 |
BIST 0505
Introduction to SAS for Data Analysis in Public Health (3)
Students will learn how to create basic Excel tables and graphs and create a simple database and enter data, import databases into SAS, and conduct data analysis using SAS.
Prerequisites: PHCO 0502 and PHCO 0504. Students are also expected to know the basics of MS Windows (e.g., use of the mouse; selecting, copying, cutting, and pasting files and text; working with more than one application window at a time, etc.) and MS Office (e.g. basic formatting in Word, Excel, and PowerPoint; entering data and performing simple calculations in Excel, etc.).
|
BIST 0515
Introduction to SAS and Research for Data Analysis in Public Health (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.
Prerequisite: PHCO 0504.
|
BIST 0535
Biometrics 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.
Prerequisite: PHCO 0504.
|
BIST 0551
Applied Regression Analysis for Public Health Studies (3)
Introduces students to regression methods. The primary topics are simple and multiple linear regression models, including analysis of variance (ANOVA) and covariance (ANCOVA). Logistic regression for binary outcome will also be
introduced. The emphasis will be interpretation and applications. Students will learn how to use SAS for implementing regression methods.
Prerequisites: PHCO 0504 and BIST 0535 or BIST 0625.
|
BIST 0610
Advanced Regression Methods for Public Health Studies (3)
Intermediate-to-advanced level course on
regression methods that emphasizes the theoretical concepts and applications of regression models for public health studies. It is taught at BIST M.P.H./M.S. level. Covers simple and multiple linear regression models, including analysis of variance (ANOVA) and covariance (ANCOVA); binary regression including logistic regression; and application to case-control studies. Model building, model diagnostics, building hypothesis testing, and interpretation, as well as theoretical properties of parameter estimation and inference will be taught. The theory portion will use matrix and linear algebra.
Prerequisites: BIST 0535 or BIST 0625 and BIST 0613.
|
BIST 0613
Biostatistics Theory I (3)
Statistics is a branch of science dealing with the collection, analysis, interpretation, and presentation of numerical data. This course is an introduction to probability modeling as a basis for statistical inference. The underlying strategy in prediction and inference involves the determination of a probability modeling. This can be approached from two perspectives: analytical (mathematical) and computational (algorithmic). It is important for students to master both perspectives. Advanced calculus is sufficient background for the analytical. This course serves to lay a foundation in statistical theory for M.S., M.P.H, Dr.P.H., and Ph.D. students so that they can pursue more advanced technical materials.
Prerequisites: One year of calculus, some linear algebra, and multivariable calculus, PHCO 0504, and BIST 0535 or BIST 0625.
|
BIST 0614
Biostatistics Theory II (3)
Theory of point and interval estimation and hypothesis testing. Topics include sampling distributions, sufficiency, unbiasedness, Bayes methods, and the asymptotics related to the likelihood based inference. Emphasis is on fundamental concepts underlying the theory.
Prerequisite: BIST 0613 or equivalent (determined by instructors).
|
BIST 0615
Categorical Data Analysis (3)
Public health studies, especially those utilizing surveys, contain large amounts of categorical data. This class provides an introduction to descriptive and inferential statistics for categorical data with applications to observational studies and randomized clinical trials. For two- and three-way contingency tables, measures of association and tests for homogeneity between populations and independence of variables are presented. Related tests of trend for ordinal data are studied. Generalized linear models, including loglinear, Poisson, and logistic regression analyses are presented for nominal, ordinal, and count responses.
Prerequisites: PHCO 0504, BIST 0535 or BIST 0625, and BIST 0610.
|
BIST 0616
Lifetime Data Analysis (3)
Introductory-to-intermediate course on statistical analysis that emphasizes the concepts and applications of survival analysis for life science. Topics include Kaplan-Meier estimates, lifetable analysis, log-rank test statistics, and the generalized Wilcoxon tests. Parametric inference includes the exponential and Weibull distributions. The proportional hazards model and extensions to time-dependent covariates will be discussed. Advanced topics such as analysis of competing risks, recurrent events, and clustered failure times will be introduced. Clinical and epidemiological examples will be given to illustrate the various statistical procedures. Analysis of real data sets using statistical software SAS and R will be integrated throughout.
Prerequisites: PHCO 0504, BIST 0535 or BIST 0625, BIST 0610, or permission from instructor. Computing languages: SAS, SPSS, etc.
|
BIST 0625
Fundamentals of Biostatistics (3)
An
introduction to biostatistical methods, including both experimental
design and data analysis, commonly encountered in the biomedical and
health sciences. Students are also expected to complete computer-based
exercises that illustrate these methods .
Prerequisite: Permission of instructor. This course is offered on the Piscataway/New Brunswick campus.
|
BIST 0630
Sampling Methods (3)
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.
|
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,
multivariate ANOVA for repeated measurements, linear mixed effects models and multilevel modeling,
maximum likelihood and restricted maximum likelihood estimation, modeling the variance-covariance
structures, inference for random effects, logistic and Poisson mixed effects model for binary and count data,
marginal models and generalized estimating equations, and model diagnostics. Analysis of other correlated data will also be considered. Analysis of real and substantial data sets using statistical software SAS and R will be integrated throughout. The course is suitable for doctoral and master's students in biostatistics and doctoral students in other fields such as epidemiology, clinical trials, and social science who need to
analyze longitudinal data.
Prerequisites: Excellent command of the materials in BIST 0610 and some familiarity with matrix notations. Students are encouraged to discuss their readiness for this course with the instructor.
|
BIST 0655
Biocomputing II (3)
This extension of BIST 0654 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.
|
BIST 0660
Clinical Trials: Design and Analysis of Medical Experiments (3)
An introduction to clinical trials. Topics include an overview of clinical trials, concepts of statistical efficiency and bias, design issues about randomization and blinding, sample size and power, baseline comparability and analysis of covariance, sequential data monitoring and interim analyses, and the missing data problem in clinical trials.
Prerequisites: BIST 0610, BIST 0535 or BIST 0625, or equivalent.
|
BIST 0688
Statistical Methods in Genetics (3)
This is a course to acquaint 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.
|
BIST 0690
Advanced Topics in Biostatistics (3)
An advanced-level course to provide 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.
|
BIST 0700
Advanced Theory of Biostatistics I (3)
Extends probability and statistical theory covered in BIST 0613 and 0614 to an advanced level. Topics include common stochastic processes in biostatistics, likelihood construction, maximum likelihood estimation (MLE) and the information matrix, likelihood-based tests (Wald, Score, and LRT) and confidence intervals, partial likelihood in survival analysis, conditional and marginal likelihoods, Newton-Raphson algorithm, EM algorithm and extensions, convergence and limit theorems, and asymptotic theory for likelihood-based methods. This course also includes the theory of statistical inferences for linear models, generalized linear models (GLM), and generalized linear mixed effect models (GLMM); presents theory of statistical estimation based on loss and risk functions; and discusses 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.
|
BIST 0701
Advanced Theory of Biostatistics II (3)
Extends probability and statistical theory covered in BIST 0613, 0614 to an advanced level, and continues the advanced inference topics arising in biostatistics presented in BIST 0700 (Advanced Theory of Biostatistics I). Topics include M-estimation (estimating equations), generalized estimating equation (GEE), restricted maximum likelihood (REML) methods, and computation-based statistical 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.
|
BIST 9619
Design of Experiments (3)
Teaches the 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.
|
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, 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.
|
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.
Cross-listed with 16:960:555. This course is offered through Rutgers School of Graduate Studies in New Brunswick.
|
BIST 9665
Applied Time-Series Analysis (3)
Model-based forecasting methods, autoregressive and moving average models, ARIMA, ARMAX, ARCH,
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, 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.
|
BIST 9667
Applied Multivariate Analysis (3)
Methods dimension reductiony, including principal components, factor analysis, and multidimensional scaling; correlation techniques, including partial, multiple, and canonical correlation; classification and clustering methods. 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.
|
BIST 9686 and BIST 9687
Interpretation of Data I, II (3)
Use of various computer-based techniques, including graphical, to understand and interpret data sets.
Exposure to, and intuitive understanding of, some basic techniques for the analysis of multivariate,
categorical, and time-series data as well as other miscellaneous applications of statistical procedures.
Prerequisite: Level IV statistics.
Cross-listed with 16:960:586 and 16:960:587. This course is offered through Rutgers School of Graduate Studies in New Brunswick.
|
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, observational studies.
Prerequisite: 16:960:567 or 568 or permission of instructor.
Cross-listed with 16:960:588. This course is offered through Rutgers School of Graduate Studies in New Brunswick.
|
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.
|
|
Courses 
