Applied Time Series Analysis in Healthcare

This 3-hour seminar will provide attendees with the theory and application of time series analysis. The main focus will be on autoregressive integrated moving average (ARIMA) techniques. Variations of the ARIMA and other models which operate under non-linear data, non-stationary data, seasonality, and trends will also be examined.

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Why Should You Attend:

Time series models are invaluable to the health care field when it comes to planning and forecasting. Time series models can be used in many aspects of healthcare, including prediction of healthcare expenditures, tracking public health outcomes such as COVID-19, and assessing trends and interventions for patients with hypertension, diabetes, or other chronic diseases.

Examples of times series analyses will be presented using R software. Data and annotated syntax/code will be provided to attendees so they may work the exercises on their own.

Learning Objectives:
  • Basic understanding of data used in health care settings
  • When to use a time series model
  • Assumptions and limitations of time series modeling
  • How to prepare data for modeling
  • Basic steps in modeling time series data
  • Develop graphical displays of time series data
  • How to report findings of time series analysis
  • How to adjust models to meet assumptions for analysis or to troubleshoot discrepancies in the data or findings
Areas Covered in the Webinar:
  • Forecasting, planning, and goal setting
  • What can I forecast/predict?
  • Basic steps in forecasting
  • Statistical theory as related to predictive models and forecasting
  • Graphics
  • Time series regression
  • Time series decomposition
  • Smoothing techniques
  • ARIMA models
  • Dynamic Regression models
  • Hierarchical and grouped times series models
  • Issues in forecasting and predictive modeling
Who Will Benefit:
  • Investigators
  • Administrators in health care fields
  • Nursing Management
  • Hospital Management
  • Physicians
  • Clinical Investigators
  • Clinical Research Statisticians
  • Clinical Research Coordinators
  • Clinical Research Nurse Coordinators
  • Clinical Research Associates/Assistants
  • Clinical Project Managers/Leaders
  • Study Managers
  • Regulatory Professionals who use statistical concepts/terminology in reporting
  • Medical Writers and others who need to interpret statistical reports
  • Administrators in health care fields
Instructor Profile:
Elaine Eisenbeisz Elaine Eisenbeisz

Owner, Omega Statistics

Elaine Eisenbeisz is a private practice statistician and owner of Omega Statistics, a statistical consulting firm based in Southern California.
Elaine earned her B.S. in Statistics at UC Riverside and received her Master’s Certification in Applied Statistics from Texas A&M.
Elaine is a member in good standing with the American Statistical Association and a member of the Mensa High IQ Society. Omega Statistics holds an A+ rating with the Better Business Bureau.

Elaine has designed the methodology and analyzes data for numerous studies in the clinical, biotech, and health care fields. Elaine has also works as a contract statistician with private researchers and biotech start-ups as well as with larger companies such as Allergan, Nutrisystem and Rio Tinto Minerals. Throughout her tenure as a private practice statistician, she has published work with researchers and colleagues in peer-reviewed journals.

Refund Policy

Registrants may cancel up to two working days prior to the course start date and will receive a letter of credit to be used towards a future course up to one year from date of issuance. FDATrainingAlert would process/provide refund if the Live Webinar has been cancelled. The attendee could choose between the recorded version of the webinar or refund for any cancelled webinar. Refunds will not be given to participants who do not show up for the webinar. On-Demand Recordings can be requested in exchange.

Webinar may be cancelled due to lack of enrolment or unavoidable factors. Registrants will be notified 24hours in advance if a cancellation occurs. Substitutions can happen any time.

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