University of Minnesota



Course Syllabus

PubH 7430

Statistical Methods for Correlated Data

Fall 2009

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Credits: 3

Meeting Days: TTH

Meeting Time: 11:15am-12:30pm

Meeting Place: Mayo 3-100; Jackson Hall 2-137

Instructor: Kyle Rudser, PhD

Office: 717 Delaware Building, 219

Phone: 612-626-6814

E-mail: rudser@umn.edu

Office Hours: Thursdays 12:30-1:30 or by appointment

TA: Sun Kyong

Office: Biostat TA office Mayo A-452

E-mail: kimx1606@umn.edu

Office Hours: Fridays 2:30-4:00pm or 9:30-11:00am adjusted according to students’ class schedules

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I. Course Description

Correlated data arising from data collected over time or space, group randomizations, cluster sampling, nested designs, or random effects assumptions. Modeling, analysis, and interpretation appropriate for such data, for normally or non-normally (e.g. binary, Poisson, gamma) distributed outcomes. Computing using SAS and R.

II. Course Prerequisites

• Statistics: Linear Regression, at the level of PubH 6451 (Biostatistics II) or PubH 7405 (Biostatistics: Regression) or Stat 5302 (Applied Regression Analysis).

• Working knowledge of a statistical software (Splus or R or SAS, e.g, at the level of PubH 6420). Other software, e.g., Stata or SPSS, can also be used for data analysis but support can be guaranteed from the instructor or teaching assistant.

• Some familiarity with basic matrix notation and operations (e.g., multiplication, inverse, transpose).

Note: This is a second year course for MS students in Biostatistics or Statistics. However it is meant to be accessible to PhD students in other quantitative fields. There will be a focus on applications, although underlying statistical concepts and theory will be reviewed as necessary.

Students should be familiar with the basic notions of random variables, statistical inference (confidence intervals, hypothesis testing), multiple linear regression, and logistic regression. Familiarity with matrix notation is helpful; we will review this VERY briefly towards the beginning of the course. During the semester, the underlying statistical theory will be outlined using matrix notation, but deep understanding of the theory is not necessary for homeworks or exams. The course is accessible to graduate students in all fields.

III. Course Goals and Objectives

The objective of this course is to further enhance the students' capability and comfort level in data analysis and study design, specifically in the context of correlated data. After taking this course, students should be able to recognize situations where it is necessary to account for correlation, apply necessary techniques in analysis and study design, and interpret the results.

IV. Methods of Instruction and Work Expectations

Students are expected to attend class, participate in class discussions, and complete the assigned homeworks, exam, and project. Working together on homework assignments is allowed and encouraged. However, each student is expected to independently write-up homework assignments, using their own computing and in their own words.

V. Course Text and other Materials

There is no required text for this course. However, the following are suggested for consideration:

• Diggle, Heagerty, Liang, and Zeger (2002) Analysis of Longitudinal Data, New York: Oxford University Press.  ISBN 0198524846. (This is an excellent text that gives some mathematical theory as well as practical aspects and applications of methods for the analysis of longitudinal data. If you have the first edition, that will do quite well, though there are two excellent new chapters in the second edition on advanced material.)

• Fitzmaurice, Laird and Ware (2004) Applied Longitudinal Analysis. John Wiley and Sons. (This text provides an introductory presentation of longitudinal data methods suitable for graduate level work.)

• Verbeke and Molenberghs (2000). Linear Mixed Models for Longitudinal Data, New York: Springer-Verlag, Inc. ISBN 0387950273. (If you expect your future work to involve lots of longitudinal data analysis in SAS, this is a helpful book to own.)

• Weiss (2005) Modeling Longitudinal Data, New York: Springer-Verlag, Inc.  ISBN 0387402713. (This text has been part of either the required or recommended materials for this course the past several years. The fundamental approach taken is via modeling the data, which I am generally not in favor of for inference.)

In addition, the Biostatistics Reading Room (Mayo A-460) has full documentation for SAS Version 8, two books on graphing in SAS, and the following:

• Littell, Miliken, Stroup, and Wolfinger (1996). SAS System for Mixed Models, Cary, NC: SAS Institute, Inc. For detailed examples using SAS PROC MIXED.

• Khattree and Naik (1999). Applied Multivariate Statistics with SAS Software, 2nd Edition, Cary, NC: SAS Institute, Inc. For detailed graphing and modeling examples using SAS.

These cannot be checked out except to make copies, but can be browsed in the Reading Room.

VI. Course Outline/Weekly Schedule

Tentative schedule:

• Overview and introduction (week 1)

• Review independent approaches (2)

• Paired data, pre-post analyses (3)

• Exploratory analysis of correlated data (4-5)

• Derived variables (6)

• Correlation structures (7)

• Marginal Models (GEE) (8-10)

• Mixed Models (GLMM) (10-12)

• Review, midterm exam (13)

• Missing Data (14-15)

• Advanced topics (time permitting): transition models, nonlinear models, etc.

VII. Evaluation and Grading

There will be approximately 5 homework assignments. For each assignment it is intended to have two weeks before it is due. There will also be a midterm exam towards the middle of the term. There will also be a final project towards the end of the term (no final exam). The project will entail a formal correlated data analysis and write-up. More details will be given later in the course.

Homeworks, midterm, and final project will each be worth 50 points.

A letter grade will be determined from the percentage of points each student receives as follows:

| | |B+ |87-89% |C+ |77-79% |D+ |67-69% |

|A |93-100% |B |83-86% |C |73-76% |D |63-66% |

|A- |90-92% |B- |80-82% |C- |70-72% |F |0-62% |

For those enrolled S/N, a letter grade of C or better must be achieved to receive an S. The University Senate has established a uniform grading policy for all letter grades: www1.umn.edu/usenate/policies/ gradingpolicy.html. If you would like to switch grading options (e.g., A/F to S/N), it must be done within the first two weeks of the semester.

Course Evaluation

Beginning in fall 2008 the SPH will collect student course evaluations electronically using a software system called CoursEval. The system will send email notifications to students when they can access and complete their course evaluations. Students who complete their course evaluations promptly will be able to access their final grades just as soon as the faculty member renders the grade. All students will have access to their final grades two weeks after the last day of the semester regardless of whether they completed their course evaluation or not. Student feedback on course content and faculty teaching skills are important means for improving our work. Please take the time to complete a course evaluation for each of the courses for which you are registered.

Incomplete Contracts

A grade of incomplete “I” shall be assigned at the discretion of the instructor when, due to extraordinary circumstances (e.g., documented illness or hospitalization, death in family, etc.), the student was prevented from completing the work of the course on time. The assignment of an “I” requires that a contract be initiated and completed by the student before the last day of class, and signed by both the student and instructor. If an incomplete is deemed appropriate by the instructor, the student in consultation with the instructor, will specify the time and manner in which the student will complete course requirements. Extension for completion of the work will not exceed one year (or earlier if designated by the student’s college). For more information and to initiate an incomplete contract, students should go to: sph.umn.edu/grades.

University of Minnesota Uniform Grading and Transcript Policy

A link to the policy can be found at onestop.umn.edu.

VIII. Other Course Information and Policies

Grade Option Change (if applicable)

For full-semester courses, students may change their grade option, if applicable, through the second week of the semester. Grade option change deadlines for other terms (i.e. summer and half-semester courses) can be found at onestop.umn.edu.

Course Withdrawal

Students should refer to the Refund and Drop/Add Deadlines for the particular term at onestop.umn.edu for information and deadlines for withdrawing from a course. As a courtesy, students should notify their instructor and, if applicable, advisor of their intent to withdraw.

Students wishing to withdraw from a course after the noted final deadline for a particular term must contact the School of Public Health Student Services Center at sph-ssc@umn.edu for further information.

Student Conduct, Scholastic Dishonesty and Sexual Harassment Policies

Students are responsible for knowing the University of Minnesota, Board of Regents' policy on Student Conduct and Sexual Harassment found at umn.edu/regents/polindex.html.

Students are responsible for maintaining scholastic honesty in their work at all times. Students engaged in scholastic dishonesty will be penalized, and offenses will be reported to the Office of Student Academic Integrity (OSAI, osai.umn.edu).

The University’s Student Conduct Code defines scholastic dishonesty as “plagiarizing; cheating on assignments or examinations; engaging in unauthorized collaboration on academic work; taking, acquiring, or using test materials without faculty permission; submitting false or incomplete records of academic achievement; acting alone or in cooperation with another to falsify records or to obtain dishonestly grades, honors, awards, or professional endorsement; or altering, forging, or misusing a University academic record; or fabricating or falsifying of data, research procedures, or data analysis.”

Plagiarism is an important element of this policy. It is defined as the presentation of another's writing or ideas as your own. Serious, intentional plagiarism will result in a grade of "F" or "N" for the entire course. For more information on this policy and for a helpful discussion of preventing plagiarism, please consult University policies and procedures regarding academic integrity: .

Students are urged to be careful that they properly attribute and cite others' work in their own writing. For guidelines for correctly citing sources, go to and click on “Citing Sources”.

In addition, original work is expected in this course. It is unacceptable to hand in assignments for this course for which you receive credit in another course unless by prior agreement with the instructor. Building on a line of work begun in another course or leading to a thesis, dissertation, or final project is acceptable.

Disability Statement

It is University policy to provide, on a flexible and individualized basis, reasonable accommodations to students who have a documented disability (e.g., physical, learning, psychiatric, vision, hearing, or systemic) that may affect their ability to participate in course activities or to meet course requirements. Students with disabilities are encouraged to contact Disability Services to have a confidential discussion of their individual needs for accommodations. Disability Services is located in Suite180 McNamara Alumni Center, 200 Oak Street. Staff can be reached by calling 612/626-1333 (voice or TTY).

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