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Code and data for project: Time aggregation in interrupted time series can introduce bias in immediate level change effect estimates when using segmented linear regression. Pre-publication version.

Monash University
Andrew Forbes (Aggregated by) ELIZABETH KOREVAAR (Aggregated by) Joanne McKenzie (Aggregated by) Simon Turner (Aggregated by)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.26180/30229273.v1&rft.title=Code and data for project: Time aggregation in interrupted time series can introduce bias in immediate level change effect estimates when using segmented linear regression. Pre-publication version.&rft.identifier=https://doi.org/10.26180/30229273.v1&rft.publisher=Monash University&rft.description=When aggregating ITS data over time, researchers need to be aware of the potential for bias in the estimate of the immediate level change parameter, so that they can mitigate its impact. The commonly used immediate level change parameter is not always calculated at the designated time, nor described appropriately. The code contained in this repository includes a simulation and example of this effect.The version attached here is pre-publication.&rft.creator=Andrew Forbes&rft.creator=ELIZABETH KOREVAAR&rft.creator=Joanne McKenzie&rft.creator=Simon Turner&rft.date=2025&rft_rights=CC-BY-4.0&rft_subject=bias&rft_subject=segmented linear regression&rft_subject=statistical methods&rft_subject=interrupted time series&rft_subject=Public health not elsewhere classified&rft.type=dataset&rft.language=English Access the data
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When aggregating ITS data over time, researchers need to be aware of the potential for bias in the estimate of the immediate level change parameter, so that they can mitigate its impact. The commonly used "immediate" level change parameter is not always calculated at the designated time, nor described appropriately. The code contained in this repository includes a simulation and example of this effect.

The version attached here is pre-publication.

Issued: 2025-09-29

Created: 2025-09-29

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Subjects
Public health not elsewhere classified | bias | interrupted time series | segmented linear regression | statistical methods |

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Identifiers
ACN 633 798 857