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Jerrod  Nelms, Instructor - Time Series Analysis for Healthcare

Jerrod Nelms

Dr. Nelms holds a PhD in Epidemiology from The University of North Carolina at Chapel Hill. He is an expert in study design and execution in the health sciences and is proficient in SAS, R, and SPSS. He is also an experienced instructor, having taught over 25 courses at undergraduate and graduate levels. Dr. Nelms has served since January 2013 as the Owner and Managing Member of Lucyna Health and Safety Solutions providing data science consulting, specializing in epidemiology, informatics, quantitative research, and quality and process improvement across all sectors. His clientele include the federal government, private sector healthcare, hospital associations, the financial sector, and private individuals.

Instructor: Jerrod Nelms

A step-by-step guide to healthcare researchers

  • Detailed guide to time series analysis using SAS.
  • Instructor is an expert in epidemiology, informatics, occupational, environmental health, public health and has worked with leading companies like Quintiles and GlaxoSmithKline.
  • 10+ lectures and 2 guided student exercises.

Duration: 4h 1m

Course Description

This course provides students with a step-by-step guide to time series analysis using the SAS system. We will walk through all of the statistical methodology required to perform a proper time series analysis and will provide students with sample syntax to help them along the way.

What am I going to get from this course?

- State the basic reasons for why data are analyzed in the healthcare setting
- Demonstrate an understanding of the nature of health-related data
- Properly clean and organize data for statistical analysis
- Arrange a data set in time-series format
- Construct a statistical model to analyze time-series data
- Report results in an appropriate format

Prerequisites and Target Audience

What will students need to know or do before starting this course?

Students should have a basic understanding of the principles of data analysis and should be familiar (not expert) in SAS. 

Who should take this course? Who should not?

Health researchers who are familiar with SAS and who want to learn how to perform time-series analysis or who need a refresher.


Module 1: Introduction

Lecture 1 Course Overview and Objectives

This "lecture" introduces the instructor and provides an outline of the course. It sets expectations for previously acquired knowledge and expertise and outlines what the learner will achieve throughout the course.

Module 2: Basics of Healthcare Data

Lecture 2 Basics of Healthcare Data

This module gives an overview of the basic principles of healthcare data. By the end of this section, the learner should be able to: - Identify the origins of healthcare data; - Identify the basic elements of healthcare data; - Understand the reasons why healthcare data are analyzed; - Visualize the structure of a finished data product.

Module 3: Count Data

Lecture 3 Count Data

This lecture describes count data and its usefulness (and necessity) in a time-series analysis. By the end of this section, the learner should be able to: - List the basic components of a count data set; - Understand how to organize count data; - Describe the interplay between count data and time measurements in a time series analysis.

Module 4: Person-time and Rates

Lecture 4 Person-time and rates

By the end of this lecture, the learner will be able to: - Define the concept of person-time - Enumerate person-time for individuals and groups; - Differentiate between rates and risk calculations; - Calculate crude rates; Describe the utility of rates in a time series analysis.

Module 5: The Poisson Distribution

Lecture 5 The Poisson Distribution

This lecture explores the principles of the Poisson distribution and explains its use in time series analysis. By the end of this lecture, the learner should be able to: - Describe the nature of the Poisson distribution - List the assumptions involved in Poisson modeling - Describe which types of outcomes follow the Poisson model - Describe how the Poisson distribution relates to count data - Calculate/estimate probability in Poisson distributed data.

Module 6: Organizing Time Series Data

Lecture 6 Getting Started with SAS
Lecture 7 Organizing Time Series Data

This lecture teaches the learner coding strategies for organizing and checking time series data. By the end of this lecture, the learner should be able to: - Write code to organize time series data - Create appropriate variables for time series analysis - Perform An exploratory analysis and explain the importance of doing so using - Use the frequencies, univariate, means, and correlation procedures (PROCS).

Module 7: Time Series Modeling

Lecture 8 Time Series Modeling Part 1

This is the first part of a two-part lecture in time series modeling. By the end of this lecture, the learner should be able to: - Describe the basic principles behind time series modeling; - Successfully produce code that properly edits your analysis data preparatory to performing a time series analysis; - Produce code to identify the proper link, distribution, and offset for time series modeling using PROC GENMOD.

Lecture 9 Time Series Modeling: Part 2

This is lecture 2 of 2 in time series modeling. By the end of this lecture, the learner should be able to: - Add a predictor variable to the PROC GENMOD syntax and interpret its use; - Estimate unadjusted rates using PROC GENMOD; - Use PROC TIMESERIES to graph and estimate trends in your data; - Understand the basics of PROC ARIMA.

Lecture 10 Guided Exercise 1
Lecture 11 Adjusting the Model

This lecture teaches the statistical principles behind adjusting a statistical model and provides examples of syntax that is to be used to adjust models, By the end of the lecture, the learner should be able to: - Understand why statistical models may need adjusting; - Define the concept of "statistical influence"; - Explain the "change-in-estimate" criteria; - Explain the concept of effect modification and how to code it.

Lecture 12 Reporting Results

This lecture explores the tenants of reporting results in an organized and professional manner. By the end of this lecture, the learner will be able to: - List the sections involved in a manuscript/white paper; - Properly formulate a Methods section; - Understand the order in which Results should be reported; - Properly formulate tables for manuscripts; - Organize an abstract properly.

Lecture 13 Guided Exercise 2


6 Reviews

Srinka G

August, 2017

The course content is accurate and the learning is wonderful!! Thanks for organizing this specialized guide for time series analysis using SAS in healthcare. As a beginner in this field, this has definitely become a reference material for me to refer whenever I need it.

Antony T

August, 2017

I am a working professional in the healthcare field where in SAS is one of the applications we use on a daily basis. My motive for taking this course is to learn some more innovative techniques that are used by professionals in healthcare environment. I would say this course suits best for a beginner but not for someone who already has a lot of experience with time series analysis in healthcare.

Periklis M

August, 2017

Excellent course - If you want to internalize and understand course content and tools, it requires more time than you’d think. I am happy to learn common views of health-correlated data, and how to clean up and put in order data for statistical analysis.

Sreeni P

August, 2017

The guided student exercises are helpful, especially for figuring out the right issues. Substances are useful to build up a statistical model to analyze time-series data. Overall a good course!

Javier R

November, 2017

Dr. Nelms' Time Series Analysis in Healthcare course uses a soup-to-nuts approach to take the learner from raw data to a finished time series analysis. The course materials are clear and easy to follow, even for those who have never performed a time series analysis. Overall a very informative course, thanks!

Tanya R

November, 2017

This course is structured as a step-by-step guide for time series analysis, making it very accessible for both advanced data scientists and beginners. In this course, the instructor gives you all the tools need to go from raw data to a completed time series analysis.