P8483:

Application of Epidemiologic Research Methods

Spring 2025 Syllabus

COURSE OBJECTIVES

1. Think logically!

2. Use SAS to walk through the steps of an epidemiologic analysis from data entry through regression analysis.

3. Operationalize conceptual epidemiologic and biostatistical concepts covered in other classes through application with real data sets.

4. Learn to summarize your research findings with succinct scientific writing.

5. Develop skills in “prompt engineering” to use generative AI to assist with writing computer code.

The material builds on concepts introduced in Quantitative Foundations in Public Health and is intended to complement and serve as a bridge to the methods presented in more advanced epidemiology courses.

WEEKLY STRUCTURE AND EXPECTATIONS

This class is a hybrid.

Recorded lecture material is supplemented by weekly in-person small group exercises.

In-person attendance is expected.

Most weeks following Week 1 will have the following structure.

Tuesday 530-650 pm. Synchronous session.

· First 15 minutes: Questions on asynchronous material from last week

· Second 50 minutes: small group breakout sessions focusing on completing specific in-class assignments

· Last 15 minutes: review of small group exercise as a class

Tuesday following Synchronous session:

· Asynchronous material for the following week is released

o Slides with embedded recorded audio and video

o Reading assignments

o Homework assignment

Wednesday am – Monday pm

· Attend office hours as needed

· Watch asynchronous material

· Do readings

· Work on homework

All homework assignments will be due at 11:59 pm on the Monday prior to the synchronous session, with a no-penalty grace period until 7:59 am on Tuesday morning. No extensions will be provided. No exceptions.

PREREQUISITES

· Prior completion of the Core, P6400, or equivalent

· Registered for the course on SAS On Demand

OPTIONAL

· This year I am exploring ways to integrate the R statistical program into this class using generative AI. There will be optional materials using R available. If interested, please download R and R Studio.

SKILLS TO LEARN

The primary objective of this course is to provide you with the tools necessary to import, clean, error-check, operationalize, analyze, and disseminate data from epidemiologic research studies. In this class we will be using the SAS statistical software package, but the logical processes this course develops will be useful across any statistical package.

Generative AI tools such as ChatGPT are excellent resources at helping you to write statistical programming commands for epidemiologic analyses, and we will explore how best to use these tools in this class as well.

 By the end of the course, you should be able to:

· Understand and implement the steps involved in data collection, management, data quality assurance, operationalization, descriptive analysis, and multivariable regression analyses using SAS

o Read raw data from a variety of formats into SAS

o Peruse, manipulate and clean data sets through printing, sorting, merging, and the use of conditional logical expressions

o Apply simple statistical and graphical procedures for the descriptive analysis of normally distributed data

o Conduct correlation, linear, and logistic regression in SAS and interpret SAS output for these analytical methods

· Understand the concepts of statistical model building

· Understand the difference between statistical model building and multivariable analysis for causal inference

· Understand the purpose of indicator variables (“dummy” variables), how to create these, and how to interpret output for these variables

· Conduct multivariable data analyses in SAS

· Understand the concept of confounding and how to use standard methods to remove confounding

· Develop skills in succinct summarization of findings from an epidemiological analysis through writing a scientific abstract.

ASSESSMENT AND GRADING POLICY

Assessment for this course is based on homework assignments, a mid-term exam, a final project, and weekly in-class laboratory assignments. The contribution of each grading assessment toward the final grade is as follows:

Assignments: 45%

In-class group exercises: 8%

In class check-ins: 2%

Midterm exam: 25%

Final project: 20%

Letter grades will be assigned by the instructor based on the following general rubric.

No rounding up.

A+ 99-100% Highly Exceptional Achievement

A 94-98% Excellent. Outstanding Achievement

A- 90-93% Excellent, close to outstanding

B+ 88-89% Very good. Solid achievement expected of most graduate students

B 84-87% Good. Acceptable achievement

B- 80-83% Acceptable achievement, but below what is generally expected

C+    78-79%

C 74-78%

C-    70-74%

Midterm Exam: The take-home midterm will take place during the approximate mid-point of the semester and will be similar in format to weekly homework assignments. More detail will be provided during the semester.

Homework Assignments: There will be approximately 8 graded homework assignments. Assignments are to be completed and saved in SAS as enhanced editor files (.sas extension or .txt extension). Since most of you will not download SAS onto your computer, saving your SAS editor file as a .txt extension will allow you to actually view it prior to submission.

Your name and Columbia email UNI must be included in all assignment submissions. 

All course assignments will be turned in electronically via CourseWorks. At the end of the semester, the top (n-1) grades will be used in compiling a student’s final grade. There will be no accepting late homework submissions.

In-class check ins

During weekly in-person classes, you will be given an in-person learning check at the beginning of each class. Grades will be based on completion. These will count toward 2% of your grade. To receive full credit you will need to be physically present in class for these. The two lowest scores on these will be excluded from your final grade.

In-class group exercises

The most important part of the Tuesday in-person sessions are the group exercises. You will work with your randomly assigned laboratory group on an assignment. These will count toward 8% of your grade. These will be due after the class and graded for reasonable effort. All lab group members are expected to participate in order to receive credit for the lab assignment. During submission, you will be asked to attest to which members were (1) present in person (2) present remotely or (3) not present. Credit will be given to all members present in person or remotely.

Final Project: The objective of the final group project is to provide students with experience in analyzing data from a large scale data set.  Specific details will be provided later on in the semester.