# Course Descriptions 2023-2024

BIOS 6301. Introduction to Statistical Computing

This course is designed for students who seek to develop skills in statistical computing. Students learn how to use R software for data management, data tabulation and summarization, data presentation, and report generation. Topics include organization and documentation of data, input and export of data sets, methods of cleaning data, tabulation and graphing of data, programming capabilities, and an introduction to data simulations and bootstrapping. Students are also introduced to markdown, knitr, and Quarto for report writing. Prerequisites: None. Fall [2]. Beck.

BIOS 6306. Introduction to Study Design

This course introduces principles of study design in biomedical and health sciences. The designs considered include case series, ecologic studies, matched and unmatched case-control studies, observational cohort studies, historically controlled clinical trials, screening trials, and randomized clinical trials. The goal is to introduce critical design challenges that ultimately impact the ability to generate statistical inferences from observed samples to the target populations. Concepts such as internal and external validity, bias identification and control, confounding, and effect modification are discussed and illustrated with examples from the medical literature. The dependence of traditional univariate measures of statistical association (absolute risk, relative risk, odds ratios) on critical design elements is highlighted. Statistical evaluation of diagnostic tests is also introduced along with a brief introduction to causal inference. Prerequisites: None. Fall [3]. Dupont.

BIOS 6311. Principles of Modern Biostatistics

This course is the first in a two-course series on modern biostatistical reasoning and evaluation of data analysis methods. Students learn the statistical principles that govern the analysis of data in the health sciences and biomedical research. Traditional probabilistic concepts and modern computational techniques are integrated with applied examples from biomedical and health sciences. Topics include axioms of probability, probability distributions and their moments, properties of estimators, law of large numbers, central limit theorem, theory of confidence intervals and hypothesis testing (for one-sample and two-sample problems), paradigms of statistical inference (frequentist, Bayesian, likelihood), introduction to non-parametric techniques, bootstrapping and simulation, sample size projections, and basic study design issues. Prerequisites: Familiarity with calculus and probability concepts such as double integrals, partial derivatives, expectations, variances, cumulative density functions, and quantile functions is expected. Fall [3]. Vandekar.

BIOS 6311L
This is a laboratory and discussion session for BIOS 6311 (Principles of Modern Biostatistics). Students review relevant theory and work on applications as a group. Computing solutions and extensions using R software are emphasized. Students are required to take BIOS 6311 [3] concurrently. Fall [1]. Vandekar.

BIOS 6312. Modern Regression Analysis

This course is the second in a two-course series on modern biostatistical reasoning and evaluation of data analysis methods. Students learn modern regression analysis and model-building techniques from an applied perspective. Theoretical principles are demonstrated with applied examples from biomedical and health sciences. Topics include regression modeling for continuous outcomes, including simple linear regression, multiple linear regression, and analysis of variance with one-way, two-way, three-way, and analysis of covariance models. There is a brief introduction to regression models for binary outcomes (logistic regression), ordinal outcomes (proportional odds models), count outcomes (Poisson and negative binomial models), and time-to-event outcomes (Kaplan-Meier curves, Cox proportional hazards models). Incorporated into the presentation of these models are topics such as regression diagnostics, non-parametric regression, splines, data-reduction techniques, model validation, parametric bootstrapping, and a brief introduction to methods for handling missing data. Prerequisites: BIOS 6311. Spring [3]. Slaughter.

BIOS 6312L

This is a laboratory and discussion session for BIOS 6312 (Modern Regression Analysis). Students review relevant theory and work on applications as a group. Computing solutions and extensions using R software are emphasized. Students are required to take BIOS 6312 [3] concurrently. Spring [1]. Slaughter.

BIOS 6321. Clinical Trials and Experimental Design

This course is an introduction to statistical aspects of the design, monitoring, and analysis of experiments. Emphasis is on studies of human subjects (i.e., clinical trials). Topics include study designs, randomization and balance, selection of estimands and endpoints, sample size projections, data collection and quality control, data monitoring and interim analysis, principles of and issues in the analysis of trial data, and interpretation and reporting of results. Prerequisites: BIOS 6301 or equivalent, BIOS 6311 or equivalent. Spring [3]. Samuels.

BIOS 6341. Fundamentals of Probability

This course is the first in a two-course series on probability and statistical inference that introduces and explores the probabilistic framework underling statistical theory. Students learn probability theory—the formal language of uncertainty—and its application to everyday statistical concepts and analysis methods. Topics include probability axioms, probability and sample space, events and random variables, transformation of random variables, probability inequalities, independence, discrete and continuous distributions, expectations and variances, conditional expectation, moment generating functions, random vectors, convergence concepts (in probability, in distribution, and almost surely), weak and strong law of large numbers, central limit theorem, delta method, order statistics, and exponential family. Prerequisites: Familiarity with infinite series, double integrals, integration by parts, and partial derivatives is expected. Fall [3]. Hackstadt.

BIOS 6341L

This is a laboratory and discussion session for BIOS 6341 (Fundamentals of Probability). Students review relevant theory and work on applications as a group. Collaborative work and problem solving as a group are emphasized. Students validate analytical solutions and explore limit theorems using R software. Students are required to take BIOS 6341 [3] concurrently. Fall [1]. Hackstadt.

BIOS 6342. Contemporary Statistical Inference

This course is the second in a two-course series on probability and statistical inference that introduces and explores the fundamental inferential framework for parameter estimation, testing hypotheses, and interval estimation. Students learn classical methods of inference (hypothesis testing), and paradigms of statistical inference (frequentist, Bayesian, likelihood) and their surrounding controversies. Topics include sufficiency, minimal sufficiency, exponential family, ancillarity, completeness, conditionality principle, Fisher’s information, Cramer-Rao inequality, hypothesis testing (likelihood ratio test, most powerful test, optimality, Neyman-Pearson lemma, inversion of test statistics), likelihood principle, law of likelihood, Bayesian posterior estimation, interval estimation (confidence intervals, support intervals, credible intervals), basic asymptotic and large sample theory, and maximum likelihood estimation. Prerequisites: BIOS 6341. Spring [3]. Johnson.

BIOS 6342L

This is a laboratory and discussion session for BIOS 6342 (Contemporary Statistical Inference). Students review relevant theory and work on applications as a group. Computing solutions and extensions using R software are emphasized. Students are required to take BIOS 6342 [3] concurrently. Spring [1]. Johnson.

BIOS 7323. Applied Survival Analysis

This course provides an applied introduction to methods for time-to-event data with censoring mechanisms. Topics include life tables, non-parametric approaches (Kaplan-Meier curves, log-rank test), semi-parametric approaches (Cox proportional hazards model), parametric approaches (Weibull, gamma), competing risks, and time-dependent covariates. Focus is on fitting the models and the relevance of those models for the biomedical application. Prerequisites: BIOS 6312 or equivalent. Fall [3]. Tao.

BIOS 7323L

This is a laboratory and discussion session for BIOS 7323 (Applied Survival Analysis). Students review relevant theory and work on applications as a group. Computing solutions and extensions using R software are emphasized. Students are required to take BIOS 7323 [3] concurrently. Fall [1]. Tao.

BIOS 7330. Regression Modeling Strategies

This course presents strategies for, and a survey of current thinking on, building multivariable regression models primarily for the purpose of prediction, but also for estimation and inference. Topics include using regression splines to relax linearity assumptions, the perils of variable selection and over-fitting, where to spend degrees of freedom, shrinkage, imputation of missing data, data reduction, and interaction surfaces. There is also an emphasis placed on describing approaches for graphically understanding models and using resampling to estimate a model’s likely performance on new data. Statistical methods related to binary logistic models, ordinal logistic models, and survival models are covered. Students will develop, validate, and graphically describe multivariable regression models. Prerequisites: BIOS 6312 or equivalent. Spring [3]. Schildcrout.

BIOS 7345. Advanced Regression Analysis I (Linear and General Linear Models)

This course is the first in a two-course series on advanced regression analysis that covers the underlying mechanics of linear and generalized linear models for independent data. Following a brief review of salient topics in linear algebra and multivariate normal theory, the first half of the course covers ordinary least squares in the cases of full rank and non-full rank design matrices, weighted least squares, optimality (the Gauss-Markov theorem), analysis of variance (ANOVA), hypothesis testing, confidence and prediction regions, model misspecification, diagnostics, and regularization. The second half of the course covers generalized linear models (e.g., binomial, Poisson, and gamma regression) from the likelihood and Z-estimation frameworks, overdispersion and quasi-likelihood, hypothesis testing, diagnostics, and negative binomial regression. As time permits, advanced topics will be covered such as matched pair designs, regression of nominal and ordinal outcomes, cumulative probability models, and receiver operating characteristic regression. Prerequisites: BIOS 6312 or equivalent, BIOS 6342 or equivalent. Fall [3]. Spieker.

BIOS 7345L

This is a laboratory and discussion session for BIOS 7345 (Advanced Regression Analysis I). Students review relevant theory and work on applications as a group. Computing solutions and extensions using R software are emphasized. Students are required to take BIOS 7345 [3] concurrently. Fall [1]. Spieker.

BIOS 7346. Advanced Regression Analysis II (General Linear Models and Longitudinal Data Analysis)

This course is the second in a two-course series on advanced regression analysis that extends linear and generalized linear models to the analysis of dependent (specifically, longitudinal) data. Parametric (generalized least squares, likelihood-based mixed-effects models) and semi-parametric (generalized estimating equations) regression-based methods are central to the course. Advanced topics include transition models, marginalized models, missing data, and joint models for longitudinal and survival data. Emphasis is placed on the theoretical framework for longitudinal data analysis, the statistical properties of longitudinal data analysis methods, and their development and application to modern biomedical research. Prerequisites: BIOS 7345. Spring [3]. French.

BIOS 7346L

This is a laboratory and discussion session for BIOS 7346 (Advanced Regression Analysis II), focusing on the illustrative application of longitudinal data analysis methods to biomedical research data using R software. Students are required to take BIOS 7346 [3] concurrently. Spring [1]. French.

BIOS 7351. Statistical Collaboration in Health Sciences I

This course is the first in a two-course series on collaboration in statistical science. Students are exposed to a variety of challenges that arise in collaborative arrangements. The course’s goal is to develop the knowledge, skills, attitude, and behavior necessary to interact with research and statistical collaborators in routine health science applications. The importance of understanding and learning the science underlying collaborations is emphasized. Students experience various biostatistics roles while collaborating with real investigators and projects. Students gain experience that builds interpersonal, communication, professionalism, leadership, teaching, and presentation and interviewing skills. Real-world issues and challenges are discussed. There will be special guest lectures on grants, career choices, and collaboration satisfaction. Course content also makes use of departmental clinics that are run concurrently. Prerequisites: None. Fall [3]. Davidson.

BIOS 7352. Statistical Collaboration in Health Sciences II

This course is the second in a two-course series on collaboration in statistical science. Students are exposed to a variety of challenges that arise in collaborative arrangements. The course’s goal is to sharpen students’ collaborative skills while exposing them to the application of advanced statistical techniques in routine health science applications. The importance of understanding and learning the science underlying collaborations are emphasized. Students are exposed to real collaborative projects, and face real-life challenges such as opaque scientific direction, poor scientific formulation, lack of time, and ill-formulated messy data. Students engage in several projects that involve the use of a wide range of biostatistics methods from design to analysis. Course content may also make use of departmental clinics. Prerequisites: BIOS 7351. Spring [3]. Raman.

BIOS 7361. Advanced Concepts in Probability and Real Analysis for Biostatisticians

This course is the first in a two-course series on advanced probability and statistical inference. Topics include characteristic functions, modes of converge, uniform integrability, Brownian motion, classical limit theorems, Lp spaces, projections, sigma-algebras and RVs, martingales, random walks, Markov chains, and probabilistic asymptotics. Emphasis on measure theory is minimal. Concepts are illustrated in biomedical applications whenever possible. Prerequisites: BIOS 6342 or equivalent. Fall [3]. Johnson.

BIOS 7362. Advanced Statistical Learning and Inference

This course is the second in a two-course series on advanced probability and statistical inference. Through a combination of lectures, readings, hands-on exercises, and student-led discussions, the course offers an intricate blend of both theoretical and applied insights into probabilistic machine learning algorithms, excluding deep learning. Initial sessions provide a concise yet comprehensive review of linear models, which is then followed by an in-depth exploration of non-parametric methods such as KNN, kernel density estimation, and kernel methods. The latter part of the course introduces advanced topics such as learning with fewer labeled examples, including transfer and active learning, and concludes with a focus on dimensionality reduction, clustering, and recommender systems. Student participation in discussions is a significant part of the learning process. The course employs a variety of biomedical case studies to offer a nuanced understanding of how statistical learning algorithms are deployed in real-world scenarios. Prerequisites: BIOS 7361. Spring [3]. Asiaee.

BIOS 7999. Master’s Thesis Research

BIOS 8004. Ethical Principles and Practices for Biostatisticians
This reading and discussion-based course is based on the American Statistical Association’s Ethical Guidelines for Statistical Practice. Topics include professional integrity and accountability, integrity of data and methods, responsibilities to science/public/funder/client, responsibilities to research subjects, responsibilities to research team colleagues, responsibilities to other statisticians or statistics practitioners, responsibilities regarding allegations of misconduct, and responsibilities of employers, including organizations, individuals, attorneys, or other clients employing statistical practitioners. Prerequisites: None. Fall [0]. Greevy.

This course introduces advanced computing and analytical skills and concepts in biomedical data sciences. The course covers real-world data platforms, diverse analytics environments, statistical data analysis in Python, numerical methods, bioinformatics computing, cloud computing, and data visualization. The objective of the course is to enable students to adapt to the computational challenges required by today’s biomedical data sciences and big data computing, and to equip them with the knowledge and skills to solve real-world problems. Students are expected to analyze big complex data in cloud-based computing and data analysis environments. The course involves substantial programming in R, Python, SQL, and Shell scripts. Prerequisites: BIOS 6301 or equivalent, BIOS 6342 or equivalent. Offered biennially [3]. Xu.

BIOS 8372. Bayesian Methods

This course covers the methodology and rationale for Bayesian methods and their applications. Topics include the historical development of Bayesian methods such as hierarchical models, Markov chain Monte Carlo (MCMC) and related sampling methods, specification of priors, sensitivity analysis, and model checking and comparison. This course features applications of Bayesian methods to biomedical research. Prerequisites: BIOS 6301 or equivalent, BIOS 7345 or equivalent. Offered biennially [3]. Choi.

BIOS 8375. Causal Inference

This advanced course introduces causal inference methods for observational data and randomized studies. Topics include the Rubin causal model, directed acyclic graphs, propensity scores, inverse probability weighting, instrumental variables, causal mediation analysis, marginal structural models, g-computation, and sensitivity analyses to examine robustness to untestable assumptions. Students learn the basic theory behind the methods and their application to biomedical data examples. Prerequisites: BIOS 7323 or equivalent, BIOS 7346 or equivalent. Offered biennially [3]. Shepherd.

BIOS 8999. Non-Candidate Research

BIOS 9999. PhD Dissertation Research

IGP 8004. Responsible Conduct in Research