BIOSTATISTICS (BSTA)
509. Introduction to Epidemiology. Introduction to Anatomy and Physiology. (A) Propert. The purpose of this course is to introduce students without a background in
medicine and biology to the basic vocabulary and principles of human anatomy and physiology in preparation for
collaborative research in biostatistics. The course will begin with an overview of basic human biochemistry, cell biology,
and genetics. Later topics willfocus on the major organ systems including circulation, digestion and excretion,
neurophysiology, and reproduction. Major disease areas of research such as cancer and drug research will also be
covered.
510. (STAT430, STAT510) Probability I. (A) Morrison. Prerequisite(s): Two semesters of calculus (through multivariable calculus), linear algebra. This course is also offered in the
Summer I session. This course covers Elements of matrix algebra. Discrete and continuous random
variables and their distributions. Monents and moment generating functions. Joint distributions. Functions and
transformations of random variables. Law of large numbers and the central limit theorem. Point estimation: sufficiency,maximum
likelihood, minimum variance, confidence intervals.
620. (STAT432, STAT512) Statisical Inference I. (B) Faculty. Prerequisite(s): BSTA 620. Statistical inference including estimation,
confidence intervals, hypothesis tests and non-parametric
methods.
621. (STAT550) Statistical Inference II. (A) Brown. Prerequisite(s): BSTA 621. Statistical inference including estimation, confidence intervals, hypothesis
tests and non-parametric methods.
630. Statistical Methods for Data Analysis I. (A) Shults and Putt. Prerequisite(s): Multivariable calculus and linear algebra, BSTA 620 (may be taken concurrently). This first course in statistical methods for data analysis is aimed at first
year Biostatistics degree candidates. It focuses on the analysis of continuous data, and includes descriptive statistics, such
as central tendencies, dispersion measures, shapes of a distribution, graphical representations of distributions, transformations,
and testing for goodness of fit for a distribution. Populations, samples, hypotheses of differences and equivalence,
and errors will be defined. One and two sample t-tests, analysis of variance, correlation, as well as non-parametric
tests and correlations will be covered.
Estimation, including confidence intervals, and robust methods will be discussed.
The relationship between outcome variables and explanatory
variables will be examined via regression analysis,
including single linear regression, multiple regression,
model fitting and testing, partial correlation, residuals,
multicolinearity. Examples of medical and biologic
data will be used throughout the course, and use
of computer software demonstrated.
631. Statistical Methods and Data Analysis II. (B) Gimotty. Prerequisite(s): linear algebra, calculus, BSTA 630, BSTA 620, BSTA 621 (may be taken concurrently). This is the second half of the
methods sequence and focuses on categorical data
and survival data. Topics in categorical data to
be covered include defining rates, incidence and
prevalence, the chi-squared test, Fisher's exact
test and its extension, relative risk and odds-ratio,
sensitivity, specificity, predictive values, logistic
regression with goodness of fit tests, ROC curves,
Mantel-Haenszel test, McNemar's test, the Poisson
model, and the Kappa statistic. Survival analysis
will include defining the survival curve, censoring,
and the hazard function, the Kaplan-Meier estimate,
Greenwood's formula and confidence bands, the log
rank test, and Cox's proportional hazards regression
models. Examples of medical and biologic data will
be used throughout the course, and use of computer
software demonstrated.
651. Introduction to Linear Models and Generalized Linear Models. (B) Tu. Prerequisite(s): linear algebra, calculus, BSTA 630, BSTA 620, BSTA 621 (may be taken concurrently). This course
extends the content on linear models in BSTA 630
and BSTA 631 to more advanced concepts and applications
of linear models. Topics include the matrix approach
to linear models including regression and analysis
of variance, general linear hypothesis, estimability,
polynomial, piecewise, ridge, and weighted regression,
regression and collinearity diagnostics, multiple
comparisons, fitting strategies, simple experimental
designs (block designs, split plot), random effects
models, Best Linear Unbiased Prediction. In addition,
generalized linear models will be introduced with
emphasis on the binomial, logit and Poisson log-linear
models. Applications of methods to example data sets
will be emphasized.
690. Consulting Laboratory I. (C) Faculty. Prerequisite(s): BSTA 630. Participation in the consulting laboratory is a requirement for both the Master's
and Ph.D. degrees. This course covers general principles
of statistical consulting and statistical consulting
experience. There is training on statistical programming, preparation of reports, presentations, and the communication aspects
of consulting. Each student will be expected to join
one of several project teams consisting of faculty,
research staff, and graduate student consultants;
attend meetings along with the project team and associated
investigators; participate in all or part of the
design, management, analysis and reporting stages
of a project; and gain valuable experience in working
with actual research projects.
752. Categorical Data Analysis II.
774. Statistical Methods for Evaluating Diagnostic Tests. (A) Gimotty. Prerequisite(s): BSTA 510, BSTA 630, BSTA 631 or equivalent; permission of instructor. This course will cover statistical methodology for evaluating diagnostic tests.The
topics will include: estimation of ROC curves, comparing multiple diagnostic tests, developing diagnostic tests
using predictive models, measurement error effects on diagnostic tests, random effects models for multi-reader studies,
verification bias in dosease classification, methods for time-dependent disease classifications, study design
issues, related software, and metaanalyses for diagnostic test data.
820. (STAT552) Statistical Inference III. (B) Faculty. Prerequisite(s): To be advised. Statistical inference including estimation, confidence intervals, hypothesis
tests and non-parametric methods.
EPIDEMIOLOGY (EPID)
Contact the department for information on courses offered in Epidemiology.
L/L 522. Probability and Estimation. Shults. Prerequisite(s): Permission of Instructor. This course is the first of a four quarter sequence in Biostatistics at the
introductory level. Topics covered include graphical methods, probability, discrete and continuous distributions, estimation,
confidence intervals, and one sample hypothesis testing. Emphasis is placed on understanding the propper application
and interpretation of the methods.
L/L 523. Inference and Linear Regression. (A) Cucchiara. Prerequisite(s): EPID 522 or Permission of Instructor. This course is the second of a four quarter sequence in Biostatistics at the
introductory level. Topics covered include two sample hypothesis testing, nonparametric techniques, sample size determination,
correlatoin, regression, analysis of variance, and alaysis of covariance. Emphasis is place on undestanding the
proper application and underlying assumption of the mehtods presented. Laboratory sessions focus on the sue of
the STATA statistical package and applications to clinical data.
L/L 524. Biostatistics III. (A) Bilker. Prerequisite(s): EPID 522 and 523 or Permission of Instructor. This course is the thrid of a four quarter sequence in Biostatistics at the
introducutory level. This quarter covers concepts in biostatistics as applied to epidemiology, primarily categorical
data analysis, analysis of case-control, cross- sectional cohort studies, and clinical trails. Topics include simple analysis
of epidemiologic measures of effect; stratified analysis; confoudning; interaction, the use of matching and sample
size determination. Emphasis is placed on understanding the proper application and underlying assumptions of the methods
presented. Laboratory sessions focus on the use of STATA and other statistical packages and applications for clinical
data
L/L 525. Biostatistics for Epidemiologic Methods II. (B) Faculty. Prerequisite(s): EPID 522, 523 and 524 or permission of instructor. This course is the fourth of a four quarter sequence
in Biostatistics at the introductory level biostatistics.
This quarter covers concepts in biostatistics as
applied to epidemiology, primarily multivariable
models in epidemiology for analyzing case-control,
cross-sectional, cohort studies, and clinical trials.
Topics include logistic, conditional logistics, and
Poisson regression methods; simple survival analyses
including Cox regression. Emphasis is placed on understanding
the proper application and underlying assumptions
of the methods presented. Laboratory sessions focus
on the use of the STATA and other statistical packages
and applications to clinical data.
545. Found Comm Oriented Research.
623. Applied Survival Analysis.
SM 633. Advanced Database Mangement for Clinical Research. Holmes. This course is intended to provide in-depth, practical exposure to
the design, implementation, and use of secondary
data resoources in clinical research. This course
is inteneded to provide students with the skills
needed to design and conduct a research project using
secondary data, with a focuson data management. We
will focus on analysis only to the extent that one needs to be aware of the demands that particualar analytic strategies
put on the structure and management of data.
656. Research Methods in ID Epidemiology.
658. Gastroenterology EPI.
690. Ethical Issues In Clinical Research.
SM 714. Grant Writing/Review. Farrar. Prerequisite(s): EPID510, EPID520, EPID 560, and EPID 570. This course is designed to provide background, and guidance on writing and submitting
NIH grants. Students will submit a mini proposal at the beginning of the term. Each proposal will be reviewed
bya goupd of 3 students from the class and scores will be given. The final project will be a full NIH proposal
ready for submission.
805. Practicum In Applied Clinical Research Methods.
813. Biostatistics in Practice Lab. Faculty. SM 816. Economic Evaluation of Medical Therapies. Faculty. |
