Graduate Group in Epidemiology & Biostatistics

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Courses in Biostatistics and Statistics

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The Center for Clinical Epidemiology and Biostatistics, the Department of Biostatistics and Epidemiology, and the Graduate Group in Epidemiology and Biostatistics offer a wide range of courses; a brief description of current offerings is provided below. Not all courses are offered every year. The program may revise these courses over time; the descriptions given here are for guidance only.

 

 


BSTA 509: Introductory Epidemiology
(EPID 801)

• Fall term
• 0.5 credit unit
• Instructor (s): TBA
• Prerequisites: Permission of instructor.

Description: This course is a series of lectures designed to teach basic principles of epidemiologic research. It provides an overview of the types of research questions that can be addressed by epidemiologic methods. Topics covered include definitions of epidemiology; measures of disease frequency; measures of effect and association; epidemiologic study designs, both experimental and non-experimental; data collection methods; and an overview of analysis of epidemiologic studies. (The lectures for this course are identical to those in EPID 801.)



BSTA 510: Introduction to Human Health and Diseases

• Fall term (Course no longer offered)
• 0.5 credit unit
• Instructor (s): John Farrar, MD, PhD
• Prerequisites: Permission of instructor.

Description: This course is a series of lectures designed to teach basic principles of epidemiologic research. It provides an overview of the types of research questions that can be addressed by epidemiologic methods. Topics covered include definitions of epidemiology; measures of disease frequency; measures of effect and association; epidemiologic study designs, both experimental and non-experimental; data collection methods; and an overview of analysis of epidemiologic studies. (The lectures for this course are identical to those in EPID 801.)
 

BSTA 511: Biostatistics in Practice I

• Fall/Spring Term (offered to Biostatistics students only)
• 0.5 credit unit
• Instructor (s): TBA
• Prerequisites: Open to Biostatistics students only.

 


BSTA 512: Database Management for Clinical Epidemiology I (EPID 532)

 


BSTA 513: Measurement of Health in Epidemiology
(EPID 542)

 

BSTA 514: Clinical Economics and Clinical Decision Making
(EPID 550)
 

BSTA 550: Applied Regression and Analysis of Variance
(STAT 500)
 


BSTA 620: Probability

• Fall term
• 1.0 credit unit
• Instructor : TBD
• Prerequisites: Two semesters of calculus (through multivariable calculus), linear algebra; permission of instructor.

Description: This core course covers elements of (non-measure theoretic) probability necessary for the further study of statistics and biostatistics. Topics include set theory, axioms of probability, counting arguments, conditional probability, random variables and distributions, expectations, generating functions, families of distributions, joint and marginal distributions, hierarchical models, covariance and correlation, random sampling, sampling properties of statistics, modes of convergence, and random number generation.

 


BSTA 621: Statistical Inference I

• Spring term
• 1.0 credit unit
• Instructor: Haochang Shou, PhD
• Prerequisites: BSTA 620; permission of instructor.

Description: This class will cover the fundamental concepts of statistical inference. Topics include sufficiency, consistency, finding and evaluating point estimators, finding and evaluating interval estimators, hypothesis testing, and asymptotic evaluations for point and interval estimation.



BSTA 622: Statistical Inference II

• Spring term
• 1.0 credit unit
• Instructors: Jinbo Chen, PhD and Yong Chen, PhD
• Prerequisites: BSTA 620; permission of instructor.

Description: This class will cover the fundamental concepts of statistical inference. Topics include sufficiency, consistency, finding and evaluating point estimators, finding and evaluating interval estimators, hypothesis testing, and asymptotic evaluations for point and interval estimation.

 


BSTA 630: Statistical Methods and Data Analysis I

• Fall term
• 1.0 credit unit
• Instructors: Yenchih Jesse Hsu, PhD and Dawei Xie, PhD
• Prerequisites: Multivariable calculus and linear algebra, BSTA 620 (may be taken concurrently); permission of instructor.

Description: This first course in statistical methods for data analysis is aimed at first-year Biostatistics students. It focuses on the analysis of continuous data. Topics include descriptive statistics (measures of central tendency and dispersion, shapes of distributions, graphical representations of distributions, transformations, and testing for goodness of fit); populations and sampling (hypotheses of differences and equivalence, statistical errors); one- and two-sample t tests; analysis of variance; correlation; nonparametric tests on means and correlations; estimation (confidence intervals and robust methods); categorical data analysis (proportions; statistics and test for comparing proportions; test for matched samples; study design); and regression modeling (simple linear regression, multiple regression, model fitting and testing, partial correlation, residuals, multicollinearity). Examples of medical and biologic data will be used throughout the course, and use of computer software demonstrated.



BSTA 631: Statistical Methods and Data Analysis II

• Spring term (NOTE: Course replaced by BSTA 632: Statistical Methods for Categorical and Survival Data)
• 1.0 credit unit
• Instructor (s):
• Prerequisites: linear algebra, calculus, BSTA 630, BSTA 620, BSTA 621 (may be taken concurrently); permission of instructor. 

Description: This is the second half of the methods sequence, where the focus shifts to methods for categorical and survival data. Topics in categorical include defining rates; incidence and prevalence; the chi-squared test; Fisher's exact test and its extension; relative risk and odds-ratio; sensitivity; specificity; predictive values; logistic regression with goodness of fit tests; ROC curves; the Mantel-Haenszel test; McNemar's test; the Poisson model; and the Kappa statistic. Survival analysis will include defining the survival curve, censoring, and the hazard function; the Kaplan-Meier estimate, Greenwood's formula and confidence bands; the log rank test; and Cox's proportional hazards regression model. Examples of medical and biologic data will be used throughout the course, and use of computer software demonstrated.



BSTA 632: Statistical Methods for Categorical and Survival Data
 

• Spring term
• 1.0 credit unit
• Instructors: Warren Bilker, PhD
• Prerequisites: Linear algebra, calculus, BSTA 630, BSTA 620, BSTA 621 (may be taken concurrently); permission of instructor.

Description: This is the second half of the methods sequence, where the focus shifts to methods for categorical and survival data. Topics in categorical include defining rates; incidence and prevalence; the chi-squared test; Fisher's exact test and its extension; relative risk and odds-ratio; sensitivity; specificity; predictive values; logistic regression with goodness of fit tests; ROC curves; the Mantel-Haenszel test; McNemar's test; the Poisson model; and the Kappa statistic. Survival analysis will include defining the survival curve, censoring, and the hazard function; the Kaplan-Meier estimate, Greenwood's formula and confidence bands; the log rank test; and Cox's proportional hazards regression model. Examples of medical and biologic data will be used throughout the course, and use of computer software demonstrated.



BSTA 651: Introduction to Linear Models and Generalized Linear Models

• Spring term
• 1.0 credit unit
• Instructors: Mary D. Sammel, ScD and Justine Shults
• Prerequisites: Linear algebra, calculus, BSTA 620, BSTA 630. BSTA 621 and BSTA 632 (may be taken concurrently); permission of instructor.

Description: This course extends the content on linear models in BSTA 630 and BSTA 631 to more advanced concepts and applications of linear models. Topics include the matrix approach to linear models including regression and analysis of variance; multiple linear regression, collinearity diagnostics; multiple comparisons; fitting strategies; simple experimental designs (block designs, split plot); and prediction. In addition, generalized linear models will be introduced with emphasis on the binomial, logit and Poisson log-linear models. Applications of methods to example datasets will be emphasized.
 


BSTA 652: Categorical Data Analysis

• Fall term (Course no longer offered)
• 1.0 credit unit
• Instructor (s):
• Prerequisites: BSTA 621, BSTA 631, BSTA 651; permission of instructor.

Description: This course elaborates on the treatment of categorical data analysis in Statistical Methods I and II. Topics include probability models for contingency tables, estimation of odds ratios, exact and asymptotic tests of independence, generalized linear models (logit, complementary log-log, and loglinear), ordinal regression models, Mantel-Haenszel tests, and estimation.

 


BSTA 653: Survival Analysis
 

• Fall term (NOTE: Course replaced by BSTA 754: Advanced Survival Analysis as of Fall 2015)
• 1.0 credit unit
• Instructor (s):
• Prerequisites: BSTA 621, BSTA 631, BSTA 651; permission of instructor.

Description: This course extends the methods for the analysis of time to event data or survival analysis covered in BSTA 631. Concepts include survival distributions, hazard distributions, censoring mechanisms and truncation mechanisms. Parametric and nonparametric methods for estimation and inference will be covered, including the Kaplan-Meier estimator, exponential and Weibull models, logrank tests, the generalized Wilcoxon test, the Cox proportional hazards regression and extensions to time-dependent covariates.  

 


BSTA 656: Longitudinal Data Analysis

• Spring term
• 1.0 credit unit
• Instructor: Matthew Bryan, PhD
• Prerequisites: BSTA 621, BSTA 631 or 632, BSTA 651, BSTA 653 or 754; permission of instructor.

Description: This course covers both the applied aspects and methods developments in longitudinal data analysis. In the first part, we review the properties of the multivariate normal distribution and cover basic methods in longitudinal data analysis, such as exploratory data analysis, two-stage analysis and mixed-effects models. Focus is on the linear mixed-effects models, where we cover restricted maximum likelihood estimation, estimation and inference for fixed and random effects and models for serial correlations. We will also coverBayesian inference for linear mixed-effects models.The second part covers advanced topics, including nonlinear mixed-effects models, GEE, generalized linear mixed-effects models, nonparametric longitudinal models, functional mixed-effects models, and joint modeling of longitudinal data and the dropout mechanism.

 


BSTA 657: Design of Biomedical Studies I

• Spring term (Course no longer offered)
• 0.5 credit unit
• Instructor (s):
• Prerequisites: BSTA 621, BSTA 631, BSTA 651, BSTA 652, BSTA 653; permission of instructor.

Description: This course is an introduction to the statistical planning and design of biomedical investigations. It introduces the classical theory of experimental design using case studies in biomedical research as illustrations. Topics include randomization, blocking, complete and incomplete block designs, factorial designs, sample size estimation, random vs fixed effects, and practical applications.

 


BSTA 658: Design of Biomedical Studies II

• Spring term (Course no longer offered)
• 0.5 credit unit
• Instructor (s):
• Prerequisites: BSTA 621, BSTA 631, BSTA 651, BSTA 652, BSTA 653; BSTA 657 highly recommended; permission of instructor.

Description: This course builds on the basic theory of experimental design in biomedical investigations by focusing on statistical topics and randomized trials. Case studies will be used to illustrate the methods.

 


BSTA 660: Design of Observational Studies

• Spring term
• 1.0 credit unit
• Instructor (s): TBA
• Prerequisites: BSTA 621, BSTA 631 or BSTA 632, BSTA 651; permission of instructor.

Description: This course will cover statistical methods for the design and analysis of observational studies.  Topics for the course will include epidemiologic study designs, issues of confounding and hidden bias, matching methods, propensity score methods, sensitivity analysis, and instrumental variables. Case studies in biomedical researchwill be presented as illustrations.
 


BSTA 661: Design of Interventional Studies

• Spring term
• 1 credit unit
• Instructor: Kathleen J. Propert, ScD
• Prerequisites: BSTA 621, BSTA 631 or BSTA 632; permission of instructor.

Description: This course is designed for graduate students in statistics or biostatistics interested in the statistical methodology underlying the design, conduct, and analysis of clinical trials and related interventional studies. General topics include designs for various types of clinical trials (Phase I, II, III), endpoints and control groups, sample size determination, and sequential methods and adaptive design. Regulatory and ethical issues will also be covered.
 


BSTA 670: Statistical Computing

• Fall term
• 1.0 credit unit
• Instructor: Sarah J. Ratclliffe, PhD
• Prerequisites: BSTA 651,BSTA 620, BSTA 621 or equivalents, or permission of instructor.

Description: This course concentrates on computational tools, which are useful for statistical research and for computationally intensive statistics. Through this course you will develop a knowledge base and skill set of a wide range of computational tools needed for statistical research. Topics include computer storage, architecture and arithmetic; random number generation; numerical optimization methods; spline smoothing and penalized likelihood; numerical integration; simulation design; Gibbs sampling; bootstrap methods; and the EM algorithm.

 


BSTA 751: Statistical Methods for Neuroimaging

• Spring term
• 1.0 credit unit
• Instructor: Russell Taki Shinohara, PhD
• Prerequisites: BSTA 621, BSTA 651; permission of instructor.

Description:This course is intended for students interested in both statistical methodology, and the process of developing this methodology, for the field of neuroimaging. This will include quantitative techniques that allow for inference and prediction from ultra-high dimensional and complex images. In this course, basics of imaging neuroscience and preprocessing will be covered to provide students with requisite knowledge to develop the next generation of statistical approaches for imaging studies. High-performance computational neuroscience tools and approaches for voxel- and region-level analyses will be studied. The multiple testing problem will be discussed, and the state-of-the art in the area will be examined. Finally, the course will end with a detailed study of multivariate pattern analysis, which aims to harness patterns in images to identify disease effects and provide sensitive and specific biomarkers. The student will be evaluated based on 3 homework assignments and a final in-class presentation.

 


BSTA 752: Categorical Data Analysis II

• Spring term (Course no longer offered)
• 1.0 credit unit
• Instructor (s): TBA
• Prerequisites: BSTA 652; permission of instructor.

Description: In this course, students present and discuss methodological papers chosen by the instructor from the literature on advanced categorical methods in a variety of areas. These areas include accounting for correlated data with population-averaged and random and fixed effects models fitted with estimation procedures including generalized estimating equations, maximum likelihood, penalize quasilikelihood, and conditional likelihood methods. Additional topics including accommodating non-ignorable missing data, confounding by cluster, treatment non-adherence in randomized trials, mediation analysis, and latent class and latent variable models. Software for implementing these methods will also be considered in the context of some examples from medical research. The student presentations will be reviewed by peer students in the course, who will provide feedback. Grades will be based on the instructor evaluations of these presentations and ensuing discussion. In addition, a data analysis project will be handed in as part of the final grade. 

 


BSTA 753: Survival Analysis II

(NOTE: Course replaced by BSTA 754: Advanced Survival Analysys)
• 1.0 credit unit
• Instructor (s):
• Prerequisites: Prerequisites: BSTA 653, BSTA 622 (may be taken concurrently). 


Description: This course discusses the theoretical basis of concepts and methodologies associated with survival data and censoring, nonparametric tests, and competing risk models. Much of the theory is developed using counting processes and martingale methods. Material is drawn from recent literature.
 


BSTA 754: Advanced Survival Analysis

• Fall term
• 1.0 credit unit
• Instructor: Kevin G. Lynch, PhD and Sharon X. Xie,PhD
• Prerequisites: BSTA 622 (may be taken concurrently); permission of instructor.

Description: This advanced survival analysis course will cover statistical theory in counting processes, large sample theory using martingales, and other state of the art theoretical concepts useful in modern survival analysis research. Examples in deriving rank-based tests and Cox regression models as well as their asymptotic properties will be demonstrated using these theoretical concepts. Additional potential topics may include competing risk, recurrent event analysis, multivariate failure time analysis, joint modeling of survival and longitudinal data, sample size calculations, multistate models, and complex sampling schemes involving failure time data.
 


BSTA 770: Nonparametric Inference
(STAT 915)

 


BSTA 771: Applied Bayesian Analysis

• Spring term
• 1.0 credit unit
• Instructor: Jason A. Roy, PhD
• Prerequisites: BSTA 620, BSTA 621, BSTA 651; permission of instructor.

Description: This course introduces Bayesian methods from philosophical, theoretical, and practical perspectives. These methods are compared and contrasted with alternatives, such as maximum likelihood and semiparametric methods. Core topics include Bayes' theorem, the likelihood principle, selection of prior distributions (both informative and non-informative), and computational methods for sampling from the posterior distributions. Bayesian approaches to linear models, generalized linear models, and survival models are presented, along with methods for model checking and model choice such as posterior predictive distributions and Bayes factors. Computational methods include MCMC, Gibbs sampling, metropolis algorithms, and slice sampling. Advanced topics include Bayesian non-parametric models and data augmentation. The course emphasizes the development and estimation of hierarchical models as a means of modeling complicated real-world problems.

 


BSTA 774: Statistical Methods for Evaluating Diagnostic Tests

• Fall term
• 1.0 credit unit
• Instructor (s): TBA
• Prerequisites: BSTA 621 or equivalent; permission of instructor.

Description: Topics include estimation of ROC curves; comparison of multiple diagnostic tests; development of diagnostic tests using predictive models; effects of measurement errors; random-effects models for multi-reader studies; verification bias in disease classification; methods for time-dependent disease classifications; study design; related software; meta-analyses for diagnostic test data; and current topics in the statistical literature.

 

BSTA 775: Sample Survey Methods (STAT 920)

 


BSTA 779: Semiparametric Inferences and Biostatistics

• Spring term (Course not offered every year)
• 1.0 credit unit
• Instructor (s): TBA
• Prerequisites: The course is designed for students in biostatistics, statistics, or other strongly quantitative disciplines. BSTA 621/622 or equivalent; ability to program in R/S-Plus, SAS, Stata or Matlab; permission of the instructor. 

Description: This course will expose students to semiparametric inference theory through its applications to cutting-edge research topics in biostatistics, including two-phase design problems and modeling problems in genetic epidemiology. Thus, this course will benefit those who wish to advance their theoretical statistical training, those who wish to explore biostatistics research in the area of two-phase design problems and in genetic epidemiology, and those who wish to deepen their understanding of commonly used semiparametric biostatistical methods such as partial likelihood inference for Cox regression and the prospective analysis of retrospective case-control studies.

 


BSTA 781: Asymptotic Theory with Biomedical and Psychosocial Applications

• Fall term (Course not offered every year)
• 1.0 credit unit
• Instructor (s): TBA
• Prerequisites: BSTA 621, BSTA 622, BSTA 630, BSTA 631 or BSTA 632, BSTA 651; permission of instructor.

Description: This course is an introduction to the asymptotic theory of statistics, with an array of applications to motivate as well as demonstrate its utility in addressing problems in biomedicine and psychosocial research. Notions of convergence of random sequences and common asymptotic techniques are introduced without measure theory. In addition to classical likelihood-based asymptotic theory, this course also focuses on distribution-free inference from estimating equations and U-statistics. Examples from AIDS, genetic, and psychosocial research are presented to motivate the methods development and to demonstrate the utility of the asymptotic theory.

 


BSTA 782: Statistical Methods for Incomplete Data

• Spring term (Course not offered every year)
• 1.0 credit unit
• Instructor (s): TBA
• Prerequisites: BSTA 621 required; BSTA 670 recommended; permission of instructor.

Description: This course reviews the theory and methodology of incomplete data, covering ignorability and the coarse-data model, including MAR, MCAR and their generalizations; computational methods such as the EM algorithm and its extensions; methods for handling missing data in commonly used models such as the generalized linear model and the normal mixed model; methods based on imputation; diagnostics for sensitivity to nonignorability; and nonignorable modeling and current topics.

 


BSTA 783: Multivariate and Functional Data Analysis

• Fall term
• 1.0 credit unit
• Instructor (s): TBA
• Prerequisites: BSTA 621, BSTA 651, BSTA 656; permission of instructor.

Description: This course covers both the classical theory and recent methods for multivariate exploratory analysis, as well as techniques for handling functional data. The first part reviews classical multivariate exploratory methods such as principal component analysis, factor analysis, cluster analysis and discriminant analysis, as well more recent methods, such as structural equations models, neural networks and classification trees. The second part covers the more advanced topic of functional data analysis, including graphical representations, principal component analysis and linear models for functional data.

 


BSTA 784: Analysis of Biokinetic Data

• Fall term (Note: Course no longer offered)
• 0.5 credit unit
• Instructor (s): TBA
• Prerequisites: Introductory statistics including regression and hypothesis testing; EPID 520, BSTA 630 or equivalent; permission of instructor.

Description: The time-course of a drug monitored via circulation samples gives us a comprehensive account of the number and sizes of body pools within which the drug distributes before its eventual elimination. Furthermore, the pattern of change of the time-course with increasing drug doses will expose the nature of the mechanisms facilitating that transport and metabolism. How these features are elucidated falls under the general topic of Compartmental Analysis, and the tools and technique of kinetics as well as those of drug dynamics form a part of this topic investigating 'the analysis of biokinetic data'. Additionally we will be exploring how metabolic challenges, such as the glucose challenge, the TRH challenge, and the epinephrine challenge expose aspects of the functionality of their targeted tissues, and, most specifically, we will show how indices relating to insulin resistance are derived.

 


BSTA 785: Statistical Methods for Genomic Data Analysis

• Spring term
• 1.0 credit unit
• Instructor (s): TBA
• Prerequisites: BSTA 620, BSTA 621, these courses can be taken concurrently with this course; permission of the instructor.

Description: This course covers statistical, probabilistic and computational methods for analyzing high-throughput genomic data. With the advent of inexpensive DNA sequencing, statistical genetics is undergoing the transition to big data. The following materials will be selectively covered. Basics of Molecular Biology and Population Genetics; Large-scale inference, empirical Bayes methods, False discovery rate theory and applications to differential expression analysis, RNA-seq data analysis; Network-based analysis of genomic data and Hidden Markov random field models; Sparse segment identification in high dimensional settings with applications to copy number variation analysis using SNP chip data and next generation sequencing data; High dimensional regression and regularization methods in genomics; Genetic networks and Gaussian graphical models, Conditional Gaussian graphical models, Causal inference and directed graphs; Analysis of microbiome data and high dimensional compositional data; Kernel methods and analysis of rare variants; Other miscellaneous topics in analysis of next generation sequencing data (e.g. ChIP-seq data, epigenomics data); Bioconductor/R programs for genomic data analysis.

 


BSTA 786: Advanced Topics in Clinical Trials

• Spring term
• 0.5 credit unit
• Instructor (s): TBA
• Prerequisites: BSTA 661; permission of instructor.

Description: This course will cover in some depth selected topics of interest in clinical trials that are discussed only minimally in the introductory clinical trials courses. Topics may include methods of treatment allocation and blinding, sequential and/or adaptive trial designs, methods of handling missing data, design of active control/noninferiority trials, constructed endpoints, and other topics based on interest of registrants.

 


BSTA 787: Methods for Statistical Genetics and Genomics in Complex Human Disease

• Spring term
• 1.0 credit unit
• Instructor (s): Mingyao Li, PhD and Rui Xiao, PhD
• Prerequisites: Introductory graduate-level courses in statistics (such as BSTA 630-632 or EPID 520-521) are required; or permission of the instructor.

Description: This is an advanced elective course for graduate students in Biostatistics, Statistics, Epidemiology, Bioinformatics, Computational Biology, and other BGS disciplines. This course will cover statistical methods for the analysis of genetics and genomics data. Topics covered will include genetic linkage and association analysis, analysis of next-generation sequencing data, including those generated from DNA sequencing and RNA sequencing experiments. Students will be exposed to the latest statistical methodology and computer tools on genetic and genomic data analysis. They will also read and evaluate current statistical genetics and genomics literature.
 


BSTA 788: Functional Data Analysis

• Spring term
• 1.0 credit unit
• Instructor (s): Wensheng Guo, PhD
• Prerequisites: BSTA 621 and BSTA 651; permission from the instructor.

Description: This course will cover both the basic techniques in functional data analysis and the latest methodological developments in the area. The first half of the course will cover graphical representations, smoothing techniques, curve registration, functional linear models, functional principal component and discriminant analysis. The first half will follow the book by Ramsay and Silverman (2005). The first half aims to prepare the students to analyze functional data. The second half will cover several special topics of the recent development. We will cover around twenty papers in the second half. Each student is expected to complete a term project at the end. The ideal term project can potentially lead to a dissertation topic.

 


BSTA 789: Big Data

• Fall term
• 1.0 credit unit
• Instructor: Hongzhe Li, PhD
• Prerequisites: BSTA 621 and BSTA 622.  BSTA 622 can be taken concurrently.

Description: Selected topics from public health and biomedical research where "Big data" are being collected and methods are being developed and applied, together with some core statistical methods in high dimensional data analysis. Topics include dimension reduction, detection of novel association in large datasets, regularization and high dimensional regression, ensemble learning and prediction, kernel methods, deep learning and network analysis. R programs will be used throughout the course, other standalone programs will also be used.
 


BSTA 790: Causal Inference in Biomedical Research

• Fall term
• 1.0 credit unit
• Instructor: TBA
• Prerequisites: BSTA 621, BSTA 622; permission of instructor.

Description: This course considers approaches to defining and estimating causal effects in various settings. The potential-outcomes approach provides the framework for the concepts of causality developed here, although we will briefly consider alternatives. Topics considered include: the definition of effects of scalar or point treatments; nonparametric bounds on effects; identifying assumptions and estimation in simple randomized trials and observational studies; alternative methods of inference and controlling confounding; propensity scores; sensitivity analysis for unmeasured confounding; graphical models; instrumental variables estimation; joint effects of multiple treatments; direct and indirect effects; intermediate variables and effect modification; randomized trials with simple noncompliance; principal stratification; effects of time-varying treatments; time-varying confounding in observational studies and randomized trials; nonparametric inference for joint effects of treatments; marginal structural models; and structural nested models.

 


BSTA 798: Advanced Topics in Biostatistics I

• Spring term
• 0.5 credit unit
• Instructor (s): TBA
• Prerequisites: TBA; permission of instructor.

Description: This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics in biostatistical methodology will vary from year to year. Methodology related to clinical trials, missing data, functional data analysis, generalized linear models, statistical genetics, advances in Bayesian methodology are examples of areas that may be covered.

 


BSTA 799: Advanced Topics in Biostatistics II

• Fall/Spring term
• 0.5 credit unit
• Instructor (s): TBA
• Prerequisites: TBA; permission of instructor.

Description: This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics in biostatistical methodology will vary from year to year. Methodology related to clinical trials, missing data, functional data analysis, generalized linear models, statistical genetics, advances in Bayesian methodology are examples of areas that may be covered.
 


BSTA 812: Seminar in Probability Theory
(STAT 955)

 


BSTA 820: Statistical Inference III
(STAT 552)

 


BSTA 852: Forecasting and Time Series
(STAT 910)

 


BSTA 870: Seminar in Advanced Applications of Statistics (STAT 991)

 


BSTA 920: Guided Tutorial: Research (0.5 - 3.0 course units)

 


BSTA 995: Dissertation Research (0.5 – 3.0 course units)

 


BSTA 999: Independent Study (0.5 - 1.0 course unit)