Syllabus

Graduate Programs in Public Health

GPH 718 – Biostatistics II (Fall 2022)

Credits - 3

Description

This course is a continuation of GPH 716 (Biostatistics). The course will assume familiarity with the basic principles of data collection, one-and two-sample confidence intervals, and hypothesis testing, as well as one-way ANOVA and the fundamentals of simple linear regression. It will focus on a more in-depth look at simple linear regression extending to multiple linear regression. Additionally, it will cover topics in probability, diagnostic and screening tests, nonparametrics, and logistic regression. 

Materials

  • Rosner B. Fundamentals of Biostatistics. 8 ed. Boston, MA: Cengage Learning; 2016
  • SAS OnDemand for Academics 
    • NOTE: Watch the “Intro to SAS” lecture video in Module 1 of the course. This will prepare you to follow later SAS tutorials beginning in Module 3.

Learning Objectives and Outcomes

Course Outcomes

  • Perform estimation and prediction using simple linear regression.
  • Perform logistic regression on binary outcomes.
  • Describe and solve probability using discrete probability rules, with an emphasis on probability calculations for screening tests.
  • Describe nonparametric testing alternatives to classical hypothesis tests for the mean
  • Perform analyses of contingency tables, including estimating measures of association.
  • Describe the role of several predictors in a multiple regression model

Public Health Competencies

  • FC 3: Analyze quantitative and qualitative data using biostatistics, informatics, computer-based programming and software, as appropriate
  • FC 4: Interpret results of data analysis for public health research, policy or practice
  • PC 2: Search databases and critically analyze peer reviewed literature
  • PC 3: Develop strategies for qualitative and quantitative data management.

Assignments

Forum Discussions

Each student is expected to post at least twice each week in response to forum questions on that week’s topic. Because this is an online course, the online discussion portion is an important way to exchange ideas with your classmates. Students will be graded on their participation and effort of their posts. These posts will take time to complete but they are an essential part of this online course and a great way to get to know your colleagues. Please be familiar with the course material (readings/lectures) before posting each week. Full marks will be given to those who ask questions, bring in new data from the literature or other resources, and demonstrate a thorough understanding of the topics for the week.

Weekly Assignments

Each weekly module features one assignment, set up as an untimed quiz with written-response answer fields. These activities require you to complete statistical problem sets, and beginning in Module 3, will require the use of the SAS software (see Module 1 for an Intro to SAS).

Grading Policy

Your grade in this course will be determined by the following criteria:

Grade Breakdown

Assignment / TaskGrade Point Value
Discussions5 x 5 = 25 points
Module 1 Probability Assignment 9 points
Module 2 Screening Tests Assignment 9 points
Module 3 Nonparametrics Assignment9 points
Module 4 Contingency Table Assignment 9 points
Module 5 Linear Regression Assignment9 points
Module 6 Multiple Linear Regression Assignment9 points
Module 7 Logistic Regression Assignment9 points
Module 8 Final Exam12 points
Total100 points

Grade Scale

Grade Points Grade Point Average (GPA)
A 94 – 100% 4.00
A- 90 – 93% 3.75
B+ 87 – 89% 3.50
B 84 – 86% 3.00
B- 80 – 83% 2.75
C+ 77 – 79% 2.50
C 74 – 76% 2.00
C- 70 – 73% 1.75
D 64 – 69% 1.00
F 00 – 63% 0.00

Schedule

Course Calendar

Week 1: Oct 19 – Oct 26
Week 2: Oct 26 – Nov 2
Week 3: Nov 2 – Nov 9
Week 4: Nov 9 – Nov 16
Week 5: Nov 16 – Nov 23
Week 6: Nov 23 – Nov 30
Week 7: Nov 30 – Dec 7
Week 8: Dec 7 – Dec 11

Week 1: Introduction to Probability

Learning Outcomes

  • Understand the three axioms of probability
  • Apply probability rules to solve probability problems

Readings/Videos

  • Read: Rosner: Chapter 3.1 through 3.6 and 4.1 through 4.6; pages 42-55 and 77-85
  • Review: Rosner: Chapters 6, 7, and 8 as needed to re-familiarize with confidence intervals and hypothesis tests.
  • Watch: Lectures

Assignments/Tasks

  • Probability Assignment
  • Discussion 
    • Welcome to GPH-718. Begin by introducing yourself to the class. What is your background in Biostatistics? what are you most excited about? In what ways will this course come in handy for your Public Health Career? 
  • Discussion 
    • A famous problem in probability comes from the game show “Let’s Make a Deal.” In it, a contestant is shown three doors. Behind one door is a new car, and behind the other doors, there is nothing. The contestant is asked to pick one of the three doors. The host then opens up one of the two that was not chosen which always reveals that there is nothing behind that opened door. The contestant is then asked if he or she would like to change their chosen door to the other unopened one.

Week 2: Properties of Screening and Diagnostics

Learning Outcomes

  • Apply concepts of sensitivity and specificity to evaluate diagnostic and screening tests
  • Understand the relationships among positive/negative predictive values, sensitivity/specificity, and false positive/negative

Readings/Videos

  • Read: Rosner: Chapter 3.7 through 3.10; pages 55-64
  • Read: Bibbins-Domingo K, Grossman DC, Curry SJ, et al. Screening for syphilis infection in nonpregnant adults and adolescents: US preventive services task force recommendation statement.
  • Watch: Lectures

Assignments/Tasks

  • Screening/Diagnostic Test Assignment 
  • Discussion
    • The article states that RPR tests for syphilis have a sensitivity of 86% and a specificity of approximately 92%.  Given a syphilis prevalence rate of 5 cases per 100,000 people, can you calculate the predictive value positive and predictive value negative? What does this suggest about the usefulness of screening the general population?

Week 3: Nonparametrics

Learning Outcomes

  • Perform nonparametric equivalents to one- and two-sample parametric tests
  • Analyze the assumptions and limitations of nonparametric tests

Readings/Videos

  • Read: Rosner: Chapter 9; pages 338-365
  • Read: “Clonal Hematopoiesis and Risk of Atherosclerotic Cardiovascular Disease,” New England Journal of Medicine.
  • Watch: Lecture and SAS Tutorial

Assignments/Tasks

  • Nonparametrics Assignment
  • Discussion 
    • In the second paragraph of page 117 in the NEJM article, it says that the Wilcoxon rank sum test was used to determine that the aortic lesion in those with bone marrow from knockout mice was twice as large as the lesions in those not receiving such bone marrow.  Why do you think the Wilcoxon rank sum test was used?  What is the “standard” parametric alternative to this test?  Are there reasons given in the article as to why a nonparametric test was used?  Do you believe the conclusion reached in this case?  Why or why not?

Week 4: Contingency Table Analysis

Learning Outcomes

  • Create and analyze contingency tables
  • Apply exact methods for sparse contingency tables

Readings/Videos

  • Read: Rosner: Chapter 10.1 through 10.4 and 10.6; pages 372-403 and 413-425
  • Read: Rosner: Chapter 13.1 through 13.3; pages 633-647
  • Watch: 2 Lectures and 2  SAS Tutorials

Assignments/Tasks

  • Contingency Table Assignment

Week 5: Inference for Simple Linear Regression

Learning Outcomes

  • Evaluate simple linear regression models
  • Synthesize inferences and predictions in a simple linear regression model setting

Readings/Videos

  • Read: Rosner: Chapter 11.1 through 11.6; pages 457-485
  • Watch: Lecture and SAS Tutorial

Assignments/Tasks

  • Linear Regression Assignment

Week 6: Multiple Regression

Learning Outcomes

  • Incorporate more than one predictor in a linear regression model

Readings/Videos

  • Read: Rosner: Chapter 11.9; pages 502-518
  • Read: “Early versus Later Rhythm Analysis in Patients with Out-of-Hospital Cardiac Arrest,” The New England Journal of Medicine.
  • Watch: Lecture and SAS Tutorial

Assignments/Tasks

  • Multiple Regression Assignment
  • Discussion
    • In the last paragraph on page 789 continuing on page 790, the authors state that they determined the difference in the outcome between patient groups, “adjusted for baseline measurements” through the use of multiple regression.
      • What do they mean by saying this? Does this give you enough information to determine whether the analysis was done appropriately? Explain.
      • What should the writers actually say instead of “adjusted for baseline measurements” to make what they did clearer?

Week 7: Logistic Regression

Learning Outcomes

  • Model binary outcomes in a logistic regression

Readings/Videos

  • Read: Rosner: Chapter 13.7; pages 673-694
  • Read: “Association Between Maternal Use of Folic Acid Supplements and Risk of Autism Spectrum Disorders in Children,” Journal of the American Medical Association.
  • Watch: Lecture and SAS Tutorial

Assignments/Tasks

  • Logistic Regression Assignment
  • Discussion:
    • Initial Post: Read the Statistical Analysis section on page 572. Can you determine what the logistic regression models the authors ran looked like? Do you agree with the procedure they used? Based on the odds ratios given, can you determine if there is a statistically significant association between folic acid supplements in mothers and autistic spectrum disorder development in children?

Week 8:  Topics in Logistic Regression

Learning Outcomes

  • Incorporate more than one predictor in a logistic regression model
  • Evaluate confounding and interaction in logistic regression

Readings/Videos

  • Read: “How to control for confounding effects by statistical analysis.”  Gastroenterol Hepatol Bed Bench.
  • Watch: Lecture

Assignments/Tasks

  • Final Exam

Student Resources

Online Student Support

Your Student Support Specialist is a resource for you. Please don't hesitate to contact them for assistance, including, but not limited to course planning, current problems or issues in a course, technology concerns, or personal emergencies.

Questions? Visit the Student Support Public Health page

UNE Libraries:

UNE Student Academic Success Center

UNE's Student Academic Success Center (SASC) offers a range of free online services to support your academic achievement. Writing support, ESOL support, study strategy and learning style consultations, as well as downloadable resources, are available to all matriculating students. The SASC also offers tutoring for GPH 712 Epidemiology, GPH 716 Biostatistics, GPH 717 Applied Epidemiology, GPH 718 Biostatistics II, and GPH 719 Research Methods. To make an appointment for any of these services, go to une.tutortrac.com. For more information and to view and download writing and studying resources, please visit:

Information Technology Services (ITS)

  • ITS Contact: Toll Free Help Desk 24 hours/7 days per week at 1-877-518-4673

Accommodations

Any student who would like to request, or ask any questions regarding, academic adjustments or accommodations must contact the Student Access Center at (207) 221-4438 or pcstudentaccess@une.edu. Student Access Center staff will evaluate the student's documentation and determine eligibility of accommodation(s) through the Student Access Center registration procedure.

Policies

AMA Writing Style Statement

The American Medical Association Manual (AMA) of Style, 11th edition is the required writing format for this course. Additional support for academic writing and AMA format is provided throughout the coursework as well as at the UNE Portal for Online Students.

Online resources: AMA Style Guide

Turnitin Originality Check and Plagiarism Detection Tool

The College of Professional Studies uses Turnitin to help deter plagiarism and to foster the proper attribution of sources. Turnitin provides comparative reports for submitted assignments that reflect similarities in other written works. This can include, but is not limited to, previously submitted assignments, internet articles, research journals, and academic databases.

Make sure to cite your sources appropriately as well as use your own words in synthesizing information from published literature. Webinars and workshops, included early in your coursework, will help guide best practices in APA citation and academic writing.

You can learn more about Turnitin in the Turnitin Student quick start guide.

Technology Requirements

Please review the technical requirements for UNE Online Graduate Programs: Technical Requirements

Course Evaluation Policy

Course surveys are one of the most important tools that University of New England uses for evaluating the quality of your education, and for providing meaningful feedback to instructors on their teaching. In order to assure that the feedback is both comprehensive and precise, we need to receive it from each student for each course. Evaluation access is distributed via UNE email at the beginning of the last week of the course.

Late Policy

Students are responsible for submitting work by the date indicated in Brightspace.

Quizzes and Tests: Quizzes and tests must be completed by the due date. They will not be accepted after the due date.

Assignments: Unless otherwise specified, assignments will be accepted up to 3 days late; however, there is a 10% grade reduction (from the total points) for the late submission. After three days the assignment will not be accepted.

Discussion posts: If the initial post is submitted late, but still within the discussion board week, there will be a 10% grade reduction from the total discussion grade (e.g., a 3 point discussion will be reduced by 0.3 points). Any posts submitted after the end of the Discussion Board week will not be graded.

Please make every effort ahead of time to contact your instructor and your student support specialist if you are not able to meet an assignment deadline. Arrangements for extenuating circumstances may be considered by faculty.

Student Handbook Online - Policies and Procedures

The policies contained within this document apply to all students in the College of Graduate and Professional Studies. It is each student's responsibility to know the contents of this handbook.

Student Handbook

UNE Course Withdrawal

Please contact your student support specialist if you are considering dropping or withdrawing from a course. The last day to drop for 100% tuition refund is the 2nd day of the course. Financial Aid charges may still apply. Students using Financial Aid should contact the Financial Aid Office prior to withdrawing from a course.

Academic Integrity

The University of New England values academic integrity in all aspects of the educational experience. Academic dishonesty in any form undermines this standard and devalues the original contributions of others. It is the responsibility of all members of the University community to actively uphold the integrity of the academy; failure to act, for any reason, is not acceptable. For information about plagiarism and academic misconduct, please visit https://www.une.edu/studentlife/plagiarism.

Academic dishonesty includes, but is not limited to the following:

  1. Cheating, copying, or the offering or receiving of unauthorized assistance or information.
  2. Fabrication or falsification of data, results, or sources for papers or reports.
  3. Action which destroys or alters the work of another student.
  4. Multiple submissions of the same paper or report for assignments in more than one course without permission of each instructor.
  5. Plagiarism, the appropriation of records, research, materials, ideas, or the language of other persons or writers and the submission of them as one's own.

Charges of academic dishonesty will be reviewed by the Program Director. Penalties for students found responsible for violations may depend upon the seriousness and circumstances of the violation, the degree of premeditation involved, and/or the student’s previous record of violations.  Appeal of a decision may be made to the Dean whose decision will be final.  Student appeals will take place through the grievance process outlined in the student handbook.