| PHST 500 | Introduction to Biostatistics for Health Sciences I | 3 hours |
| Introduction to descriptive and inferential statistical methods, including graphics, estimation, confidence intervals, 1- and 2- sample hypothesis testing, one-way analysis of variance and use of statistical software. |
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| PHST 501 | Introduction to Biostatistics for Health Sciences II | 3 hours |
| Continued introduction to descriptive and inferential statistical methods including ANOVA regression, chi-square analysis of frequencies, survival analysis, and nonparametric methods. |
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| PHST 561 | Math Tools I | 1 hour |
| This course covers mathematical tools required for sound comprehension of mathematical probability and statistics concepts included in methodological portions of coursework in the MS in Biostatistics degree. Course topics include: (1) functions and graphs with particular focus on polynomials and roots, rational functions, and exponential and logarithmic functions, and (2) limits and continuity of functions. |
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| PHST 562 | Math Tools II | 1 hour |
| This course covers mathematical tools required for sound comprehension of mathematical probability and statistics concepts included in methodological portions of coursework in the MS in Biostatistics degree. Course topics include: (1) first and higher order differentiation of single variable functions and techniques for differentiation, (2) applications of differentiation including identification of minima, maxima, and inflection points, (3) antiderivatives, the definite integral, and the Fundamental Theorem of Calculus, (4) techniques of integration including substitution, integration by parts, etc.; using integrals to compute areas, and (5) sequences and series, convergence of each, partial and infinite sums, geometric series, Taylor series. |
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| PHST 563 | Math Tools III | 1 hour |
| This course covers mathematical tools required for sound comprehension of mathematical probability and statistics concepts included in methodological portions of coursework in the MS in Biostatistics degree. Course topics include: (1) multivariable functions, limits, and continuity, (2) partial differentiation and its applications, and (3) multiple integration and its applications. |
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| PHST 564 | Math Tools IV | 1 hour |
| This course covers mathematical tools required for sound comprehension of mathematical probability and statistics concepts included in methodological portions of elective coursework in the MS in Biostatistics degree. Course topics include: (1) vector/matrix algebra and operations, (2) solving systems of linear equations, (3) vector spaces, linear independence, rank, and basis, (4) eigenvalues and eigenvectors, (5) orthogonal vectors and projections, (6) quadratic forms |
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| PHST 602 | Biostatistics Seminar | 1 hour |
| Weekly seminar series with topics covering active areas of research in biostatistics. |
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| PHST 603 | Biostatistics Public Health Practicum I | 1-2 hours |
| Practical experience in biostatistical collaboration in which a student works with one or more investigators in the health sciences. |
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| PHST 604 | Biostatistics Public Health Practicum II | 1-2 hours |
| Practical experience in biostatistical collaboration in which a student works with one or more investigators in the health sciences. |
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| PHST 620 | Introduction to Statistical Computing | 3 hours |
| This course addresses fundamentals of statistical computing with special emphasis on software tools employed most often in biostatistics. |
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| PHST 624 | Clinical Trials I: Planning and Design | 2 hours |
| Phases of trials, experimental designs, inclusion and exclusion criteria, randomization and blinding, the general linear model and mixed and fixed effects repeated measures analysis of variance, intention to treat methods, survival analysis. |
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| PHST 625 | Clinical Trials II | 2 hours |
| Protocol development; patient recruitment and retention; safety and efficacy; benefit to risk assessment; monitoring and auditing trials; terminating or extending clinical trials; and, regulatory, patent and legal considerations. |
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| PHST 630 | Applied Statistical Models | 3 hours |
| Topics will include linear and multiple regression, analysis of variance, analysis of covariance, logistic regression, survival analysis using Cox regression and repeated measures. |
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| PHST 631 | Data Collection for Clinical Research | 2 hours |
| Design of data collection instruments for clinical research and the psychometric properties of measurement instruments with coverage of REDCap and SPSS. |
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| PHST 640 | Statistical Methods for Research Design in Health Sciences | 3 hours |
| Statistical methods for clinical research and interpretation for the literature. |
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| PHST 645 | Health Sciences Data Collection Instrumentation | 3 hours |
| Design of data collection instruments in the health sciences with extensive coverage of psychometric and biometric properties of measurement instruments. Usage of the Epi Info and SPSS software packages for data collection and analysis. |
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| PHST 650 | Advanced Topics in Biostatistics | 3 hours |
| A treatment of one or more topics in advanced biostatistics not usually covered in a regularly offered course. |
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| PHST 655 | Basic Statistical Methods for Bioinformatics | 3 hours |
| An introduction to some core topics in bioinformatics. Topics will include-pairwise and multiple sequence alignment algorithms; gene expression profiling using microarrays; introduction to next generation sequencing; analyzing RNA-Seq data and phylogenetics. Students are expected to be familiar with some elementary statistics and probability concepts. |
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| PHST 661 | Probability | 3 hours |
| This course in introductory probability theory; includes probability spaces, random variables, probability distributions, moments, moment generating functions, mathematical expectation, joint distribution, transformations of random variables, sampling distributions. |
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| PHST 662 | Mathematical Statistics | 3 hours |
| This course in introductory statistical theory; includes limiting distributions, central limit theorem, point estimation, maximum likelihood estimation, least squares, sufficiency and completeness, confidence intervals, Bayesian estimation, Neyman-Pearson theory of hypothesis testing, statistical power, uniformly most powerful tests, likelihood ratio tests, non-central distributions, advanced topics as time permits. |
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| PHST 666 | Master's Thesis Research | 1-6 hours |
| Mentored research; M.S. thesis preparation. |
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| PHST 671 | Special Topics in Biostatistics | 1-3 hours |
| A treatment of one or more topics in advanced Biostatistics not usually covered in a regularly offered course. May be repeated under different subtitles. |
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| PHST 675 | Independent Study in Biostatistics | 1-3 hours |
| Course allows students to pursue advanced study with faculty guidance on a topic related to biostatistics. |
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| PHST 680 | Biostatistical Methods I | 3 hours |
| A mathematically sophisticated presentation of principles and methods of: exploratory data analysis; statistical graphics; point estimation; interval estimation; hypothesis testing of means, proportions and counts; chi-square analysis; rate ratio; and Mantel-Haensel analysis. Matrix algebra is required. Data sets will be analyzed using statistical computer packages; examples will be drawn from the biomedical and public health literature. Emphasis will be placed on methods and models most useful in clinical research. |
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| PHST 681 | Biostatistical Methods II | 3 hours |
| A mathematically sophisticated introduction to: general linear models; regression; correlation; analysis of covariance; one and two-way analysis of variance; and multiple comparisons. Matrix algebra is required. Data sets will be analyzed using statistical computer packages; examples will be drawn from the biomedical and public health literature. Emphasis will be placed on methods and models most useful in clinical research. |
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| PHST 682 | Multivariate Statistical Analysis | 3 hours |
| Focuses on the multivariate statistical methods; topics include the multivariate normal distribution, inference for mean vectors; inference for covariance and correlation matrices, analysis of covariance structure, analysis of serial measurements, factor analysis, and discriminant analysis. Instruction will also be given in the proper use of software to carry out these analyses. Emphasis will be placed on methods and models most useful in clinical research. |
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| PHST 683 | Survival Analysis | 3 hours |
| Focuses on statistical methods for analyzing survival data, including both parametric and nonparametric methods. Topics include life-table analysis, proportional hazard models, log-rank tests, parametric survival distributions, graphical methods, and goodness- of -fit tests. Emphasis will be placed on methods and models most useful in clinical research. |
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| PHST 684 | Categorical Data Analysis | 3 hours |
| Focuses on statistical methods for analyzing categorical data; topics include inference for two-way contingency tables; models for binary response variables, including logistic and logit models; models for ordinal data; mutinominal response data; and analysis of repeated categorical response data. Emphasis will be placed on methods and models most useful in clinical research. |
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| PHST 691 | Bayesian Analysis | 3 hours |
| Focus on the use of Bayesian probability and statistics in scientific inference. The frequency and subjective interpretations of probability are explored, as well as probability and decision making. |
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| PHST 703 | Biostatistical Consulting Practicum | 1-3 hours |
| Practical experience in biostatistical collaboration in which a student works with one or more investigators in the health sciences. |
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| PHST 710 | Advanced Statistical Computing I | 3 hours |
| This course will cover modern/classical statistical/biostatistical methods like smoothing techniques and data summaries, linear models, generalized linear models, modern nonlinear regression techniques, multivariate statistics using S-PLUS/R and SAS. Several real data examples will be analyzed following the 4th edition of the book titled Modern Applied Statistics with S by Venables and Ripley. |
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| PHST 711 | Advanced Statistical Computing II | 3 hours |
| The course covers advanced topics in statistical computing, with an emphasis on biostatistical applications. Topics include matrix factorization, methods, numerical optimization, the EM algorithm, random number generation, Monte Carlo techniques, simulation, randomization and resampling methods, bootstrapping, and recursive partitioning. Computer programming will be conducted using MATLAB,R, and SAS IML. |
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| PHST 724 | Advanced Clinical Trials | 3 hours |
| Advanced statistical methods for design and analysis of clinical trials. Content includes analysis of complex clinical trial designs, including post-stratification, cross-over, phases I, II, and III clinical trials. Sample size calculations will be covered. Interim analysis methods and sample size re-estimation methods will be developed. |
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| PHST 750 | Statistics for Bioinformatics | 3 hours |
| Development of high throughout technologies has changed the face of biological sciences. The high dimensional complicated data generated from DNA sequences, genetic maps, and polymorphic marker data etc. help to unravel the mysteries of many biological processes. However, sophisticated statistical methods and computational tools are needed to analyze these data. This course will introduce basics of genetics and introduction of such data, knowledge of statistical inference and probability, Introduction to stochastic processes, Analysis of DNA and protein sequences, Hidden Markov models, Evolutionary models etc. This course is developed for individuals interested in pursuing research in computational biology, genomics, and bioinformatics. Students are expected to be familiar with some elementary statistics and probability concepts. |
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| PHST 751 | High-throughput Data Analysis | 3 hours |
| High-throughput technology has changed the dimension of biotechnology. The array of high-speed, highly automated bio-technical equipment DNA sequencers, microarray (DNA, Protein), proteomic analyzers ( mass spectrometers) and cell sorters are all designed to capture and process vast amounts of biological data at high speeds. We will briefly discuss some of these technologies. Secondly, this course will concentrate with the process of microarray data mining (analysis) from beginning to end. In particular, this course will provide researchers and practitioners guidelines to use appropriate statistical methodology for experimental design, image processing, normalization, identifying differently expressed genes, clustering and classification techniques etc. Introduction to S-PLUS/R library for the data analysis will also be attempted. |
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| PHST 752 | Statistical Genetics | 3 hours |
| This course covers methods for mapping disease associated genes in human populations, including linkage, association, and quantitative trait analysis. Software and study design for gene mapping studies are also covered. |
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| PHST 762 | Advanced Statistical Inference | 3 hours |
| This course is a mathematically sophisticated introduction to the theory and methods of statistical inference. Students will learn fundamental technical tools that are essential to carry out methodological research in the field of Biostatistics. Emphasis will be placed on how to correctly propose statistical methods in a general setting including concepts such as asymptotic unbiasedness, robust variance estimation and efficiency. |
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| PHST 777 | Dissertation Research | 1-12 hours |
| Mentored research towards completion of the doctoral dissertation. |
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| PHST 780 | Advanced Nonparametrics | 3 hours |
| A mathematically advanced introduction to theory and methods of nonparametric statistical methods. Course will be useful to students planning to analyze data that do not follow a standard parametric distribution. |
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| PHST 781 | Advanced Linear Models | 3 hours |
| An introduction to the theory of linear models, with an emphasis on health sciences applications. Topic coverage includes projections, distributions of quadratic forms under normality, estimation procedures, general linear hypotheses, estimating and testing linear parametric functions, simultaneous inference, multifactor ANOVA models, hierarchical linear models, mixed effects models, and covariance parameter estimation methods. Examples will be illustrated using advanced statistical software. |
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| PHST 782 | Generalized Linear Models | 3 hours |
| Advanced statistical methods using inference based on the exponential family of distributions. Relationship to linear and non-linear regression. Theoretical development of link functions. Model-building and assessment of goodness-of-fit. Estimation and hypothesis testing. Correlated response methods using generalized estimating equations. |
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| PHST 783 | Advanced Survival Analysis | 3 hours |
| This course is a mathematically advanced introduction to the theory and methods of survival analysis. This course will be useful for students planning to analyze complex event time data including multivariate survival and multistate data. Also it will be useful for students who are planning to carry out research in the general area of survival analysis. |
