Effect size quantification for interrupted time series analysis: implementation in R and analysis for Covid-19 research
- DOI
- 10.1186/s12982-022-00118-7
- Published
- 2022-11-11
- Container
- Emerging Themes in Epidemiology
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1186/s12982-022-00118-7,
title = {Effect size quantification for interrupted time series analysis: implementation in R and analysis for Covid-19 research},
author = {Yael Travis-Lumer and Yair Goldberg and Stephen Z. Levine},
year = {2022},
journal = {Emerging Themes in Epidemiology},
doi = {10.1186/s12982-022-00118-7},
url = {https://doi.org/10.1186/s12982-022-00118-7}
}RIS
TY - JOUR TI - Effect size quantification for interrupted time series analysis: implementation in R and analysis for Covid-19 research AU - Yael Travis-Lumer AU - Yair Goldberg AU - Stephen Z. Levine PY - 2022 JO - Emerging Themes in Epidemiology DO - 10.1186/s12982-022-00118-7 UR - https://doi.org/10.1186/s12982-022-00118-7 ER -
APA
Travis-Lumer, Y., Goldberg, Y., & Levine, S. Z. (2022). Effect size quantification for interrupted time series analysis: implementation in R and analysis for Covid-19 research. Emerging Themes in Epidemiology. https://doi.org/10.1186/s12982-022-00118-7
Source records
- crossref · retrieved 2026-11-10T18:13:05.942Z