PhD in Biostatistics Courses & Requirements

biostatistics courses

The objective of the PhD degree program is to provide students with the quantitative skills needed for the development, evaluation and application of novel methods for the analysis of modern biomedical data. 51 credits beyond the Master’s degree are required

  • 22 credits core courses
  • 5 credits electives
  • 24 dissertation credits

Course Options 

The PhD program will offer an emphasis in three broad areas:

Big data and statistical genetics; for theory and methods of analyses for genetics and genomic data.

REQUIRED COURSES 

Complete all of the following courses (19) credits

Fall Semester

EPI 810 INTRODUCTORY EPIDEMIOLOGY

Disease from a population perspective as the interaction of host, agent, and environment. Case definition, measuring frequency of disease, mortality and morbidity data, and major study designs.

Semester: Fall (Every year) Credits: 3
Prerequisite: N/A
Instructor: Dr. Honglei Chen / Syllabus

 

EPI 828 RESPONSIBLE CONDUCT OF RESEARCH

Ethical and regulatory issues in the responsible conduct of epidemiology research. Topics include informed consent; scientific misconduct; human subjects protection; responsible data management including electronic medical records, biological samples and genetic data; HIPAA compliance; and other current issues of scientific integrity.

Semester: Fall (Every year) Credits: 1
Prerequisite: EPI 810
Instructor: Dr. David Barondess / Syllabus

 

EPI 859 CLINICAL TRIALS II

Course not available yet. 

  

STT 867 LINEAR MODEL METHODOLOGY

Properties of the multivariate normal distribution, Cochran's Theorem, simple and multiple linear regression models, Gauss-Markov Theorem, best linear unbiased prediction, one- and two-way ANOVA models, sums of squares, diagnostics and model selection, contingency tables and multinomial models, generalized linear models, logistic regression.

Semester: Fall (Every year) Credits: 3
Prerequisite: STT 862 


Spring Semester

EPI 858 CLINICAL TRIALS I

Statistical methods for design and analysis of clinical trials and epidemiological studies. Phase I, II, and III clinical trials. Principle of Intention-to-Treat, effects of non-compliance, drop-outs. Interim monitoring of clinical trials and data safety monitoring boards. Meta-analysis. Crossover designs. Sample size and power in clinical trials. Sequential designs.

Semester: Spring (Even years) Credits: 3
Prerequisite: EPI 808B or (EPI 809 or LCS 829)
Instructor Dr. Dorothy Pathak  /  Syllabus

 

EPI 860 ADVANCED INFERENCE FOR BIOSTATISTICS

Statistical inference problems with biomedical applications.

Semester: Spring (Every year) Credits: 3
Prerequisite: STT 861 and STT 862
Instructor Dr. David Todem  Syllabus

 

STT 868 MIXED MODELS: THEORY, METHOD & APPLICATIONS

Maximum likelihood estimation and other estimation methods for linear mixed models. Statistical properties of LME models. Prediction under LME models. Generalized linear mixed models. Quasi-likelihood estimation, generalized estimating equations for GLMM. Nonlinear mixed models. Diagnostics and influence analysis. Bayesian development in mixed linear models. Application of mixed models.

Semester: Spring (Every year) Credits: 3
Prerequisite: STT 867


CHOOSE 1
OF THE NEXT 2 COURSES 3 credits

 

EPI 952 DURATION & SEVERITY ANALYSIS

Analysis of data that involve time to occurrence of a single event or multiple durations between occurrences of several events; modeling techniques; survival analysis in clinical and public health studies; frailty models; experimental and non-experimental applications using major statistical software.

Semester: Spring (odd yearsr) Credits: 3
Prerequisite: EPI 826or EPI 826B
Instructor Dr. Joseph Gardiner  / Syllabus

 

STT 847 ANALYSIS OF SURVIVAL DATA

Analysis of lifetime data. Estimation of survival functions for parametric and nonparametric models. Censored data. The Cox proportional hazards model. Accelerated failure time models. Frailty models. Use of statistical software packages.

Semester: Spring (odd years) Credits: 3
Prerequisite: N/A
Instructor:   /  Syllabus


ELECTIVES 

Complete 5 credits of elective course work from the below list of approved courses.

Note:  Additional elective courses may be chosen with advisor’s approval. 


Fall Semester

EPI 812 CAUSAL INFERENCE

Causality in epidemiology. Application of theoretical concepts to the design, analysis, and assessment of epidemiologic research.

Semester: Fall (Every year) Credits: 3
Prerequisite: EPI 810
Instructor: Dr. Claudia Holzman / Syllabus

 

EPI 817 COMMUNICABLE DISEASE

Application of principles of epidemiology to research in communicable diseases relevant to public health in the U.S. and other countries.

Semester: Fall (Every year) Credits: 3
Prerequisite: EPI 810
Instructor: Dr. Lixin Zhang / Syllabus

 

EPI 835 NEUROEPIDEMIOLOGY

Epidemiology of neurologic and neuropsychiatric disorders with emphases on neurodegenerative disorders (e.g., Alzheimer's disease).

Semester: Fall (odd years) Credits: 3
Prerequisite: EPI 810
Instructor: Dr. Honglei Chen, Dr. Andrew Bender  /  Syllabus

 

EPI 853B Statistical Computing

Prerequisite: EPI 808B and EPI 826B

Statistical computation and algorithms using programming languages, SAS/IML, R and/or Stata, Newton-Raphson method, Monte Carlo simulation of probability distributions, bootstrap, statistical graphics.

Semester: Fall (Every year) Credits: 3
Prerequisite: EPI 808B & EPI 826B
Instructor: Dr. Gustavo de los Campos / Syllabus

 

EPI 855 Biostatistical Modeling in Genomic Data Analysis

Introduction to fundamental principles and modeling of genomic /genetic data and computational techniques

Semester: Fall (Every year) Credits: 3
Prerequisite: EPI 808B & EPI 826 or 826B
Instructor: Dr. Gustavo de los Campos / Syllabus

 

EPI 950 ADVANCED BIOSTATISTICAL METHODS IN EPIDEMIOLOGY

Study of specific biostatistical methods and epidemiology applications.

Semester: Fall (even years) Credits: 3
Prerequisite: EPI 826B OR EPI 826
Instructor: Dr. Zhehui Luo / Syllabus

 

EPI 953 ANALYTICAL STRATEGIES FOR OBSERVATIONAL STUDIES

Models and methods such as propensity scores, instrumental variables, regression discontinuity design, discrete choice analysis, and marginal structural models. Examples will be demonstrated with procedures in major statistical software.

Semester: Fall (odd years) Credits: 3
Prerequisite: EPI 826B OR EPI 826
Instructor: Dr. Zhehui Luo Syllabus

 

EPI 977 SOCIAL EPIDEMIOLOGY

Introduction to the field of social epidemiology and the social determinants of health. Contemporary theoretical and methodological issues in social epidemiology.

Semester: Fall (even years) Credits: 3
Prerequisite: EPI 810
Instructor: Dr. Claire Margerision / Syllabus

 

STT 465 BAYESIAN STATISTICAL METHODS

Probability, belief, and exchangeability. Objective, subjective, and empirical Bayes approaches. Applications to one-parameter models, linear regression models, and multivariate normal models. Hierarchical modeling. Computational methods.

Semester: Fall (every year) Credits: 3
Prerequisite: STT 422

 

STT 801 DESIGN OF EXPERIMENTS

Blocking and randomization. Split-plot, latin square and factorial designs. Fractional factorial designs, aliasing and confounding of effects. Mixture and central composite designs and response surface exploration. Clinical trials.

Semester: Fall (every year) Credits: 3
Prerequisite: N/A

 

STT 825 SAMPLE SURVEYS

Application of statistical sampling theory to survey designs. Simple random, stratified, and systematic samples. Sub-sampling, double sampling. Ratio and regression estimators.

Semester: Fall (Every year) Credits: 3
Prerequisite: N/A

 

STT 855 STATISTICAL GENETICS

Probabilistic and statistical methods for genetic linkage and association studies. Quantitative trait locus mapping.

Semester: Fall (odd years) Credits: 3
Prerequisite: N/A

 

STT 861 THEORY OF PROBABILITY & STATISTICS I

Probability models, random variables and vectors. Special distributions including exponential family. Expected values, covariance matrices, moment generating functions. Convergence in probability and distribution. Weak Law of Large Numbers and Lyapunov Central Limit Theorem.

Semester: Fall (every) Credits: 3
Prerequisite: N/A 

 

STT 873 STATISTICAL LEARNING & DATA MINING

Statistical methods focusing on machine learning and data mining, modern regression and classification techniques, support vector machines, boosting, kernel methods and ensemble methods, clustering dimension reduction, manifold learning, and selected topics.

Semester: Fall (odd years) Credits: 3
Prerequisite: STT 868 and STT 872
Instructor   /  Syllabus

 

EC 821A CROSS SECTION & PANEL DATA ECONOMETRICS I

Analyses of systems of equations, panel data models, instrumental variables and generalized method of moments, M-estimation, quantile regression, maximum likelihood estimation, binary and multinomial response models, Tobit and two-part models, and other selected topics.

Semester: Fall (every year) Credits: 3
Prerequisite: EC 820B

 

CSE 881 DATA MINING

Techniques and algorithms for knowledge discovery in databases, from data preprocessing and transformation to model validation and post-processing. Core concepts include association analysis, sequential pattern discovery, anomaly detection, predictive modeling, and cluster analysis. Application of data mining to various application domains.

Semester: Fall (every year) Credits: 3
Prerequisite: N/A 


Spring Semester

EPI 815 EPIDEMIOLOGY OF CARDIOVASCULAR DISEASE

Survey of methodologies used in epidemiologic studies of cardiovascular diseases. Review of evidence of genetic, environmental, and behavioral causes of cardiovascular disease.

Semester: Spring (even years) Credits: 3
Prerequisite: N/A
Instructor: Dr. Mat Reeves / Syllabus

 

EPI 816 PERINATAL EPIDEMIOLOGY

Epidemiology of adverse health states in pregnancy and the puerperium. Impact of these health states on subsequent child development.

Semester: Spring (Even years) Credits: 3
Prerequisite: N/A
Instructor: Dr. Nigel Paneth  /  Syllabus

 

EPI 823 CANCER EPIDEMIOLOGY

Basic principles of carcinogenesis. Major etiologic factors, types of malignancies, and biomarkers for susceptibility and exposure. Prevention and early detection of cancer.

Semester: Spring (odd years) Credits: 3
Prerequisite: EPI 810 & (EPI 809 OR EPI 808B)
Instructor: Dr. Dorothy Pathak / Syllabus

 

EPI 920 ADVANCED METHODS IN EPIDEMIOLOGY AND APPLIED STATISTICS

Pattern recognition and cluster analysis, longitudinal data analysis, path analysis, repeated measures and time-series analysis.

Semester: Spring (every year) Credits: 3
Prerequisite: EPI 826B OR EPI 826
Instructor: Dr. David Todem / Syllabus

 

EPI 979 ADVANCED TOPICS OF INFECTIOUS DISEASE EPIDEMIOLOGY

Epidemiological and public health perspectives on the etiology, transmission and prevention of infectious diseases. Key conceptual and methodological issues associated with studying infectious diseases from molecular and population based perspectives.

Semester: Spring (even years) Credits: 3
Prerequisite: N/A
Instructor: Dr. Lixin Zhang / Syllabus 

 

STT 814 ADVANCED STATISTICS FOR BIOLOGISTS

Concepts of reducing experimental error for biological and agricultural research. Covariance, randomized block designs, latin squares, split plots, repeated-measures designs, regression applications, and response surface designs. Analyses using statistical software.

Semester: Spring (every year) Credits: 3
Prerequisite: N/A

 

STT 862 THEORY OF PROBABILITY & STATISTICS II

Statistical inference: sufficiency, estimation, confidence intervals and testing of hypotheses. One and two sample nonparametric tests. Linear models and Gauss-Markov Theorem.

Semester: Spring (every year) Credits: 3
Prerequisite: STT 861

 

EC 821B CROSS SECTION & PANEL DATA ECONOMETRICS II

Analyses of quasi-maximum likelihood estimation, count data models, fractional response models, duration models, sample selection and attrition, stratified sampling, estimating treatment effects, stochastic frontier models, and other advanced topics.

Semester: Spring (every year) Credits: 3
Prerequisite: EC 821A 

 

CSE 480 DATABASE SYSTEMS

Storage of and access to physical databases including indexing, hashing, and range accesses. Relational data models, database design principles, query languages, query optimization, transaction processing and recovery techniques. Object-oriented and distributed databases.

Semester: Spring (every year) Credits: 3
Prerequisite: CSE 331 or CSE 335

 

CSE 482 BIG DATA ANALYSIS

Data collection, storage, and preprocessing, and analysis techniques. Programming for large-scale data analysis. Case studies and applications.

Semester: Spring (every year) Credits: 3
Prerequisite: CSE 331 and (STT 351 or STT 380 or STT 430 or STT 441)

 

CSE 847 MACHINE LEARNING

Computational study of learning and data mining. Strengths and limitations of various learning paradigms, including supervised learning, learning from scalar reward, unsupervised learning, and learning with domain knowledge.

Semester: Spring (every year) Credits: 3
Prerequisite: CSE 841


Summer Semester

EPI 880 SELECT TOPICS IN BIOSTATISTICS

Select topics in biostatistics including global disease distribution and estimation, causal inference, Bayesian methods in health services research.

Semester: Summer (Every year) Credits: 3
Prerequisite: EPI 808B or (EPI 808 & EPI 809) or (PHM 830 OR STT 464)
Instructor Dr. David Todem / Syllabus


Any Semester

EPI 990 INDEPENDENT STUDY (subject not taught in another course)

Special projects, directed reading, and research arranged by an individual graduate student and a faculty member in areas supplementing regular course offerings.

Semester: Any  Credits: 2
Prerequisite: N/A 

 

CSE 231 INTRODUCTION TO PROGRAMMING I

Introduction to programming using Python. Design, implementation and testing of programs to solve problems such as those in engineering, mathematics and science. Programming fundamentals, functions, objects, and use of libraries of functions.

Semester: All (every year) Credits: 4
Prerequisite: LB 118 OR MTH 124 OR MTH 132 OR MTH 152H

 

CSE 232 INTRODUCTION TO PROGRAMMING II

Continuation of object-centered design and implementation in C++. Building programs from modules. Data abstraction and classes to implement abstract data types. Static and dynamic memory allocation. Data structure implementation and algorithm efficiency. Lists, tables, stacks, and queues. Templates and generic programming.

Semester: All (every year) Credits: 4
Prerequisite: CSE 231 and LB 118 or MTH 124 or MTH 132 or MTH 152H

 

CSE 331 ALGORITHMS AND DATA STRUCTURES

Linear data structures, trees, graphs and algorithms which operate on them. Fundamental algorithms for searching, sorting, string matching, graph problems. Design and analysis of algorithms.

Semester: All (every year) Credits: 3
Prerequisite: CSE 232 and (CSE 260 or CMSE 202)


DISSERTATION 

EPI 999 PhD DISSERTATION RESEARCH

Doctoral dissertation research.

Semester: Fall, Spring or Summer (every year) Credits: 24
Prerequisite: N/A

Clinical trials; for the design, methods and analysis from randomized trials of treatments.

Requirement for the Big Data and Statistical Genetics PhD

REQUIRED COURSES

Complete all of the following courses (19) credits


Fall Semester

EPI 810 INTRODUCTORY EPIDEMIOLOGY

Disease from a population perspective as the interaction of host, agent, and environment. Case definition, measuring frequency of disease, mortality and morbidity data, and major study designs.

Semester: Fall (Every year) Credits: 3
Prerequisite: N/A
Instructor: Dr. Honglei Chen / Syllabus

 

EPI 828 RESPONSIBLE CONDUCT OF RESEARCH

Ethical and regulatory issues in the responsible conduct of epidemiology research. Topics include informed consent; scientific misconduct; human subjects protection; responsible data management including electronic medical records, biological samples and genetic data; HIPAA compliance; and other current issues of scientific integrity.

Semester: Fall (Every year) Credits: 1
Prerequisite: EPI 810
Instructor: Dr. David Barondess / Syllabus

 

EPI 860 ADVANCED INFERENCE FOR BIOSTATISTICS

Semester: Spring (Every year) Credits: 3
Prerequisite: STT 861 and STT862
Instructor Dr. David Todem  /  Syllabus

 

STT 867 LINEAR MODEL METHODOLOGY

Properties of the multivariate normal distribution, Cochran's Theorem, simple and multiple linear regression models, Gauss-Markov Theorem, best linear unbiased prediction, one- and two-way ANOVA models, sums of squares, diagnostics and model selection, contingency tables and multinomial models, generalized linear models, logistic regression.

Semester: Fall (Every year) Credits: 3
Prerequisite: STT 862


Spring Semester

STT 868 MIXED MODELS: THEORY, METHOD & APPLICATIONS

Maximum likelihood estimation and other estimation methods for linear mixed models. Statistical properties of LME models. Prediction under LME models. Generalized linear mixed models. Quasi-likelihood estimation, generalized estimating equations for GLMM. Nonlinear mixed models. Diagnostics and influence analysis. Bayesian development in mixed linear models. Application of mixed models.

Semester: Spring (Every year) Credits: 3
Prerequisite: STT 867

 


CHOOSE 1
OF THE NEXT 2 COURSES AS A CORE COURSE

 

EPI 855 Biostatistical Modeling in Genomic Data Analysis

Introduction to fundamental principles and modeling of genomic /genetic data and computational techniques

Semester: Fall (Every year) Credits: 3
Prerequisite: EPI 808B & EPI 826 or 826B
Instructor: Dr. Gustavo de los Campos / Syllabus

or

STT 855 STATISTICAL GENETICS

Probabilistic and statistical methods for genetic linkage and association studies. Quantitative trait locus mapping.

Semester: Fall (odd years) Credits: 3
Prerequisite: N/A


CHOOSE 1
OF THE NEXT 2 COURSES AS A CORE COURSE

 

CSE 231 INTRODUCTION TO PROGRAMMING I

Introduction to programming using Python. Design, implementation and testing of programs to solve problems such as those in engineering, mathematics and science. Programming fundamentals, functions, objects, and use of libraries of functions.

Semester: All (every year) Credits: 4
Prerequisite: LB 118 OR MTH 124 OR MTH 132 OR MTH 152H

 

CSE 232 INTRODUCTION TO PROGRAMMING II

Continuation of object-centered design and implementation in C++. Building programs from modules. Data abstraction and classes to implement abstract data types. Static and dynamic memory allocation. Data structure implementation and algorithm efficiency. Lists, tables, stacks, and queues. Templates and generic programming.

Semester: All (every year) Credits: 4
Prerequisite: CSE 231 and LB 118 or MTH 124 or MTH 132 or MTH 152H


CHOOSE 1
OF THE NEXT 2 COURSES AS A CORE COURSE

 

STT 465 BAYESIAN STATISTICAL METHODS

Probability, belief, and exchangeability. Objective, subjective, and empirical Bayes approaches. Applications to one-parameter models, linear regression models, and multivariate normal models. Hierarchical modeling. Computational methods.

Semester: Fall (every year) Credits: 3
Prerequisite: STT 422

 

STT 874 INTRODUCTION TO BAYESIAN ANALYSIS

Bayesian methods including empirical Bayes, hierarchical Bayes and nonparametric Bayes, computational methods for Bayesian inference including the Gibbs Sampler and Metropolis-Hastings method, and applications.

Semester: Fall (even years) Credits: 3
Prerequisite: STT 868 and STT 872


ELECTIVES

Complete 5 credits of elective course work from the below list of approved courses.

Note:  Additional elective courses may be chosen with advisor’s approval. 


Fall Semester

EPI 812 CAUSAL INFERENCE

Causality in epidemiology. Application of theoretical concepts to the design, analysis, and assessment of epidemiologic research.

Semester: Fall (Every year) Credits: 3
Prerequisite: EPI 810
Instructor: Dr. Claudia Holzman / Syllabus

 

EPI 817 COMMUNICABLE DISEASE

Application of principles of epidemiology to research in communicable diseases relevant to public health in the U.S. and other countries.

Semester: Fall (Every year) Credits: 3
Prerequisite: EPI 810
Instructor: Dr. Lixin Zhang / Syllabus

 

EPI 835 NEUROEPIDEMIOLOGY

Epidemiology of neurologic and neuropsychiatric disorders with emphases on neurodegenerative disorders (e.g., Alzheimer's disease).

Semester: Fall (odd years) Credits: 3
Prerequisite: EPI 810
Instructor: Dr. Jim Anthony / Syllabus

 

EPI 853B Statistical Computing

Prerequisite: EPI 808B and EPI 826B

Statistical computation and algorithms using programming languages, SAS/IML, R and/or Stata, Newton-Raphson method, Monte Carlo simulation of probability distributions, bootstrap, statistical graphics.

Semester: Fall (Every year) Credits: 3
Prerequisite: EPI 808B & EPI 826
Instructor: Dr. Gustavo de los Campos / Syllabus

 

EPI 855 Biostatistical Modeling in Genomic Data Analysis

Introduction to fundamental principles and modeling of genomic /genetic data and computational techniques

Semester: Fall (Every year) Credits: 3
Prerequisite: EPI 808B & EPI 826 or 826B
Instructor: Dr. Qing Luo / Syllabus

 

STT 465 BAYESIAN STATISTICAL METHODS

Probability, belief, and exchangeability. Objective, subjective, and empirical Bayes approaches. Applications to one-parameter models, linear regression models, and multivariate normal models. Hierarchical modeling. Computational methods.

Semester: Fall (every year) Credits: 3
Prerequisite: STT 422

 

STT 801 DESIGN OF EXPERIMENTS

Blocking and randomization. Split-plot, latin square and factorial designs. Fractional factorial designs, aliasing and confounding of effects. Mixture and central composite designs and response surface exploration. Clinical trials.

Semester: Fall (even year) Credits: 3
Prerequisite: N/A 

 

EPI 953 ANALYTICAL STRATEGIES FOR OBSERVATIONAL STUDIES

Models and methods such as propensity scores, instrumental variables, regression discontinuity design, discrete choice analysis, and marginal structural models. Examples will be demonstrated with procedures in major statistical software.

Semester: Fall (odd years) Credits: 3
Prerequisite: EPI 826B OR EPI 826
Instructor: Dr. Zhehui Luo / Syllabus

 

EPI 977 SOCIAL EPIDEMIOLOGY

Introduction to the field of social epidemiology and the social determinants of health. Contemporary theoretical and methodological issues in social epidemiology.

Semester: Fall (even years) Credits: 3
Prerequisite: EPI 810
Instructor: Dr. Claire Margerision / Syllabus

 

STT 825 SAMPLE SURVEYS

Application of statistical sampling theory to survey designs. Simple random, stratified, and systematic samples. Sub-sampling, double sampling. Ratio and regression estimators.

Semester: Fall (Every year) Credits: 3
Prerequisite: N/A 

 

STT 861 THEORY OF PROBABILITY & STATISTICS I

Probability models, random variables and vectors. Special distributions including exponential family. Expected values, covariance matrices, moment generating functions. Convergence in probability and distribution. Weak Law of Large Numbers and Lyapunov Central Limit Theorem.

Semester: Fall (every) Credits: 3
Prerequisite: N/A

 

STT 873 STATISTICAL LEARNING & DATA MINING

Statistical methods focusing on machine learning and data mining, modern regression and classification techniques, support vector machines, boosting, kernel methods and ensemble methods, clustering dimension reduction, manifold learning, and selected topics.

Semester: Fall (odd years) Credits: 3
Prerequisite: STT 868 and STT 872

 

EC 821A CROSS SECTION & PANEL DATA ECONOMETRICS I

Analyses of systems of equations, panel data models, instrumental variables and generalized method of moments, M-estimation, quantile regression, maximum likelihood estimation, binary and multinomial response models, Tobit and two-part models, and other selected topics.

Semester: Fall (every year) Credits: 3
Prerequisite: EC 820B 

 

CSE 881 DATA MINING

Techniques and algorithms for knowledge discovery in databases, from data preprocessing and transformation to model validation and post-processing. Core concepts include association analysis, sequential pattern discovery, anomaly detection, predictive modeling, and cluster analysis. Application of data mining to various application domains.

Semester: Fall (every year) Credits: 3
Prerequisite: N/A 


Spring Semester

EPI 815 EPIDEMIOLOGY OF CARDIOVASCULAR DISEASE

Survey of methodologies used in epidemiologic studies of cardiovascular diseases. Review of evidence of genetic, environmental, and behavioral causes of cardiovascular disease.

Semester: Spring (even years) Credits: 3
Prerequisite: N/A
Instructor: Dr. Mat Reeves / Syllabus

 

EPI 816 PERINATAL EPIDEMIOLOGY

Epidemiology of adverse health states in pregnancy and the puerperium. Impact of these health states on subsequent child development.

Semester: Spring (Even years) Credits: 3
Prerequisite: N/A
Instructor: Dr. Nigel Paneth / Syllabus

 

EPI 823 CANCER EPIDEMIOLOGY

Basic principles of carcinogenesis. Major etiologic factors, types of malignancies, and biomarkers for susceptibility and exposure. Prevention and early detection of cancer.

Semester: Spring (odd years) Credits: 3
Prerequisite: EPI 810 & (EPI 809 OR EPI 808B)
Instructor: Dr. Dorothy Pathak / Syllabus

 

EPI 858 CLINICAL TRIALS I

Statistical methods for design and analysis of clinical trials and epidemiological studies. Phase I, II, and III clinical trials. Principle of Intention-to-Treat, effects of non-compliance, drop-outs. Interim monitoring of clinical trials and data safety monitoring boards. Meta-analysis. Crossover designs. Sample size and power in clinical trials. Sequential designs.

Semester: Spring (Even years) Credits: 3
Prerequisite: EPI 808B or (EPI 809 or LCS 829)
Instructor Dr. Dorothy Pathak / Syllabus

 

EPI 859 CLINICAL TRIALS II

Course not available yet

 

EPI 920 ADVANCED METHODS IN EPIDEMIOLOGY AND APPLIED STATISTICS

Pattern recognition and cluster analysis, longitudinal data analysis, path analysis, repeated measures and time-series analysis.

Semester: Spring (every year) Credits: 3
Prerequisite: EPI 826B OR EPI 826
Instructor: Dr. David Todem / Syllabus

 

EPI 952 DURATION AND SEVERITY ANALYSIS

Analysis of data that involve time to occurrence of a single event or multiple durations between occurrences of several events; modeling techniques; survival analysis in clinical and public health studies; frailty models; experimental and non-experimental applications using major statistical software.

Semester: Spring (odd years) Credits: 3
Prerequisite: EPI 826B OR EPI 826
Instructor: Dr. Joseph Gardiner / Syllabus

 

CSE 480 DATABASE SYSTEMS

Storage of and access to physical databases including indexing, hashing, and range accesses. Relational data models, database design principles, query languages, query optimization, transaction processing and recovery techniques. Object-oriented and distributed databases.

Semester: Spring (every year) Credits: 3
Prerequisite: CSE 331 or CSE 335

 

EPI 979 ADVANCED TOPICS OF INFECTIOUS DISEASE EPIDEMIOLOGY

Epidemiological and public health perspectives on the etiology, transmission and prevention of infectious diseases. Key conceptual and methodological issues associated with studying infectious diseases from molecular and population based perspectives.

Semester: Spring (even years) Credits: 3
Prerequisite: N/A
Instructor: Dr. Lixin Zhang / Syllabus

 

STT 814 ADVANCED STATISTICS FOR BIOLOGISTS

Concepts of reducing experimental error for biological and agricultural research. Covariance, randomized block designs, latin squares, split plots, repeated-measures designs, regression applications, and response surface designs. Analyses using statistical software.

Semester: Spring (every year) Credits: 3
Prerequisite: N/A

 

STT 847 ANALYSIS OF SURVIVAL DATA

Analysis of lifetime data. Estimation of survival functions for parametric and nonparametric models. Censored data. The Cox proportional hazards model. Accelerated failure time models. Frailty models. Use of statistical software packages.

Semester: Spring (odd years) Credits: 3
Prerequisite: N/A

 

STT 862 THEORY OF PROBABILITY & STATISTICS II

Statistical inference: sufficiency, estimation, confidence intervals and testing of hypotheses. One and two sample nonparametric tests. Linear models and Gauss-Markov Theorem.

Semester: Spring (every year) Credits: 3
Prerequisite: STT 861 

 

EC 821B CROSS SECTION & PANEL DATA ECONOMETRICS II

Analyses of quasi-maximum likelihood estimation, count data models, fractional response models, duration models, sample selection and attrition, stratified sampling, estimating treatment effects, stochastic frontier models, and other advanced topics.

Semester: Spring (every year) Credits: 3
Prerequisite: EC 821A 

 

CSE 482 BIG DATA ANALYSIS

Data collection, storage, and preprocessing, and analysis techniques. Programming for large-scale data analysis. Case studies and applications.

Semester: Spring (every year) Credits: 3
Prerequisite: CSE 331 and (STT 351 or STT 380 or STT 430 or STT 441)

 

CSE 847 MACHINE LEARNING

Computational study of learning and data mining. Strengths and limitations of various learning paradigms, including supervised learning, learning from scalar reward, unsupervised learning, and learning with domain knowledge.

Semester: Spring (every year) Credits: 3
Prerequisite: CSE 841 


Summer Semester

EPI 880 SELECT TOPICS IN BIOSTATISTICS

Select topics in biostatistics including global disease distribution and estimation, causal inference, Bayesian methods in health services research.

Semester: Summer (Every year) Credits: 3
Prerequisite: EPI 808B or (EPI 808 & EPI 809) or (PHM 830 OR STT 464)
Instructor Dr. David Todem / Syllabus

 

EPI 950 ADVANCED BIOSTATISTICAL METHODS IN EPIDEMIOLOGY

Study of specific biostatistical methods and epidemiology applications.

Semester: Fall (even years) Credits: 3
Prerequisite: EPI 826B OR EPI 826
Instructor: Dr. Zhehui Luo / Syllabus


Any Semester

EPI 990 INDEPENDENT STUDY (subject not taught in another course)

Special projects, directed reading, and research arranged by an individual graduate student and a faculty member in areas supplementing regular course offerings.

Semester: Any  Credits: 2
Prerequisite: N/A 

 

CSE 231 INTRODUCTION TO PROGRAMMING I

Introduction to programming using Python. Design, implementation and testing of programs to solve problems such as those in engineering, mathematics and science. Programming fundamentals, functions, objects, and use of libraries of functions.

Semester: All (every year) Credits: 4
Prerequisite: LB 118 OR MTH 124 OR MTH 132 OR MTH 152H

 

CSE 232 INTRODUCTION TO PROGRAMMING II

Continuation of object-centered design and implementation in C++. Building programs from modules. Data abstraction and classes to implement abstract data types. Static and dynamic memory allocation. Data structure implementation and algorithm efficiency. Lists, tables, stacks, and queues. Templates and generic programming.

Semester: All (every year) Credits: 4
Prerequisite: CSE 231 and LB 118 or MTH 124 or MTH 132 or MTH 152H

 

CSE 331 ALGORITHMS AND DATA STRUCTURES

Linear data structures, trees, graphs and algorithms which operate on them. Fundamental algorithms for searching, sorting, string matching, graph problems. Design and analysis of algorithms.

Semester: All (every year) Credits: 3
Prerequisite: CSE 232 and (CSE 260 or CMSE 202)

 


DISSERTATION 

EPI 999 PhD DISSERTATION RESEARCH

Doctoral dissertation research.

Semester: Fall, Spring and Summer (every year) Credits: 24
Prerequisite: N/A

Flexible emphasis, where the program will cover methods and applications in the context of biomedical studies.

REQUIRED COURSES

Complete all of the following courses (13 credits)


Fall Semester

EPI 828 RESPONSIBLE CONDUCT OF RESEARCH

Ethical and regulatory issues in the responsible conduct of epidemiology research. Topics include informed consent; scientific misconduct; human subjects protection; responsible data management including electronic medical records, biological samples and genetic data; HIPAA compliance; and other current issues of scientific integrity.

Semester: Fall (Every year) Credits: 3
Prerequisite: EPI 810
Instructor: Dr. David Barondess / Syllabus

 

EPI 860 ADVANCED INFERENCE FOR BIOSTATISTICS

Semester: Spring (Every year) Credits: 3
Prerequisite: EPI 808B or (EPI 809 or LCS 829)
Instructor Dr. David Todem  /  Syllabus

 

STT 867 LINEAR MODEL METHODOLOGY

Properties of the multivariate normal distribution, Cochran's Theorem, simple and multiple linear regression models, Gauss-Markov Theorem, best linear unbiased prediction, one- and two-way ANOVA models, sums of squares, diagnostics and model selection, contingency tables and multinomial models, generalized linear models, logistic regression. /p>

Semester: Fall (Every year) Credits: 3
Prerequisite: STT 862
Instructor   /  Syllabus

 

STT 873 STATISTICAL LEARNING & DATA MINING

Statistical methods focusing on machine learning and data mining, modern regression and classification techniques, support vector machines, boosting, kernel methods and ensemble methods, clustering dimension reduction, manifold learning, and selected topics.

Semester: Fall (odd years) Credits: 3
Prerequisite: N/A

 

EC 821A CROSS SECTION & PANEL DATA ECONOMETRICS I

Analyses of systems of equations, panel data models, instrumental variables and generalized method of moments, M-estimation, quantile regression, maximum likelihood estimation, binary and multinomial response models, Tobit and two-part models, and other selected topics.

Semester: Fall (every year) Credits: 3
Prerequisite: EC 820B


Spring Semester

STT 868 MIXED MODELS: THEORY, METHOD & APPLICATIONS

Maximum likelihood estimation and other estimation methods for linear mixed models. Statistical properties of LME models. Prediction under LME models. Generalized linear mixed models. Quasi-likelihood estimation, generalized estimating equations for GLMM. Nonlinear mixed models. Diagnostics and influence analysis. Bayesian development in mixed linear models. Application of mixed models.

Semester: Spring (Every year) Credits: 3
Prerequisite: STT 867
Instructor   /  Syllabus

 


ELECTIVES

Complete 14 credits of elective course work from the below list of approved courses.
Note: Additional elective courses may be chosen with advisor’s approval.


Fall Semester

EPI 810 INTRODUCTORY EPIDEMIOLOGY

Disease from a population perspective as the interaction of host, agent, and environment. Case definition, measuring frequency of disease, mortality and morbidity data, and major study designs.

Semester: Fall (Every year) Credits: 3
Prerequisite: N/A
Instructor: Dr. Honglei Chen / Syllabus

 

EPI 812 CAUSAL INFERENCE

Causality in epidemiology. Application of theoretical concepts to the design, analysis, and assessment of epidemiologic research.

Semester: Fall (Every year) Credits: 3br /> Prerequisite: EPI 810
Instructor: Dr. Claudia Holzman / Syllabus

 

EPI 817 COMMUNICABLE DISEASE

Application of principles of epidemiology to research in communicable diseases relevant to public health in the U.S. and other countries.

Semester: Fall (Every year) Credits: 3
Prerequisite: EPI 810
Instructor: Dr. Lixin Zhang / Syllabus

 

EPI 835 NEUROEPIDEMIOLOGY

Epidemiology of neurologic and neuropsychiatric disorders with emphases on neurodegenerative disorders (e.g., Alzheimer's disease).

Semester: Fall (odd years) Credits: 3
Prerequisite: EPI 810
Instructor: Dr. Jim Anthony / Syllabus

 

EPI 855 Biostatistical Modeling in Genomic Data Analysis

Introduction to fundamental principles and modeling of genomic /genetic data and computational techniques

Semester: Fall (Every year) Credits: 3
Prerequisite: EPI 808B & EPI 826 or 826B
Instructor: Dr. Gustavo de los Campos / Syllabus

 

EPI 950 ADVANCED BIOSTATISTICAL METHODS IN EPIDEMIOLOGY

Study of specific biostatistical methods and epidemiology applications.

Semester: Fall (even years) Credits: 3
Prerequisite: EPI 826B OR EPI 826
Instructor: Dr. Zhehui Luo / Syllabus

 

EPI 953 ANALYTICAL STRATEGIES FOR OBSERVATIONAL STUDIES

Models and methods such as propensity scores, instrumental variables, regression discontinuity design, discrete choice analysis, and marginal structural models. Examples will be demonstrated with procedures in major statistical software.

Semester: Fall (odd years) Credits: 3
Prerequisite: EPI 826B OR EPI 826
Instructor: Dr. Zhehui Luo / Syllabus

 

EPI 977 SOCIAL EPIDEMIOLOGY

Introduction to the field of social epidemiology and the social determinants of health. Contemporary theoretical and methodological issues in social epidemiology.

Semester: Fall (even years) Credits: 3
Prerequisite: EPI 810
Instructor: Dr. Claire Margerision / Syllabus

 

STT 465 BAYESIAN STATISTICAL METHODS

Probability, belief, and exchangeability. Objective, subjective, and empirical Bayes approaches. Applications to one-parameter models, linear regression models, and multivariate normal models. Hierarchical modeling. Computational methods.

Semester: Fall (every year) Credits: 3
Prerequisite: STT 422

 

STT 801 DESIGN OF EXPERIMENTS

Blocking and randomization. Split-plot, latin square and factorial designs. Fractional factorial designs, aliasing and confounding of effects. Mixture and central composite designs and response surface exploration. Clinical trials.

Semester: Fall (every year) Credits: 3
Prerequisite: N/A

 

STT 814 ADVANCED STATISTICS FOR BIOLOGISTS

Concepts of reducing experimental error for biological and agricultural research. Covariance, randomized block designs, latin squares, split plots, repeated-measures designs, regression applications, and response surface designs. Analyses using statistical software.

Semester: Fall (every year) Credits: 3
Prerequisite: N/A

 

STT 825 SAMPLE SURVEYS

Application of statistical sampling theory to survey designs. Simple random, stratified, and systematic samples. Sub-sampling, double sampling. Ratio and regression estimators.

Semester: Fall (Every year) Credits: 3
Prerequisite: N/A

 

STT 855 STATISTICAL GENETICS

Probabilistic and statistical methods for genetic linkage and association studies. Quantitative trait locus mapping.

Semester: Fall (odd years) Credits: 3
Prerequisite: N/A

 

STT 861 THEORY OF PROBABILITY & STATISTICS I

Probability models, random variables and vectors. Special distributions including exponential family. Expected values, covariance matrices, moment generating functions. Convergence in probability and distribution. Weak Law of Large Numbers and Lyapunov Central Limit Theorem.

Semester: Fall (every) Credits: 3
Prerequisite: N/A

 

CSE 881 DATA MINING

Techniques and algorithms for knowledge discovery in databases, from data preprocessing and transformation to model validation and post-processing. Core concepts include association analysis, sequential pattern discovery, anomaly detection, predictive modeling, and cluster analysis. Application of data mining to various application domains.

Semester: Fall (every year) Credits: 3
Prerequisite: N/A 


Spring Semester

EPI 815 EPIDEMIOLOGY OF CARDIOVASCULAR DISEASE

Survey of methodologies used in epidemiologic studies of cardiovascular diseases. Review of evidence of genetic, environmental, and behavioral causes of cardiovascular disease.

Semester: Spring (even years) Credits: 3
Prerequisite: N/A
Instructor: Dr. Mat Reeves / Syllabus

 

EPI 816 PERINATAL EPIDEMIOLOGY

Epidemiology of adverse health states in pregnancy and the puerperium. Impact of these health states on subsequent child development.

Semester: Spring (Even years) Credits: 3
Prerequisite: N/A
Instructor: Dr. Nigel Paneth  /  Syllabus

 

EPI 823 CANCER EPIDEMIOLOGY

Basic principles of carcinogenesis. Major etiologic factors, types of malignancies, and biomarkers for susceptibility and exposure. Prevention and early detection of cancer.

Semester: Spring (odd years) Credits: 3
Prerequisite: EPI 810 & (EPI 809 OR EPI 808B)
Instructor: Dr. Dorothy Pathak  /  Syllabus

 

EPI 853B Statistical Computing

Prerequisite: EPI 808B and EPI 826B

Statistical computation and algorithms using programming languages, SAS/IML, R and/or Stata, Newton-Raphson method, Monte Carlo simulation of probability distributions, bootstrap, statistical graphics.

Semester: Spring (Every year) Credits: 3
Prerequisite: EPI 808B & EPI 826
Instructor: Dr. Gustavo de los Campos / Syllabus

 

EPI 858 CLINICAL TRIALS I

Statistical methods for design and analysis of clinical trials and epidemiological studies. Phase I, II, and III clinical trials. Principle of Intention-to-Treat, effects of non-compliance, drop-outs. Interim monitoring of clinical trials and data safety monitoring boards. Meta-analysis. Crossover designs. Sample size and power in clinical trials. Sequential designs.

Semester: Spring (Even years) Credits: 3
Prerequisite: EPI 808B or (EPI 809 or LCS 829)
Instructor Dr. Dorothy Pathak  /  Syllabus

 

EPI 859 CLINICAL TRIALS II

Course not available yet

 

EPI 920 ADVANCED METHODS IN EPIDEMIOLOGY AND APPLIED STATISTICS

Pattern recognition and cluster analysis, longitudinal data analysis, path analysis, repeated measures and time-series analysis.

Semester: Spring (every year) Credits: 3
Prerequisite: EPI 826B OR EPI 826
Instructor: Dr. David Todem / Syllabus

 

EPI 952 DURATION AND SEVERITY ANALYSIS

Analysis of data that involve time to occurrence of a single event or multiple durations between occurrences of several events; modeling techniques; survival analysis in clinical and public health studies; frailty models; experimental and non-experimental applications using major statistical software.

Semester: Spring (odd years) Credits: 3
Prerequisite: EPI 826B OR EPI 826
Instructor: Dr. Joseph Gardiner / Syllabus

 

EPI 952 DURATION & SEVERITY ANALYSIS

Analysis of data that involve time to occurrence of a single event or multiple durations between occurrences of several events; modeling techniques; survival analysis in clinical and public health studies; frailty models; experimental and non-experimental applications using major statistical software.

Semester: Spring (odd yearsr) Credits: 3
Prerequisite: EPI 826or EPI 826B
Instructor Dr. Joseph Gardiner / Syllabus

 

EPI 979 ADVANCED TOPICS OF INFECTIOUS DISEASE EPIDEMIOLOGY

Epidemiological and public health perspectives on the etiology, transmission and prevention of infectious diseases. Key conceptual and methodological issues associated with studying infectious diseases from molecular and population based perspectives.

Semester: Spring (even years) Credits: 3
Prerequisite: N/A
Instructor: Dr. Lixin Zhang / Syllabus

 

STT 847 ANALYSIS OF SURVIVAL DATA

Analysis of lifetime data. Estimation of survival functions for parametric and nonparametric models. Censored data. The Cox proportional hazards model. Accelerated failure time models. Frailty models. Use of statistical software packages.

Semester: Spring (odd years) Credits: 3
Prerequisite: N/A 

 

STT 862 THEORY OF PROBABILITY & STATISTICS II

Statistical inference: sufficiency, estimation, confidence intervals and testing of hypotheses. One and two sample nonparametric tests. Linear models and Gauss-Markov Theorem.

Semester: Springl (every year) Credits: 3
Prerequisite: STT 861

 

EC 821B CROSS SECTION & PANEL DATA ECONOMETRICS I

Analyses of quasi-maximum likelihood estimation, count data models, fractional response models, duration models, sample selection and attrition, stratified sampling, estimating treatment effects, stochastic frontier models, and other advanced topics.

Semester: Spring (every year) Credits: 3
Prerequisite: EC 820B

 

CSE 480 DATABASE SYSTEMS

Storage of and access to physical databases including indexing, hashing, and range accesses. Relational data models, database design principles, query languages, query optimization, transaction processing and recovery techniques. Object-oriented and distributed databases.

Semester: Spring (every year) Credits: 3
Prerequisite: CSE 331 or CSE 335

 

CSE 482 BIG DATA ANALYSIS

Data collection, storage, and preprocessing, and analysis techniques. Programming for large-scale data analysis. Case studies and applications.

Semester: Spring (every year) Credits: 3
Prerequisite: CSE 331 and (STT 351 or STT 380 or STT 430 or STT 441)

 

CSE 847 MACHINE LEARNING

Computational study of learning and data mining. Strengths and limitations of various learning paradigms, including supervised learning, learning from scalar reward, unsupervised learning, and learning with domain knowledge.

Semester: Spring (every year) Credits: 3
Prerequisite: CSE 841  


Summer Semester

EPI 880 SELECT TOPICS IN BIOSTATISTICS

Select topics in biostatistics including global disease distribution and estimation, causal inference, Bayesian methods in health services research.

Semester: Summer (Every year) Credits: 3
Prerequisite: EPI 808B or (EPI 808 & EPI 809) or (PHM 830 OR STT 464)
Instructor Dr. David Todem / Syllabus


Any Semester

EPI 990 INDEPENDENT STUDY (subject not taught in another course)

Special projects, directed reading, and research arranged by an individual graduate student and a faculty member in areas supplementing regular course offerings.

Semester: Any  Credits: 3
Prerequisite: N/A 

 

CSE 231 INTRODUCTION TO PROGRAMMING I

Introduction to programming using Python. Design, implementation and testing of programs to solve problems such as those in engineering, mathematics and science. Programming fundamentals, functions, objects, and use of libraries of functions.

Semester: All (every year) Credits: 4
Prerequisite: LB 118 OR MTH 124 OR MTH 132 OR MTH 152H

 

CSE 232 INTRODUCTION TO PROGRAMMING II

Continuation of object-centered design and implementation in C++. Building programs from modules. Data abstraction and classes to implement abstract data types. Static and dynamic memory allocation. Data structure implementation and algorithm efficiency. Lists, tables, stacks, and queues. Templates and generic programming.

Semester: All (every year) Credits: 4
Prerequisite: CSE 231 and LB 118 or MTH 124 or MTH 132 or MTH 152H

 

CSE 331 ALGORITHMS AND DATA STRUCTURES

Linear data structures, trees, graphs and algorithms which operate on them. Fundamental algorithms for searching, sorting, string matching, graph problems. Design and analysis of algorithms.

Semester: All (every year) Credits: 3
Prerequisite: CSE 232 and (CSE 260 or CMSE 202)


DISSERTATION

EPI 999 PhD DISSERTATION RESEARCH

Doctoral dissertation research.

Semester: Spring (every year) Credits: 24
Prerequisite: N/A

  • Doctoral students are required to participate in a monthly Ph.D. Journal Club over the course of four (4) semesters.
  • Students are also expected to attend the department’s Epidemiology and Biostatistics Seminar Series each semester.
  • After earning an MS or MPH in Epi, students have up to 8 years to complete all requirements and successfully defend their doctoral dissertation.
PhD Schedule