Speaker
Description
This package allows the user to perform interrupted time series (ITS) with a control across successive interventions (up to 3). This code is based on a prior analysis done at our county where we compared the effect of two successive behavioural interventions designed in improving the uptake of a COVID-19 booster intervention programme amongst immunosuppressed patients at several primary care practices within our county and from an rpub article detailing the methodology by Prof Chrissy Roberts at London School of Hygiene and Tropical Medicine.
Interrupted Time Series is a robust quasi-experimental design which can assess the performance of an intervention through the effects the intervention has on the level or slope of an outcome’s trend over time after a clear defined time point, and has wide applications in healthcare, public health, public policy, economics, social programmes etc. Multiple Interrupted Time Series with Control extends this method by allowing the possibility to compare pre and post-intervention trends while controlling for external factors via the control, to account for independent secular and periodic changes, and for successive interventions.
Functions in the package greatly streamline the process for the end user where each step of performing multiple ITS with control is guided in a step by step manner, from wrangling, to modelling, to data visualisation, to interpretation and key coefficient statistical significance testing.
Vignettes in the package demonstrate examples and provide guidance on how to apply the functions and interpret their findings.
Target audience (only for tutorials)
Anyone interested in applying statistical methods for programme evaluation for interventions with clearly defined time points and the possibility of implementing a control.
Additional Material or Paper
https://github.com/herts-phei/multipleITScontrol
If you used AI tools or services to support the preparation of this submission, please state the name and reason for using each of them.
No AI tools/services were used
Learning goals (only for tutorials)
For people to become familiar with the background and use case of multiple interrupted time series and control. For people to understand it's utility and benefits but also its limitations and what it can and cannot be applied to, and what people can infer from the results.
For the package, I want people to become familiar with the core functions which allow the user to go from a basic formatted dataset appropriate for interrupted time series with a control, to applying the functions the package has created for wrangling, modelling, interpretation, and visualisation.
| Keywords: Please list up to 5 keywords to help us find the right session for your contribution. | interrupted time series, control, multiple, quasi-experimental, causal |
|---|---|
| Virtual Option | This submission is for onsite presentation primarily, but I would also like it to be considered for pre-recorded virtual presentation if I don't get an onsite slot |
| Video Recording | Video sharing is fine |
| The author(s) agree(s) to take responsibility and be accountable for the contents of the submission and is/are authorized to present it. | Confirm |
| Interested in serving as reviewer? | victoryu@hertfordshire.gov.uk |