PH 717: Applied Longitudinal Data Analysis
Spring 2016
Location: All lectures will be held in the Joseph J. Zilber School of Public Health building, Room
110
Schedule: Fridays 9:30 AM-12:10 PM
Instructor:
Cheng Zheng, PhD
Assistant Professor
Joseph J. Zilber School of Public Health
Office Hours: By appointment via email
Email: zhengc@uwm.edu
Office phone: (414) 227-3015
Course Description:
Short description: This course will cover data analysis techniques for longitudinal data with
focus on application in public health with related examples using SAS.
Full description: This course will cover data analysis techniques for longitudinal data that are
time-dependent. Methods and techniques will include mixed models, marginal models and
transition models for longitudinal data analysis. Techniques to handle missing and drop out
events for longitudinal data analysis will also be covered. The course will be taught in an applied
perspective with emphasis on public health applications. The students may use any software
they are familiar with (e.g. R), but the examples will be taught in SAS.
Prerequisites: PH 711 (P) and PH712 (P), or consent of instructor
Course Learning Outcome: By the end of the course, students will be able to
1. Select the appropriate analysis method for longitudinal data. (MPH #6, Biost #2, #3, #4,
#5)
2. Analyze longitudinal data and make correct inferences to answer scientific questions
using statistical software. (MPH #8, Biost #6)
3. Communicate with public health collaborators for their model selection and
interpretation of results. (MPH #11, Biost #1, #8, #10, #11)
4. Critique longitudinal data analysis methods used in published public health literature.
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(MPH #8, Biost #8)
Class Format: Lecture
Credit Hours: This course counts for 3 graduate credits. Investment of time outside of class will
vary student-by-student. The requirements for this class will require no less than 48 hours per
credit hour or no less than 144 hours of time in accordance with UWM’s Credit Hour Policy. The
workload is an estimate and that students are assessed on their performance, not on the time
put into the course.
Required and recommended readings:
Required: Fitzmaurice, G.M., Laird, N.M. and Ware, J.H. (2011) Applied longitudinal analysis, 2nd
edition. New York: Wiley.
Optional: Diggle, P.J., Heagerty, P., Liang, K.Y. and Zeger, S.L. (2002) Analysis of longitudinal
data, 2nd edition. Oxford University Press
Optional: SAS/STAT(R) 9.3 User’s Guide.
http://support.sas.com/documentation/cdl/en/statug/63962/HTML/default/viewer.htm#titlep
age.htm
Course Requirements:
To meet course objective, students are expected to:
1. Complete reading assignments and attend class with good preparation;
2. Complete homework assignments in a timely manner;
3. Complete midterm and final exams;
4. Complete the course project.
Attendance:
Attendance is required. From the 3rd absence, 1% will be deducted from total course score for
each absence.
Classroom conduct:
All students will be expected to conduct themselves in a professional and collegial manner at all
times.
Assignments:
Homework: A total of 12 homework assignments will be distributed. Homework assignments
will be distributed on Fridays and due in one week. Each task will be graded on a scale of 0-10.
The lowest two will be dropped. The purposes of these assignments are to help students
understand when to select each model, how to implement and interpret each model and how
to critique an incorrectly used method. The assignment will include short-answer questions
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based on supplement reading materials and small data analysis projects. The homework aims to
prepare students for the exams.
Course project: A data analysis project with several specific scientific questions will be assigned
to the student early in the semester. The students are expected to select the appropriate
analysis method and implement it using statistical software. Then the students shall write a
data analysis report to answer the scientific questions using output from statistical software.
The report shall be written in a form that a non-statistician collaborator can understand. In the
report, the student shall write clearly the method they use and the interpretation of their
result.
Exams: Both mid-term and final exams will consist of a series of multiple-choice and short
answer questions. The exam aims to evaluate students’ knowledge of different models, and
ability to select and interpret the model correctly. The exams’ form will be in-class closed book
with one double sided A-4 cheat sheet allowable. These exams aim to test whether the student
grasps the materials covered in class.
Late homework/project: late assignment submission will be accepted with 10% point deducted
per each late day (i.e. you will not obtain score if you submit 10 days after due date).
Grading:
The weights assigned to each of assignments above will be
MPH Program
% of Grade Competencies
Assignment
Due Date
Homework
Midterm Exam
Fridays (See outline)
March 18th
30 #6
15 #6
Course Project
Final Exam
April 22nd
May 12th
30 #6, #8, #11
25 #6
For this course, grades will be based on the following scale:
Percent
94 – 100%
90 – 93%
87 – 89%
84 – 86%
80 – 83%
77 – 79%
74 – 76%
70 – 73%
67 – 69%
Letter Grade
A
AB+
B
BC+
C
CD+
3
Track
Competencies
#2, #4, #8, #11
#2, #4, #5, #8
#1, #2, #3, #4,
#5, #6, #10, #11
#2, #4, #5, #8
64 – 66%
60 – 63%
< OR = 59%
D
DF
Course Evaluations:
The Joseph J. Zilber School of Public Health administers end of semester course evaluations.
Students enrolled in this class will receive an evaluation via PantherMail during the last full
week of the semester, and they must complete it before the last day of final exams. If you do
not use your PantherMail, then please forward all messages to your primary email account, so
you do not miss this correspondence.
General Information:
In the event of disruption of normal classroom activities due to an outbreak, or any other public
health emergency, the format for this course may be modified to enable completion of the
course. In that event, you will be provided an addendum to this syllabus that will supersede this
version.
Incomplete Grade: An "Incomplete" grade will be given only for a major reason that occurs at
the end of the semester and only if the bulk of the course work is complete. The student must
make arrangements with me to complete the course work by a designated time.
Contesting a grade: Students are expected to contact the instructor within 2 weeks of receiving
a grade on any assignment if the student feels she/he was graded unfairly.
Comprehensive information on UWM policy: Specific points are mentioned below.
The policy can be found at http://www.uwm.edu/Dept/SecU/SyllabusLinks.pdf.
Accommodation for Religious Observance: Students will be allowed to complete examinations
and other requirements in advance of religious observance given that the student informs the
instructor at the beginning of the semester or no later than 3 weeks prior to absences related
to religious observance.
Drop /Withdrawal/Repeat Policies: A student may drop a full-term course(s) through the end
of the eighth week of classes.
Special Needs: Students in need of special accommodations in order to meet course
requirements are expected to contact the instructor as soon as possible to make arrangements.
Cancellation of Class: If the canceled class is an exam date, the exam will be held on the next
class day. If weather conditions warrant the cancellation of class, the UWM home page, the
radio or TV will announce the closing. Also check UWM email.
Academic Misconduct Policy: Academic misconduct is an act in which a student seeks to claim
credit for the work or efforts of another without authorization or citation, uses unauthorized
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materials or fabricated data in any academic exercise, forges or falsifies academic documents or
records, intentionally impedes or damages the academic work of others, engages in conduct
aimed at making false representation of a student's academic performance, or assists other
students in any of these.
Prohibited conduct includes cheating on an examination; collaborating with others in work to
be presented, contrary to stated rules of the course; submitting a paper or assignment as one's
own work when a part or all of the paper or assignment is the work of another; submitting a
paper or assignment that contains ideas or research of others without appropriately identifying
the sources of those ideas; stealing examinations or course materials; submitting, if contrary to
the rules of a course, work previously presented in another course; tampering with the
laboratory experiment or computer program of another student; knowingly and intentionally
assisting another student in any of the above, including assistance in an arrangement whereby
any work, classroom performance, examination or other activity is submitted or performed by a
person other than the student under whose name the work is submitted or performed.
In fairness to all students and to promote academic integrity, the Instructor of this course
accepts responsibility to deal effectively with any instance of academic dishonesty should it
occur. Students who violate academic standards as set forth in UWS Chapter 14 and UWM
Faculty Document 1686 (http://www4.uwm.edu/acad_aff/policy/academicmisconduct.cfm) will
be confronted and must accept the consequences and sanctions levied against them for their
actions.
Plagiarism and Cheating: (Student Handbook – pgs. 154-155)
Dishonesty, including but not limited to cheating, plagiarism, or knowingly supplying false
information or deceiving the school and its officials is a violation of the student conduct policy.
Any student who is found to have violated this policy is subject to disciplinary sanctions up to
and including suspension or permanent dismissal. Please be aware that plagiarism is presenting
another’s ideas as one’s own and includes paraphrasing as well as copying without proper
citations or quotation marks.
What is copyright?
Copyright is a form of protection provided by the laws of the United States (title 17, U.S. Code)
to the authors of “original works of authorship,” including literary, dramatic, musical, artistic,
and certain other intellectual works. This protection is available to both published and
unpublished works. Section 106 of the 1976 Copyright Act generally gives the owner of
copyright the exclusive right to do and to authorize others to use their materials. You must get
permission to use copyrighted original works of authorship if you plan to make your project
available to the public in any way. For more on gaining permission, see:
http://www4.uwm.edu/ltc/copyright/getting-permission.cfm
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Course Outline
FLW: Fitzmaurice, G.M., Laird, N.M. and Ware, J.H. (2011) Applied longitudinal analysis, 2nd
edition. New York: Wiley.
DHLZ: Diggle, P.J., Heagerty, P., Liang, K.Y. and Zeger, S.L. (2002) Analysis of longitudinal data,
2nd edition. Oxford University Press
Weeks
Week 1
1/29
Description
Introduction;
FLW: Chap 1.1-1.4, Longitudinal and clustered data
FLW: Chap 2.1-2.5, Longitudinal data: basic concepts
Week 2
2/5
Overview of linear models; HW1 due
FLW: Chap 3.1-3.6, Overview of linear models for longitudinal
data
Week 3
2/12
Maximum likelihood and restricted maximum likelihood
estimator; HW2 due
FLW: Chap 4.1-4.5, Estimation and statistical inference
Week 4
2/19
Week 5
2/26
Modeling the mean; HW3 due
FLW: Chap 5.1-5.9, Modeling the mean: analyzing response
profiles
FLW: Chap 6.1-6.6, Modeling the mean: parametric curves
Modeling the covariance; HW4 due
FLW: Chap 7.1-7.8, Modeling the covariance
Week 6
3/4
Linear mixed effect models; HW5 due
FLW: Chap 8.1-8.9, Linear mixed effect models
Week 7
3/11
Comparison between fixed effect and random effect models;
Model checking; HW6 due
FLW: Chap 9.1-9.8, Fixed effects versus random effects models
FLW: Chap 10.1-10.7, Residual analysis and diagnostics
Review of generalized linear models; Mid-term Exam
FLW: Chap 11.1-11.8, Review of generalized linear models
Week 8
3/18
Week 9
3/25
Marginal model; Generalized estimating equations; HW7 due
FLW: Chap 12.1-12.4, Marginal models: introduction and review
FLW: Chap 13.1-13.6, Marginal models: generalized estimating
equations (GEE)
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DHLZ: Chap 8.1-8.4, Marginal Models
Week 10
4/1
Week 11
4/8
Generalized linear mixed effects models; HW8 due
FLW: Chap 14.1-14.8, Generalized linear mixed effects models
FLW: Chap 15.1-15.7, Generalized linear mixed effects models:
approximate methods of estimation
DHLZ: Chap 9.1-9.4, Random effects models
Comparison between marginal and mixed effect model;
Overview of missing data; HW9 due
FLW: Chap 16.1-16.6, Contrasting marginal and mixed effects
models
FLW: Chap 17.1-17.6, Missing data and dropout: overview of
concepts and methods
DHLZ: Chap 13.1-13.4, Missing values in longitudinal data
Week 12
4/15
Multiple imputation and weighting method; modeling drop-out
process; HW10 due
FLW: Chap 18.1-18.7, Missing data and dropout: multiple
imputation and weighting methods
DHLZ: Chap 13.5-13.8, Missing values in longitudinal data
Week 13
4/22
Transition models; Course project due
DHLZ: Chap 10.1-10.4, Transition models
Week 14
4/29
Sample size; Power; Design; HW11 due
FLW: Chap 20.1-20.6, Sample size and power
FLW: 21.1-21.6, Repeated measures and related designs
Week 15
5/6
Multilevel models; Time dependent covariate; HW12 due
FLW: Chap 22.1-22.5, Multi-level models
DHLZ: Chap 12.1-12.4, Time-dependent covariates
Appendix A: Competency Sets Addressed in this Course
MPH Core Competency:
#6 Utilize appropriate quantitative and/or qualitative methods in public health practice and
research.
#8 Collect, synthesize and critically analyze information and data to identify and address, and
inform public health issues and interventions.
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#11 Communicate effectively about public health issues with diverse audiences using a variety
of strategies and modalities.
Biostatistics Track Competency:
#1 Function as a collaborator with community partners on public health projects and in
developing recommendations for appropriate study designs that advance social justice and
population health.
#2 Translate research objectives into testable hypotheses.
#3 Differentiate between quantitative problems that can be addressed with routine methods
and those requiring input from a doctoral-level biostatistician.
#4 Demonstrate a broad knowledge and understanding of statistical techniques used in public
health studies and health-related scientific investigations.
#5 Identify and apply a variety of appropriate statistical methods for developing inferences
about public-health-related questions.
#6 Demonstrate basic programming skills in multiple statistical software packages and data
management and integration techniques for public health and big data projects.
#8 Interpret and critique statistical analyses in publications for public health professionals.
#10 Demonstrate effective written and oral communication skills when reporting statistical
results to different audiences of public health professionals, policy makers and community
partners.
#11 Formulate and produce graphical displays of quantitative information (e.g., scatter plots,
box plots and line graphs) that effectively communicate analytic findings.
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