This course provides an introduction to longitudinal and clustered data. Topics include the basic concepts of longitudinal data, linear models for longitudinal data, generalized linear models and salient features, generalized estimating equations, generalized linear mixed effects models, missing data and dropouts, sample size and power, repeated measures, and multilevel linear models

Rochani, Haresh

English

Georgia Southern University: Digital Commons@Georgia Southern

Georgia Southern University Digital Commons@Georgia Southern Public Health Syllabi Public Health, Jiann-Ping Hsu College of Spring 2018 BIOS 9333A – Applied Longitudinal Data Analysis Haresh Rochani Georgia Southern University, Jiann-Ping Hsu College of Public Health, hrochani@georgiasouthern.edu Follow this and additional works at: https://digitalcommons.georgiasouthern.edu/coph-syllabi  Part of the Public Health Commons Recommended Citation Rochani, Haresh, "BIOS 9333A – Applied Longitudinal Data Analysis" (2018). Public Health Syllabi. 220. https://digitalcommons.georgiasouthern.edu/coph-syllabi/220 This other is brought to you for free and open access by the Public Health, Jiann-Ping Hsu College of at Digital Commons@Georgia Southern. It has been accepted for inclusion in Public Health Syllabi by an authorized administrator of Digital Commons@Georgia Southern. For more information, please contact digitalcommons@georgiasouthern.edu. Georgia Southern University Jiann-Ping Hsu College of Public Health BIOS 9333A– Applied Longitudinal Data Analysis Spring 2018               Prerequisites: None  Catalog Description:  This course provides an introduction to longitudinal and clustered data. Topics include the basic concepts of longitudinal data, linear models for longitudinal data, generalized linear models and salient features, generalized estimating equations, generalized linear mixed effects models, missing data and dropouts, sample size and power, repeated measures, and multilevel linear models.  3 hours.    Required Textbook:  Fitzmaurice, G. M., Laird, N. M., Ware, J. H., Laird, N. M., & Ware, J. H. (2011). Applied longitudinal analysis. Second edition, Hoboken: Wiley. ISBN: 9780470380277 Reference Texts:  Hedeker, D. R., & Gibbons, R. D. (2006). Longitudinal data     analysis. Hoboken, N.J: Wiley-Interscience.                                Instructor: Dr. Haresh Rochani Office: Hendricks Hall 1006  Phone: 912.478.1011 Email: hrochani@georgiasouthern.edu   Website: Folio Time: Monday & Wednesday, 2:30 – 3:45 PM Location: Education Bldg 2207 Office Hours:  Monday   10 AM to 12 AM                           Tuesday 10:45 AM to 1 PM                            2  DrPH  Biostatistics Student Learning Outcomes Cross-cutting 1. Demonstrate their readiness to work with communities to address public health problems. 2. Select and apply theoretically based interventions to address public health problems. 3. Apply appropriate research methods to address community health problems, particularly among rural and underserved populations. Biostatistics Concentration 1. Construct a public health and biomedical research question from ideas, conditions, and events that exist in a rural and urban community, region, state, and nation using critical thinking skills. 2. Demonstrate required skills for translating public health practice objectives to the appropriate biostatistical framework for analysis and interpretation of results. 3. Illustrate sufficient substantive knowledge of advanced biostatistical methods such as multiple regression, logistic regression, survival analysis, longitudinal data analysis, and Bayesian and adaptive methods to interact with biostatisticians and related public health researchers in a meaningful and productive fashion. 4. Communicate biostatistical principles and concepts to lay and professional audiences through both oral and written communication. Performance-Based Objectives   At the completion of this course the student will be able to:  1. Identify ANOVA Models I, II and III, crossed factors and nested factors  2. Perform a repeated measures analysis as a split plot design Perform a multivariate repeated measures analysis. 3. Perform a repeated measure using the mixed model approach. 4. Be able to select the “best” variance-covariance structure when performing a repeated measures analysis via mixed models. Be able to perform a random intercept analysis. 5. Be able to perform a random intercept and trend analysis.   3 6. Be able to perform curvilinear trend models and orthogonal polynomials.  7. Be able to identify and assess various covariance patterns. 8. Be able to assess correlation between errors and apply the appropriate ARMA adjustments. 9. Be able to apply GEE models in the analysis of longitudinal data analysis. 10.  Be able to perform a mixed-effects logistic regression.  11. Be able to perform a mixed-effects proportional odds model.  12. Be able to perform a mixed-effects multinomial regression analysis, including competing risks. 13. Be able to perform a mixed-effect Poisson Regression and ZIP regression.  14. Determine the different types of missing mechanisms.  15. Identify models for non- ignorable missingness.    Overview of the content to be covered during the semester:    Week Topic Readings  Assignment 1  Introduction to Longitudinal and Clustered Data Class notes, Chapter 1 and Chapter 2 TBA 2-3 Overview, Estimation & Statistical inference for linear models for Longitudinal data Class notes, Chapter 3 and Chapter 4 TBA 4-5 Modeling the mean Class notes , Chapter 5 and Chapter 6 TBA 6 Modeling the Covariance Class notes and Chapter 7 TBA 7 Linear Mixed Effects Models Chapter 8 TBA 8 Fixed Effects versus Random Effects Models. Chapter 9 TBA 9-10 Marginal Models :Generalized Estimating Equations (GEE) Models  Chapter 11 , Chapter 12 and Chapter 13  TBA 11-12 Generalized Linear Mixed Chapter 14 and Chapter   4 Effects Models 15 TBA 13 Contrasting Marginal and Mixed effects Models Chapter 16 TBA 14-15 Missing Data and Dropout : Overview of Concepts and Methods Chapter 16 and Chapter 17 TBA 16 Multiple Imputation and Weighting Methods in Longitudinal data Chapter 18 TBA Samples of your work may be reproduced for search purposes and/or inclusion in the professor’s teaching portfolio.  You have the right to review anything selected for use, and subsequently ask for its removal.   Problems will be due within one week of completion of topic  Instructional Methods: Class meetings will be a combination of lecture, class discussion, and computer software demonstration.  Written homework assignments and examinations constitute the basis of student evaluation.     Exam Schedule and Final Exam: Final Exam:        April 30th 1, 2018  Grading: Weighting of assignments for purposes of grading will be as follows:  Assignments ……….…………………. 70%   (assesses learning objectives: 1-19, individually * )  Presentations   ………………………………… 10% Final Exam       ……….…………………. 20%   (assesses learning objectives: 1-19, integrated * )                                                  ____  Total Possible               100%  *  Integrated refers to assessing all or some of the 14 learning objectives simultaneously, while individually refers to assessing the learning objectives as we cover them.  The following point scale will be utilized in grading:  90% - 100% A  5 80% -   90% B 70% -   80% C 60% -   70% D For calculation of your final grade, all grades above will be included. Your grades will not be posted.  All exams and assignments will be graded and returned promptly so that students may accurately calculate their grades at any point in time during the semester. There are times when extraordinary circumstances occur (e.g., serious illness, death in the family, etc.).  In such circumstances, and/or if you need additional time to satisfactorily complete any course requirement, please consult with the instructor within a reasonable amount of time.     Academic Integrity: Students are expected to follow guidelines outlined in the Student Conduct Code 2014-15 policy regarding academic dishonesty.  Any student found in violation of academic honesty will receive a grade of ‘F’ for the course.  It is the student’s responsibility to familiarize him/herself with the student policies and expectations set forth in the GSU Student Conduct Code 2009-10 (http://students.georgiasouthern.edu/judicial/).    Attendance Policy: Due to the nature and structure of this course, class attendance is of utmost importance.  You are responsible for any material covered or distributed in class, including any announcements made in class, whether or not you are present.  Furthermore, federal regulations require attendance be verified prior to distribution of financial aid allotments.  Attendance will not be recorded after this initial period.  Class Participation & Etiquette: Attendance, attention, and participation are expected for each class!  I believe it is important to foster student-teacher and student-student interactions within class, so you will discover that I will ask questions to you throughout the class.  Although class participation is not a formal part of your course grade, I will use it as a factor if your final grade is on the border between two letter grades.  Otherwise, I do not round final numerical grades to the nearest letter.    On a final note of etiquette, please turn off all cell phones during class, since ring tones are disruptive to others.  Important Dates to Remember:  http://calendar.georgiasouthern.edu/?c=35  Disclaimer: The contents of this syllabus are as complete and accurate as possible.  The instructor reserves the right to make any changes necessary to the syllabus and course  6 material.  The instructor will make every effort to inform you of changes as they occur.  It is the responsibility of the student to know what changes have been made in order to successfully complete the requirements of the course.  Plagiarism "According to the Academic Dishonesty Policy of GSU, Plagiarism includes (but is not limited to): A. Directly quoting the words of others without using quotation marks or indented format to identify them. B. Using published or unpublished sources of information without identifying them. C. Paraphrasing material or ideas without identifying the source. D. Unacknowledged use of materials prepared by another person or agency engaged in the selling of term papers or other academic material.  If you are accused of plagarism by a JPHCOPH, the following policy, as per the Judicial Affairs website ( http://students.georgiasouthern.edu/judicial/faculty.htm ) will be enforced:  PROCEDURES FOR ADJUDICATING ACADEMIC DISHONESTY CASES  First Offense - In Violation Plea 1.If the professor and the Dean of Students agree that the evidence is sufficient to warrant a charge of academic dishonesty, the professor should contact the Office of Judicial Affairs to determine if this is a first violation of academic dishonesty.  The incident will be reported via the following website:  http://students.georgiasouthern.edu/judicial/faculty.htm 2. If it is a first violation, the professor should talk with the student about the violation. If the student accepts responsibility in writing and the professor decides to adjudicate the case, the following procedures will be followed: a.The student will be placed on disciplinary probation for a minimum of one semester by the Office of Judicial Affairs. b.The student will be subject to any academic sanctions imposed by the professor (from receiving a 0 on the assignment to receiving a failing grade in the class). c.A copy of all the material involved in the case (Academic Dishonesty Report Form and the Request For Instructor to Adjudicate Form) and a brief statement from the professor concerning the facts of the case and the course syllabus should be mailed to the Office of Judicial Affairs for inclusion in the student’s discipline record.  First Offense - Not In Violation Plea (student does not admit the violation) If the professor and the Dean of Students agree that the evidence is sufficient to warrant a charge of academic dishonesty, the professor should contact the Office of Judicial Affairs to determine if this is the first or second violation of academic dishonesty. The student will be charged with academic dishonesty and the University Judicial Board or a University Hearing Officer would hear the case. If the student is found responsible, the following penalty will normally be imposed:  7 a.The student will be placed on Disciplinary Probation for a minimum of one semester by the Office of Judicial Affairs. b.The student will be subject to any academic sanctions imposed by the professor.  Second Violation of Academic Dishonesty If the professor and the Dean of Students agree that the evidence is sufficient to warrant a charge of academic dishonesty, and if it is determined this is the second violation, the student will be charged with academic dishonesty and the University Judicial Board or a University Hearing Officer would hear the case.  If the student is found responsible, the following penalty will normally be imposed: a.Suspension for a minimum of one semester or expulsion. b.The student will be subject to any academic sanctions imposed by the professor.    NOT RESPONSIBLE FINDING When a student is found not responsible of academic dishonesty, the work in question (assignment, paper, test, etc.) would be forwarded to the Department Chair. It is the responsibility of the  Department Chair to ensure that the work is evaluated by a faculty member other than the individual who brought the charge and, if necessary, submit a final grade to the Registrar. For the protection of the faculty member and the student, the work in question should not be referred back to the faculty member who charged the student with academic dishonesty. In the case of a Department Chair bringing charges against a student, an administrator at the Dean’s level will ensure that the student’s work is evaluated in an appropriate manner.  CONFIDENTIALITY In accordance with provisions of the Family Educational Rights and Privacy Act of 1974 and the Georgia Open Records Act, any information related to a violation of academic dishonesty or the outcome of a judicial hearing regarding academic dishonesty, is prohibited and must be treated as confidential by members of the faculty."  

BIOS 9333A – Applied Longitudinal Data Analysis

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BIOS 9333A – Applied Longitudinal Data Analysis

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