Department Of Biostatistics

Graduate Courses

The Biostatistics Department offers the following graduate and professional 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/CPH 506 Biostatistics I

3 credit hours
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/CPH 650 Biostatistics II

3 credit hours
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/CPH 651: Introduction to SAS Programming

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/CPH 516: Biostatistical Methods I

3 credit hours
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/CPH 652: Biostatistical Methods II

3 credit hours
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 823/CPH 653: 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/CPH 654: 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/CPH 655: 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.

BIOS 835/CPH 517: Design of Medical Health Studies

3 credit hours
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.