BIOSTATISTICS (BSTA)
509. Introduction to Epidemiology.
510. 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 will
focus 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.
620. (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.
621. (STAT432, STAT512) Statisical
Inference I. (B) Faculty. Prerequisite(s): BSTA 620.
Statistical inference including estimation, confidence intervals,
hypothesis tests and non-parametric methods.
622. (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 meta-analyses 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.
