College of Public Health
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Biostatistics Department: Teaching
Graduate Courses
The Biostatistics Department
offers the following graduate courses. Please check with the
UNMC Graduate Studies Office to
determine courses offered each semester. All prospective
students who wish to register for graduate courses must apply for admission to
the Graduate College.
BIOS 806 Biostatistics I
Fall 2008 Syllabus
Kendra Schmid, Ph.D.
3 credit hours
Fall & Spring Semesters
Prerequisite: graduate standing, degree-seeking students; a statistics class within 5 years or HED 8080.
This course is designed to prepare the graduate student to understand and apply biostatistical methods needed in the
design and analysis of biomedical and public health investigations. The major topics to be covered include types of
data, descriptive statistics and plots, theoretical distributions, probability, estimation, hypothesis testing, and
one-way analysis of variance. A brief introduction to correlation and univariate linear regression will also be
given. The course is intended for graduate students and health professionals interested in the design and analysis
of biomedical or public health studies. The statistical software package SPSS will be used for all analyses and we
assume students have basic computer knowledge (i.e. MS-Word).
BIOS 808 Biostatistics II
Spring 2008 Syllabus
Fang Yu, Ph.D.
3 credit hours
Spring Semester
Prerequisite: BIOS 806 or permission of instructor
This course is designed to prepare the student to understand and apply advanced biostatistical methods needed in
the design and analysis of biomedical investigations. The major topics to be covered include multiple linear
regression, analysis of covariance, logistic regression, survival analysis, and repeated measures analysis. The
statistical software package SPSS will be used for all analyses.
BIOS 810: Introduction to SAS Programming
Summer 2008 Syllabus
Elizabeth Lyden, M.S.
3 credit hours
Summer Session
Prerequisites: BIOS 806 or an equivalent, introductory statistics course; and instructor permission.
This course is an introduction to programming for statistical and epidemiologic analysis using the SAS Software
System. Major topics include data processing, descriptive and inferential analysis, graphical presentation, and
application of basic informatics techniques to vital statistics and public health databases.
BIOS 816: Biostatistical Methods I
Fall 2008 Syllabus
Jane Meza, Ph.D.
3 credit hours
Fall Semester
Prerequisites: Calculus (covering differential and integral calculus); instructor permission.
This course is designed to prepare the graduate student to understand and apply biostatistical methods needed
in the design and analysis of biomedical and public health investigations. The major topics to be covered
include types of data, descriptive statistics and plots, theoretical distributions, probability, estimation,
hypothesis testing, non-parametric methods, and one-way analysis of variance. A brief introduction to
correlation and univariate linear regression will also be given. Interpretation of subsequent analysis
results will be stressed. Concepts will be explored using the biomedical and public health literature,
class exercises, exams, and a data analysis project. Statistical analysis software, SAS (SAS Institute
Inc., Cary, NC, USA.), will be used to implement analysis methods. The course is intended for graduate
students and health professionals who will be actively involved in the design, analysis, and interpretation
of biomedical research or public health studies.
BIOS 818: Biostatistical Methods II
Spring 2009
3 credit hours
Spring Semester
Prerequisites: Prior linear algebra course (covering matrix notation and matrix algebra, equivalent to UNO
MATH 2050 or UNL MATH 314), calculus, and Biostatistical Methods I, BIOS 816, or an equivalent introductory statistics
course; instructor permission.
This course is designed to prepare the graduate student to analyze continuous data and interpret results using methods
of linear regression and analysis of variance (ANOVA). The major topics to be covered include simple and multiple
linear regression model specification and assumptions, specification of covariates, confounding and interactive
factors, model building, transformations, ANOVA model specification and assumptions, analysis of covariance (ANCOVA),
multiple comparisons and methods of adjustment, fixed and random effect specification, nested and repeated measures
designs and models, and diagnostic methods to assess model assumptions. Interpretation of subsequent analysis results
will be stressed. Concepts will be explored through critical review of the biomedical and public health literature,
class exercises, an exam, and a data analysis project. Statistical analysis software, SAS (SAS Institute Inc., Cary,
NC, USA.), will be used to implement analysis methods. The course is intended for graduate students and health
professionals who will be actively involved in the analysis and interpretation of biomedical research or public
health studies.
BIOS 835: Design of Medical Health Studies
Spring 2009
Jim Lynch, Ph.D.
3 credit hours
Spring Semester
Prerequisites: Biostatistics I, BIOS806, or an equivalent, introductory statistics course and instructor permission.
This course is designed to prepare the graduate student to understand and apply principles and methods in the design
of biomedical and public health studies, with a particular emphasis on randomized, controlled clinical trials. The major
design topics to be covered include sample selection, selecting a comparison group, eliminating bias, need for and
processes of randomization, reducing variability, choosing endpoints, intent-to-treat analyses, sample size
justification, adherence issues, longitudinal follow-up, interim monitoring, research ethics, and non-inferiority
and equivalence hypotheses. Data collection and measurement issues also will be discussed. Communication of design
approaches and interpretation of subsequent analysis results also will be stressed. Concepts will be explored through
critical review of the biomedical and public health literature, class exercises and a research proposal. The course is
intended for graduate students and health professionals interested in the design of biomedical or public health studies.
Courses Under Development
BIOS 823: Categorical Data Analysis
3 credit hours
This course surveys the theory and methods for the analysis of categorical response and count data. The major topics
to be covered include proportions and odds ratios, multi-way contingency tables, generalized linear models, logistic
regression for binary response, models for multiple response categories, loglinear models, and simple mixture models
for categorical data. Interpretation of subsequent analysis results will be stressed. Concepts will be explored
through critical review of the biomedical and public health literature, class exercises, an exam, and a data analysis
project. Computations will be illustrated using SAS statistical software (SAS Institute Inc., Cary, NC, USA.). The
course is intended for graduate students and health professionals who will be actively involved in the analysis and
interpretation of biomedical research or public health studies. Prerequisites: Instructor permission, Biostatistical
Methods I, BIOS 808, or an equivalent introductory statistics course, and Biostatistical Methods II, BIOS 818, or an
equivalent advanced statistics course.
BIOS 824: Survival Data Analysis
3 credit hours
The course teaches the basic methods of statistical survival analysis used in clinical and public health research.
The major topics to be covered include the Kaplan-Meier product-limit estimation, log-rank and related tests, and
the Cox proportional hazards regression model. Interpretation of subsequent analysis results will be stressed. Concepts
will be explored through critical review of the biomedical and public health literature, class exercises, two exams,
and a data analysis project. Computations will be illustrated using SAS statistical software (SAS Institute Inc.,
Cary, NC, USA.). The course is intended for graduate students and health professionals who will be actively involved
in the analysis and interpretation of biomedical research or public health studies generating time-to-event data.
Prerequisites: Instructor’s permission, Biostatistical Methods I, BIOS816, or an equivalent introductory statistics
course, and Biostatistical Methods II, BIOS818, or an equivalent advanced statistics course.
BIOS 825: Correlated Data Analysis
3 credit hours
This course surveys the theory and methods for the analysis of correlated continuous, binary, and count data. The
major topics to be covered include linear models for longitudinal continuous data, generalized estimating equations,
generalized linear mixed models, impact of missing data, and design of longitudinal and clustered studies.
Interpretation of subsequent analysis results will be stressed. Concepts will be explored through critical review
of the biomedical and public health literature, class exercises, two exams, and a data analysis project.
Computations will be illustrated using SAS statistical software (SAS Institute Inc., Cary, NC, USA.). The
course is intended for graduate students and health professionals who will be actively involved in the analysis
and interpretation of biomedical research or public health studies. Prerequisites: Instructor’s permission and
Biostatistics 823.
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