BIST 0535
Biostatistical Computing (3)
This course provides students with the ability to conduct statistical analysis using statistical software SAS and R. The primary subject is using the SAS programming language to solve a variety of database and statistical problems, and using R language to perform statistical analysis. This is a highly applied course and students are expected to complete computer exercises each week.
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BIST 0551
Applied Regression Analysis for Public Health Studies (3)
This course introduces regression analyses methods. The primary topics are simple, multiple linear regression models, model diagnostics, and model building. Logistic regression for binary outcomes will also be introduced. The emphasis will be interpretation and applications. Students will learn how to use statistical software for implementing regression analyses.
Prerequisites: PHCO 0504 (Minimum Grade of B) and BIST 0535 (Minimum Grade of B); or BIST 0625 (Minimum Grade of B)
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BIST 0610
Advanced Regression Methods for Public Health Studies (3)
This course of regression methods emphasizes the theory and applications of linear and logistic regression models for public health studies. Model formulations, statistical inference and data interpretation are taught and illustrated with real data examples. Statistical software packages and linear algebra are used.
Prerequisites: BIST 0613 and BIST 0535; or BIST 0613 and BIST 0625
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BIST 0613
Biostatistics Theory I (3)
This course introduces fundamental concepts in probability, including random variables, probability distributions, and statistical models. Students will learn to apply these principles to solve real-world problems. This course serves to lay a foundation in statistical theory for students so that they can pursue more advanced technical materials.
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BIST 0614
Biostatistics Theory II (3)
This course covers theory of estimation and hypothesis testing. Topics include sampling distributions, sufficiency, unbiasedness, maximum likelihood methods (estimation and tests). Emphasis is on the fundamental concepts of underlying theory.
Prerequisite: BIST 0613 (Minimum Grade of B).
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BIST 0615
Applied Categorical Data Analysis (3)
This class introduces descriptive and inferential statistics for categorical and ordinal data with applications to epidemiological and clinical studies. Methods that focus on 2- and 3-way contingency tables as well as generalized linear models to model nominal, ordinal, and count outcomes are presented.
Prerequisites: PHCO 0504 and BIST 0535; or BIST 0625
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BIST 0625
Fundamentals of Biostatistics (3)
This course covers core topics in distributions, estimation, and modeling. Topics include descriptive statistics, hypothesis testing, power calculations, and linear regression. Students will apply statistical software programs to solve common public health problems and will critically review and comprehend basic statistical discussions in the public health literature.
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BIST 0627
Applied Survival Data Analysis (3)
This course emphasizes concepts and applications of survival analysis used in public health studies. The product limit estimator, the Cox proportional hazard model, and parametric models will be discussed. Censoring and truncation patterns will also be studied. Model building and checking will be discussed throughout.
Prerequisites: BIST 0551 (Minimum Grade of B) or BIST 0610 (Minimum Grade of B).
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BIST 0630
Sampling Methods (3)
This course emphasizes data collection utilizing stratification, cluster sampling, multistage sampling, systematic sampling, or sampling with unequal probabilities of selection may be preferred to simple random sampling. Sampling strategies often introduce bias into traditional estimates of standard errors for these statistics. This course provides an introduction to theory and applications of methods for analysis that account for sampling design.
Prerequisites: PHCO 0504 and an intermediate statistics course.
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BIST 0650
Applied Longitudinal Data Analysis (3)
This course covers modern statistical techniques for longitudinal data from an applied perspective. Topics include characteristics of the longitudinal design, graphical exploration of the mean and correlation structure, linear and generalized linear mixed effects models including multilevel modeling, marginal and subject-specific models, generalized estimating equations method (GEE). Analysis of real data sets using statistical software are integrated throughout.
Prerequisites: BIST 0551 (Minimum Grade of B) or BIST 0610 (Minimum Grade of B).
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BIST 0655
Biocomputing II (3)
This course provides students with more advanced computing-intensive methods for their future research needs. Topics include random variable generation, jackknife and bootstrap, Monte Carlo integration, Markov chains, Monte Carlo optimization, Metropolis-Hastings algorithm, and the Gibbs Sampler. SAS, R/Splus, BUGS (Bayesian inference using Gibbs sampling), together with Fortran, C/C++ will be used as programming tools..
Prerequisites: BIST 0535 and intermediate-level graduate courses in statistical inference and probability theory (e.g., BIST 0613 and BIST 0614). One year of calculus and some linear algebra. Permission from instructor.
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BIST 0660
Clinical Trials: Design and Analysis of Medical Experiments (3)
This course teaches students essential concepts and application of a wide range of statistical methods used in the design, conduct, monitoring, and analysis of medical experiments. Students are expected to participate in group discussions during the class.
Prerequisites: BIST 0551 and BIST 0535; or BIST 0610 and BIST 0625.
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BIST 0663
Statistical Learning in Biomedical Studies (3)
This course focuses on the theoretical concepts and applications of statistical learning methods for biomedical studies. It covers supervised and unsupervised learning methods, including Ridge regression, LASSO, classification, decision trees, ensemble learning, SVM, deep learning, PCA, and clustering models. The course emphasizes model building, assessment, selection, interpretation, and explores the theoretical properties of selected models. R is used for data analysis. Suitable for BIST MS and doctoral students.
Prerequisites: BIST 0610 and BIST 0614.
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BIST 0688
Statistical Methods in Genetics (3)
This course acquaints students in public health with statistical methods for the analysis of genetic data arising in epidemiology, clinical trials, and laboratory experiments. Topics covered include genetic epidemiology, pedigree data, linkage and association analysis, phylogenetics, and the analysis of micro array data.
Prerequisites: BIST 0535 or BIST 0625 and BIST 0610; or permission from instructor.
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BIST 0690
Advanced Topics in Biostatistics (3)
This course provides students who are in research for doctoral thesis topics with in-depth survey and synthesis of recent developments in biostatistics. Topics will be based on statistical research and applications of faculty of the department, which may include missing data analysis, sequential design and analysis, Bayesian statistics, bioinformatics analysis, mixed models, etc.
Prerequisite: BIST 0700 or permission from instructor.
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BIST 0700
Advanced Theory of Biostatistics I (3)
This course extends probability and statistical theory to an advanced level. The topics include common stochastic processes in biostatistics, likelihood construction, maximum likelihood estimation and information matrix, likelihood-based tests, partial, conditional and marginal likelihoods, alogirthms, convergence and limit theorems, and asymptotic theory for likelihood-based methods. This course also includes the inference for regression models and the theory of statistical estimation, unbiasedness, consistency, and efficiency.
Prerequisites: An intermediate-level graduate course in statistical inference and probability theory (e.g., BIST 0613 and BIST 0614). One year of calculus and some linear algebra.
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BIST 0701
Advanced Theory of Biostatistics II (3)
This course extends probability and statistical theory covered in BIST 0613 and 0614 to an advanced level, and continues the advanced inference topics arising in Biostatistics presented in Advance Theory of Biostatistics I (BIST 700). Topics include estimating equations, generalized estimating equation (GEE), restricted maximum likelihood (REML) methods, jackknife and bootstrap methods, permutation and rank tests, and Bayesian data analysis.
Prerequisites: An intermediate-level graduate course in statistical inference and probability theory (e.g., BIST 0613 and BIST 0614) or Advanced Theory of Biostatistics I (BIST 0700), or equivalent. One year of calculus and some linear algebra.
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BIST 0714
Intermediate Biostatistics
This course is designed for students to take a deeper dive into modern topics in biostatistics. Topics include methods for randomized trials, observational studies, and regression methods. Within each research topic, a variety of methods will be learned, with an emphasis on assumptions, interpretation, and strengths/weaknesses of each method.
Doctoral student standing.
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BIST 0720
Advanced Biostatistical Computing (3)
This course covers advanced statistical computing techniques widely used in Statistics/Biostatistics Research. Topics in this course include dimensionality reduction, random variable generation, Monte Carlo Integration, Markov chains, Monte Carlo Optimization, EM algorithm, Metropolis-Hastings algorithm, the Gibbs sampler. R and/or C++ is used as programming tools in the class.
Prerequisites: BIST 0613, concurrent BIST 0614, and BIST 0610.
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BIST 0721
Advanced Bayesian Inference (3)
This course focuses on modern Bayesian methods for analyzing biomedical and health-related data. The course begins with a brief review of Bayesian principles, conjugate and nonconjugate priors, and computational algorithms (Gibbs; Metropolis Hastings; slice sampling) and emphasizes Bayesian nonparametric methods. This includes popular priors over distributions and priors over functions.
Doctoral student standing.
Prerequisites: BIST 0610 and BIST 0700.
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BIST 0725
Generalized Linear Models (3)
This course covers the theory and inferential methods for generalized linear models. Specific topics include linear, logistic, and Poisson regression; overdispersion; likelihood-based inference and estimating equations; algorithms; and applications.
Prerequisites: BIST 0613, BIST 0614, and BIST 0610.
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BIST 0727
Survival Analysis (3)
This is a course in survival analysis that emphasizes concepts and applications used in public health studies. The product limit estimator, the Cox proportional hazard model, and parametric models will be discussed. Censoring and truncation patterns will also be studied. Model building and checking will be discussed throughout. This course is a mathematically more advanced version of BIST 0627, with additional work required of students.
Prerequisites: BIST 0610 (Minimum Grade of B) and BIST 0614 (Minimum Grade of B).
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BIST 0750
Longitudinal Data Analysis (3)
This course covers modern statistical techniques for longitudinal data analysis from a theoretical perspective. Emphasis will be on the theory of longitudinal data analysis and modeling. Topics include general and generalized linear models for longitudinal data, with the focus on the marginal, random effects and transition models, as well as missing data analysis.
Prerequisites: BIST 0610 (Minimum Grade of B) and BIST 0614 (Minimum Grade of B).
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BIST 9619
Design of Experiments (3)
Fundamental principles of experimental design; completely randomized variance component designs; randomized blocks; Latin squares; incomplete blocks; partially hierarchic mixed-model experiments; factorial experiments; fractional factorials; and response surface exploration.
Prerequisite: 01:960:484 or 401 or equivalent.
Cross-listed with 16:960:590. This course is offered through Rutgers School of Graduate Studies in New Brunswick.
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BIST 9654
Applied Stochastic Processes (3)
Markov chains; recurrence; random walk; gambler's ruin; ergodic theorem and stationary distribution; continuous time Markov chains; queuing problems; renewal processes; martingales; Markov processes; Brownian motion; concepts in stochastic calculus; and Ito's formula.
Prerequisites: Advanced calculus, and 16:960:580 or 582 or 592.
Cross-listed with 16:960:554. This course is offered through Rutgers School of Graduate Studies in New Brunswick.
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BIST 9655
Nonparametric Statistics (3)
Introduction and survey of distribution-free approaches to statistical inference. Fisher's method of randomization; distribution-free test procedures for means, variances, correlations, and trends; rank tests; relative efficiency, asymptotic relative efficiency, and normal-score procedures; binomial, hypergeometric distributions, and combinatorial run theory. Also, tests of goodness-of-fit, including the Kolmogorov-Smirnov and chi-square tests, contingency-table analysis, tolerance sets, and Tchebycheffe-type inequalities. Emphasis on applications.
Prerequsite: BIST 0613 and 16:960:580 or 582 or 592.
Cross-listed with 16:960:555. This course is offered through Rutgers School of Graduate Studies in New Brunswick.
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BIST 9665
Applied Time-Series Analysis (3)
Model-based forecasting methods; autoregressive and moving average models; ARIMA, ARMAX, ARCH, and state-space models; estimation, forecasting, and model validation; missing data; irregularly spaced time series; parametric and nonparametric bootstrap methods for time series; multiresolution analysis of spatial and time-series signals; and time-varying models and wavelets.
Prerequisite: Level V statistics or permission of instructor.
Cross-listed with 16:960:565. This course is offered through Rutgers School of Graduate Studies in New Brunswick.
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BIST 9667
Applied Multivariate Analysis (3)
Methods dimension reduction, including principal components, factor analysis, and multidimensional scaling; correlation techniques, including partial, multiple, and canonical correlation. Clustering and classification. Emphasis on data analytic issues, concepts, and methods (e.g., graphical techniques) and on applications drawn from several areas, including behavioral, management, and physical and engineering sciences.
Prerequisite: Level V statistics or permission of instructor.
Cross-listed with 16:960:567. This course is offered through Rutgers School of Graduate Studies in New Brunswick.
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BIST 9686
Interpretation of Data (3)
Modern methods of data analysis with an emphasis on statistical computing: univariate statistics, data visualization, robust statistics, nonlinear models, logistic regression, generalized linear models (GLM), and smooth regression (including GAM models). Expect to use statistical software packages, such as SAS and R in data analysis.
Prerequisite: Level IV statistics.
Cross-listed with 16:960:586. This course is offered through Rutgers School of Graduate Students in New Brunswick.
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BIST 9687
Interpretation of Data II (3)
Modern methods of data analysis and advanced statistical computing techniques: Monte-Carlo simulation methods; the EM algorithm; MCMC methods; spatial statistics; longitudinal data analysis/mixed effects models/GEE; latent variable models; hidden Markov models; and Bayesian methods, etc. Expect to use the statistical software package R and to do some R programming for data analysis.
Prerequisite: 16:960:586.
Cross-listed with 16:960:587. This course is offered through Rutgers School of Graduate Studies in New Brunswick.
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BIST 9688
Data Mining (3)
Databases and data warehousing; exploratory data analysis and visualization; an overview of data mining algorithms; modeling for data mining; descriptive modeling; predictive modeling; pattern and rule discovery; text mining; Bayesian data mining; and observational studies.
Prerequisite: 16:960:567 and 568.
Cross-listed with 16:960:588. This course is offered through Rutgers School of Graduate Studies in New Brunswick.
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BIST 9691
Advanced Design of Experiments (3)
Strategy of experimentation; screening designs; factorial designs; response surface methodology; evolutionary operation; mixture designs; incomplete blocking designs; computer-aided experimental designs; and design optimality criteria.
Prerequisite: 16:960:590. Recommended: 16:960:563.
Cross-listed with 16:960:591. This course is offered through Rutgers School of Graduate Studies in New Brunswick.
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