PH711Intermediate Biostatistics

Spring 2018

Syllabus

Instructor:Cheng Zheng, PhD

Phone: (414)227-3015

Email:

Office: Zilber School of Public Health building room 378

Office Hours: By appointment

**Email is the preferred means of communication with me outside of class. Please

include “PH711” in the subject line**

Location:All lectures will be held in the Zilber School of Public Health building, room 110

Time:Monday and Wednesday 8:00am-9:15am

Office Hour:Wednesday 12:00pm-1:00pm or by appointment

Course Title:Intermediate Biostatistics

Course Description:

Introduce the modern multivariable statistical analysis which is based on the concept of generalized linear models. Topics include linear regression, logistic regression, Poisson regression, and other topics including longitudinal analysis and mixed models.Other than 3 hours of in-class learning, students are expected to spend at least 6 hours of out-of-class study and thehomework assignment each week to achieve the learning goals.

Credit hours:3 credits

Prerequisites:PH 702 or consent of instructor

Context:

In keeping with the objective of the MPH program, all MPH students must develop skills in basic public health concepts and demonstrate the application of these concepts. Students receive graduate level training in the five major areas of public health as determined by the accrediting body of Schools of Public Health, the Council on Education for Public Health (CEPH.) Biostatistics is one of the five core disciplines of public health.

Course Objectives:

This course is designed to provide students with a fundamental understanding of the theoryand application of the generalized linear model (GLM). At the end of the course, students are expected to be able to derive basic regression equations, understand and verify the assumptions of all models, performappropriate transformations and diagnostics, and interpret and explain the application of the GLM usingreal data. Furthermore, student will be able to utilize rigorous study designs, methods, and statistical analyses to examine research questions related to public health.

Competencies Addressed:

Upon completion of this course, the student will be able to:

  1. Translate research objectives into testable hypotheses.
  2. Demonstrate a broad knowledge and understanding of statistical techniques used in public health studies and health related scientific investigations.
  3. Identify and apply a variety of appropriate statistical methods for developing inferences about public health related questions.
  4. Demonstrate basic programming skills in multiple statistical software packages and data management and integration techniques for public health and big data projects.
  5. Demonstrate effective written and oral communication skills when reporting statistical results to different audiences of public health professionals, policy makers, and community partners.
  6. Formulate and produce graphical displays of quantitative information that effectively communicate analytic findings.

Course Requirements:

To meet course objectives, students will:

  1. Complete required readings;
  2. Constructively participate in class discussions through contributions derived from selected readings, as well as discussions related to assignments;
  3. All assignments must be individually performed by the students submitting them. Collaborative work on graded assignments is not allowed unless specifically stated by the instructor for a particular assignment. Work must be turned in on time unless special permission is granted by the instructor due to truly extenuating circumstances. Requests for time extensions will be considered on a case-by-case basis.

Attendance Requirements and Policies:

Attendance is required. From the 3rd absence, 1% will be deducted from total course score for each absence.

Assignments: will relate to readings, lectures, and class discussions. Some will be completed in class and others will be take home assignments.The purpose of assignments is to provide students an opportunity to familiarize themselves with the theory and application of the GLMThe assignments will help students learn to derive basic regression equations, verify the validity of model assumptions, perform diagnostics test, and choose the appropriate statistical models and analytic techniques to address research questions related to public health. Students are encouraged to work together on homework assignments to understand the concepts in the problems; however, it is expected that each student will turn in an assignment that reflects their own independent work. Homework assignments are expected to be turned in beforeclass (on Wednesday usually).

Evaluation: The final grade will be based on the weighted average of scores of the homework, two midterm exams, and the final project. Specifically, the following weights will be used: (1) Homework:30%. Homework projects will be assigned on weekly basis. Some homework will contain bonus problems and you can earn additional points. (2) Two midterm Exams: 40% (20% for each). The first midterm exam will be placed onFeb28th, 2018. The second midterm exam will be placed on April 18th, 2018(3) Final Project: 30%. The final project will be dueon May 9th of 2018.

Grading Scheme:

Assignment / % of Grade / MPH Program Competencies / Track Competencies
Homework / 30% / #6, #8 / Biostat #2,#4,#6,#10,#11
1stMidterm exam (in-class) / 20% / #6, #8 / Biostat #2, #4, #5, #6, #10, #11
2ndMidterm exam (in-class) / 20% / #6, #8 / Biostat #2, #4, #5, #6,#10,#11
Final Project / 30% / #6, #8 / Biostat #4, #5,#6,#10,#11

GradingFor this course, grades will be based on the following scale:

Percent / Letter Grade
94 – 100% / A
90 – 93% / A-
87 – 89% / B+
84 – 86% / B
80 – 83% / B-
77 – 79% / C+
74 – 76% / C
70 – 73% / C-
67 – 69% / D+
64 – 66% / D
60 – 63% / D-
< OR = 59% / F

Reference Texts:

  1. Kleinbaum DG, Kupper LL,NizamA, Rosenberg ES (2014). Applied Regression Analysis and Other Multivariable Methods, 5th Edition.Cengage Learning (required)
  2. Annette J. Dobson. (2010) An Introduction to Generalized Linear Models, 3rd Edition. Chapman & Hall CRC (optional)
  3. Hosmer DW, Lemeshow S, and Sturdivant RX (2013). Applied Logistic Regression. 3rd Edition. Wiley (optional)

Week / Topics / Readings
1 / Basic Statistics: A Review: normal, t, F, Chi-square distribution; Central Limit Theorem; statistical inference; confidence interval; hypothesis testing. / 1Chapter 2, 3
p.7-34
2 / Straight-line and multiple regression analysis: linear regression models; assumptions; estimating the regression parameters; ANOVA table / 1Chapter 4, 5, 6, 7, 8
p.35-164
3 / Statistical inference in multiple Regression: testing hypotheses: test for significant overall regression, partial F test, multiple partial F test / 1Chapter 9
p.165-198
4 / Correlations: correlation matrix; multiple correlation coefficient, partial correlation coefficient, and multiple partial correlation;
Confounding and interaction in regression / 1Chapter 10
p.199-255
5 / Confounding and dummy variables in regression: confounding and interaction issues; rule for defining dummy variables, methods for comparing two straight lines, testing strategies and interpretation / 1Chapter 11, 12
p.226-307
6 / Analysis of Covariance for adjusting continuous data: adjustment problem, assumption of parallelism, ANACOVA
1st mid-term / 1Chapter 13
p.308-338
7 / Regression Diagnostics and Polynomial Regression: residual analysis, treating outliers, collinearity, scaling problems, treating collinearity and scaling problems / 1Chapter 14, 15
p.339-437
8 / Selecting the best regression equation: specifying the maximum model, criteria for selecting a model, strategy for selecting variables, cross-validation. / 1Chapter 16
p.438-480
9 / Spring Recess
10 / Method of maximum likelihood and logistic regression analysis: Statistical inference using maximum likelihood; Logistic model, estimating the odds ratio, interpretation of the coefficients,theoretical considerations / 1Chapter 21, 22
p.661-713
11 / Polytomous and ordinal logistic regression / 1Chapter 23,
p.714-742
12 / Poisson regression analysis and log-linear model: Possion distribution, hypothesis testing and goodness of fit / 1Chapter 24
p.743-780
13 / 2nd mid-term
14 / Analysis of correlated data: weighted least square, general linear model approach, fixed and random effects / 1Chapter 25
p.781-824
15 / One-way and two-way ANOVA / 1Chapter 17,18,19,20
p.516-659
16 / Final Project

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Attendance Requirements and Policies:Since considerable learning takes place through sharing of ideas with colleagues and practice of statistical computing techniques, attendance at every class session is mandatory. From the 3rd absence, 1% will be deducted from total course score for each absence.

Late Assignments:Homework assignments are expected to be turned in on time. Late assignment submission will be accepted with 20% point deducted per each late day (i.e. you will not obtain score if you submit 5 days after due date). .

Format:Lecture.

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.

Grade of “Incomplete”: Students are expected to complete all course work by the designated deadlines during the semester. Grades of Incomplete will only be assigned when students are unable to complete the requisite number of research hours and all assignments.

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.

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.

Comprehensive information on UWM policy: Specific points are mentioned below.

The policy can be found at

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.

Academic Misconduct Policy: In accordance with the Board of Regents, faculty/staff and students of the UW-System, we believe that academic honesty and integrity are fundamental to the mission of higher education. In fairness to all students and to promote academic integrity, the instructors of this course accept 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 will be confronted and must accept the consequences and sanctions levied against them for their actions. The most common forms of academic dishonesty are cheating and plagiarism. Within the context of research endeavors, academic misconduct includes falsification of data.

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