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Course No. BMTRY 701

COURSE DESCRIPTION

COURSE TITLE Biostatistical Methods II (Regression methods)

DEPARTMENT Biostatistics, Bioinformatics,

and Epidemiology

PRINICIPAL

INSTRUCTOR Biostatistics Faculty

APPROVED DATE

Department Chairman

COURSE OFFERED

Yearly x METHOD OF EVALUATION % of GRADEAlternate Years

On Demand Written Assignment(s) *

Written Reports(s)

WHEN OFFERED Written Examination 75

Fall Semester (15 wks) Discussion/Presentationx Spring Semester (15 wks) Oral Examination(s)

Summer Semester (14 wks) Attitude/Application

Laboratory Projects/Reports 25*

* Written assignments include homework, laboratory projects

LENGTH OF COURSE 15 WKS.

TYPE OF SESSION

Outside Prep.

Est. Hrs./Week

No. of

Hrs./Week

Semester

Hrs. Credit No. Student Acc.

Lecture Minimum 4

Recitation or Discussion Maximum 20Laboratory Prerequisites

700

Field Work

Independent Study

Total Credits 4

CATALOG DESCRIPTION (Include general objectives, content coverage, and student to whom

it is directed).

The course is intended to focus on biostatistical applications by providing a broad coverage of

critical biostatistical applicationstopics. The primary audience for the sequence is M.S. students (orfirst-year PhD students that do not have a prior M.S. degree) in biostatistics, but the course will be

delivered at a level that it may be taken by graduate students in related scientific fields such as

bioinformatics and epidemiology.

Class Topics:

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Course No. BMTRY 701

Simple linear regression (least squares estimation, partitioning sums of squares, hypothesistesting of slope and intercept, model fit, confidence intervals of mean response, prediction

intervals)

Correlation (the correlation coefficient, test of hypothesis and confidence intervals, testes ofequality of correlation coefficients, multiple correlation coefficient, partial correlation)

Analysis of variance (inference based on F-statistic, R-notation, sums of squares, one-wayANOVA)

Multiple linear regression (general linear model, linear contrasts, testing of general linearhypotheses, confidence intervals, prediction intervals)

Model Specification (biologic plausibility, interaction, confounding, indicator variables,iterative predictor selection routines)

Model Diagnostics (diagnostic plots, multicollinearity, residual analysis, influencediagnostics)

Nonstandard conditions (transformations, heterogeneous variance, weighted least squares) Maximum likelihood (principle of maximum likelihood, statistical inference via maximum

likelihood, likelihood ratio tests) Logistic regression (ungrouped versus grouped data, interpretation, maximum likelihood

estimation)

Poisson regression (maximum likelihood estimation, interpretation, goodness of fit) Survival analysis (life tables, Kaplan Meier estimator, log-rank test, proportional-hazards

(Cox) model, graphical examination of model assumptions)

TYPE AND AMOUNT OF WORK EXPECTED OF STUDENTS

Weekly homework assignments, semester project, two midterm exams, and final exam.

ASSIGNED TEXT AND OTHER REFERENCE MATERIALS

Applied Linear Statistical Models (5rd

edition, 1998) Kutner et al.

SPECIAL FACILITIES REQUIRED (Including Library and other Learning Resources) (List

and indicate whether presently available or must be obtained)

Library and computers: Available.

Other Personnel Involved: In What Capacity?

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Course No. BMTRY 701

Teaching Assistant

(for large classes)

Grading homework assignments and assisting students

RELATION TO OTHER COURSES

How does this course fit into the Department's Graduate Program?

Core first year course.

Is this course a prerequisite for any other course? If yes, what course(s)?

Yes: Linear Models, Categorical Analysis, Survival Analysis, Multivariate Analysis, Experimental

Design and Advanced Regression

Do other course(s) cover some of the same material? Which ones (explain extent)?

This course is intended as a broad spectrum introduction to core methodology. While there is some

review of 700 (simple linear regression), this course presents fundamental methods, in an applied

sense, that will be reinforced more formally in advanced PhD-level courses.

PROJECTED SIZE AND SOURCE OF ENROLLMENT

Graduate Students: Per Year Other Students: (Specify) Per Year

Within Department 8 6 (Clin. M.S.) 0Outside Department 2 2

DETAILED STATEMENT OF OBJECTIVES (i.e. what will the student gain from the course)?

Students will be able to understand a considerable range of multiple predictor regression models

including linear, logistic, survival, and Poisson. The SAS software will be used for computations, and

students will be able to interpret results generated from SAS and other statistical packages. The

modeling concepts will include interactions, covariates, dummy variables, remedies for assumptions not

met, including transformations, multiple comparisons, and multicollinearity.

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Course No. BMTRY 701

Department of Biostatistics, Bioinformatics & Epidemiology

Biostatistical Methods II (701)

Class

Meetin

g

Chapter/Section* Topics

1 Concepts and Examples of Research

Classification of Variables and the Choice of Analysis

Basic Statistics: A Review

Introduction to Regression Analysis

2-3 Straight-Line Regression Analysis

4 The Correlation Coefficient and Straight-Line

Regression

5 The Analysis-of-Variance Table

6 Matrix Approach

7 Multiple Regression Analysis: General Considerations8 Testing Hypotheses in Multiple Regression

9 Correlations: Multiple Partial and Multiple-Partial

10 Confounding and Interaction in Regression

11 Review

12 Exam

13 Regression Diagnostics

14 Regression Diagnostics (contd)

15 Dummy Variables in Regression

16 Analysis of Covariance and Other Methods for Adjusting

Continuous Data

17 Selecting the Best Regression Equation

18 One-Way Analysis of Variance

19 Randomized Blocks:Special Case of Two-Way ANOVA

20 Overflow

21 Review

22 Exam

23 Two-Way ANOVA With Equal Cell Numbers

24 Two-Way ANOVA With Unequal Cell Numbers

25 Analysis of Repeated Measures Data

26 The Method of Maximum Likelihood

27 Logistic Regression Analysis

28 Poission Regression Analysis

29 Review

30 Final