Full Course Listing

Below is a listing of Department of Bioinformatics and Biostatistics courses (PHST courses). These courses are offered with varying frequency and on different calendar rotations. The list below contains only course codes, names, and descriptions. Information about pre- and co-requisites for these courses can be found in the university’s course catalog. A listing of courses being offered for the current and forthcoming academic year can be obtained from the University of Louisville class schedule.


PHST 500Introduction to Biostatistics for Health Sciences I3 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.

PHST 501Introduction to Biostatistics for Health Sciences II3 hours
Continued introduction to descriptive and inferential statistical methods including ANOVA regression, chi-square analysis of frequencies, survival analysis, and nonparametric methods.

PHST 602Biostatistics Seminar1 hour
Weekly seminar series with topics covering active areas of research in biostatistics.

PHST 603Biostatistics Public Health Practicum I1-2 hours
Practical experience in biostatistical collaboration in which a student works with one or more investigators in the health sciences.

PHST 604Biostatistics Public Health Practicum II1-2 hours
Practical experience in biostatistical collaboration in which a student works with one or more investigators in the health sciences.

PHST 620Introduction to Statistical Computing3 hours
This course addresses fundamentals of statistical computing with special emphasis on software tools employed most often in biostatistics.

PHST 624Clinical Trials I: Planning and Design2 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.

PHST 625Clinical Trials II2 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.

PHST 626Clinical Trials Statistics Laboratory1 hour
Statistical methods described in Clinical Trials I will be demonstrated and taught with hands-on examples and homework problems. Methods covered include randomization methods, sample size calculations, post-stratification, Phase II early-stopping designs, repeated -measures analysis, survival analysis, and methods to avoid or reduce multiplicity.

PHST 630Applied Statistical Models3 hours
Topics will include linear and multiple regression, analysis of variance, analysis of covariance, logistic regression, survival analysis using Cox regression and repeated measures.

PHST 631Data Collection for Clinical Research3 hours
Design of data collection instruments for clinical research and the psychometric properties of measurement instruments with coverage of REDCap and SPSS.

PHST 640Statistical Methods for Research Design in Health Sciences3 hours
Statistical methods for clinical research and interpretation for the literature.

PHST 645Health Sciences Data Collection Instrumentation3 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.

PHST 650Advanced Topics in Biostatistics3 hours
A treatment of one or more topics in advanced biostatistics not usually covered in a regularly offered course.

PHST 660Mathematical Tools3 hours
This course focuses on the basic techniques of analytic geometry, differential and integral calculus, and matrix algebra; topics include limits, the chain rule, higher-order derivatives, partial derivatives, integration by parts, improper integrals, multiple integrals, sequences and series, vector and matrix arithmetic, and eigenvalues.

PHST 661Probability3 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.

PHST 662Mathematical Statistics3 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.

PHST 666Master's Thesis Research1-6 hours
Mentored research; M.S. thesis preparation.

PHST 671Special Topics in Biostatistics1-3 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.

PHST 675Independent Study in Biostatistics1-3 hours
Course allows students to pursue advanced study with faculty guidance on a topic related to biostatistics.

PHST 680Biostatistical Methods I3 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.

PHST 681Biostatistical Methods II3 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.

PHST 682Multivariate Statistical Analysis3 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.

PHST 683Survival Analysis3 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.

PHST 684Categorical Data Analysis3 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.

PHST 691Bayesian Analysis3 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.

PHST 703Biostatistical Consulting Practicum1-3 hours
Practical experience in biostatistical collaboration in which a student works with one or more investigators in the health sciences.

PHST 704Mixed Effect Models and Longitudinal Data Analysis3 hours
The course focuses on theory and application of linear and nonlinear mixed effect models, particularly, the application of mixed models to longitudinal data analyses.

PHST 710Advanced Statistical Computing I3 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.

PHST 711Advanced Statistical Computing II3 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.

PHST 724Advanced Clinical Trials3 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.

PHST 725Design of Experiments3 hours
The course introduces experimental design principles and covers specific designs in detail. Topics include the completely randomized design, the randomized complete block design, cross-over designs, nested and hierarchical designs, factorial treatment arrangements, incomplete block designs, response surface methodology, and optimal designs. Concepts will be illustrated using examples from the health services.

PHST 750Statistics for Bioinformatics3 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.

PHST 751High-throughput Data Analysis3 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.

PHST 752Statistical Genetics3 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.

PHST 762Advanced Statistical Inference3 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.

PHST 777Dissertation Research1-12 hours
Mentored research towards completion of the doctoral dissertation.

PHST 780Advanced Nonparametrics3 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.

PHST 781Advanced Linear Models3 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.

PHST 782Generalized Linear Models3 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.

PHST 783Advanced Survival Analysis3 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.

PHST 785Nonlinear Regression3 hours
Advanced statistical methods for nonlinear models. Review of linear models and intrinsically linear models. Survey of generalized linear models. Development of nonlinear models, with an emphasis on uses in Phase I clinical trials, relationship to differential equations. Estimation and goodness-of-fit. Sample size methods.

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