This 4-hour webinar will provide attendees with the theory and application of time series analysis. The main focus will be on auto regressive 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.
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.
AREA COVERED
- 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
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
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
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.
- 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
- 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
- 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
