complete 11 credits of elective course work:
		ANS	814	Advanced Statistics for Biologists					4
		CSE	331	Algorithms and Data Structures					3
		CSE	480	Database Systems					3
		CSE	482	Big Data Analysis					3
		CSE	847	Machine Learning					3
		CSE	881	Data Mining					3
		EC	821A	Cross Section and Panel Data Econometrics I					3
		EC	821	Cross Section and Panel Data Econometrics II					3
		EPI	812	Causal Inference in Epidemiology					3
		EPI	858	Clinical Trials					3
		EPI	880	Selected Topics in Biostatistics					3
		EPI	920	Advanced Methods in Epidemiology and Applied Statistics					3
		EPI	950	Advanced Biostatistical Methods in Epidemiology					3
		EPI	952	Duration and Severity Analysis					3
		EPI	953	Analytical Strategies for Observational Studies					3
		EPI	990	Independent Study					3
		STT	801	Design of Experiments					3
		STT	825	Sample Surveys					3
		STT	861	Theory of Probability and Statistics I					3
		STT	862	Theory of Probability and Statistics II					3
		STT	873	Statistical Learning and Data Mining					3
		STT	874	Introduction to Bayesian Analysis					3
		Additional courses may be chosen with advisor approval.
	Biometry
	1.	Complete 14 credits of elective course work:
		ANS	814	Advanced Statistics for Biologists					4
		CSE	331	Algorithms and Data Structures					3
		CSE	480	Database Systems					3
		CSE	482	Big Data Analysis					3
		CSE	847	Machine Learning					3
		CSE	881	Data Mining					3
		EC	821A	Cross Section and Panel Data Econometrics I					3
		EC	821	Cross Section and Panel Data Econometrics II					3
		EPI	812	Causal Inference in Epidemiology					3
		EPI	855	Biostatistical Modeling in Genomic Data Analysis					3
		EPI	858	Clinical Trials					3
		EPI	880	Selected Topics in Biostatistics					3
		EPI	920	Advanced Methods in Epidemiology and Applied Statistics					3
		EPI	950	Advanced Biostatistical Methods in Epidemiology					3
		EPI	952	Duration and Severity Analysis					3
		EPI	953	Analytical Strategies for Observational Studies					3
		EPI	990	Independent Study					3
		STT	801	Design of Experiments					3
		STT	825	Sample Surveys					3
		STT	847	Survival Analysis					3
		STT	855	Statistical Genetics					3
		STT	861	Theory of Probability and Statistics I					3
		STT	862	Theory of Probability and Statistics II					3
		STT	873	Statistical Learning and Data Mining					3
		STT	874	Introduction to Bayesian Analysis					3
		Additional courses may be chosen with advisor approval.
2.	Attend all MSU Graduate School Responsible Conduct of Research (RCR) Workshops (human).
3.	Attend 80% of department-sponsored Seminars.
4.	Attend 80% of department Ph.D. Journal Club meetings.
5.	Present at one Ph.D. Journal Club meeting.
6.	Pass a comprehensive examination.
7.	Complete 24 credits of EPI 999 Doctoral Dissertation Research.
8.	Pass an oral defense of the doctoral dissertation.

Academic Standards

Students will sit for a comprehensive examination after the necessary course work is completed, typically at the end of the first year of study. A student who fails the comprehensive examination may repeat it only once. A retake examination will generally be given in January.