Interrupted time series datasets from studies investigating the impact of interventions or exposures in public health and social science: a data note
Abstract Objectives The interrupted time series (ITS) design is commonly used to investigate the impact of an intervention or exposure in public health. There are many statistical methods that can be used to analyse ITS data and to meta-analyse their results. We undertook two empirical studies to in...
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2025-01-01
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| Online Access: | https://doi.org/10.1186/s13104-024-07055-5 |
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| author | Simon L Turner Elizabeth Korevaar Amalia Karahalios Andrew B Forbes Joanne E McKenzie |
| author_facet | Simon L Turner Elizabeth Korevaar Amalia Karahalios Andrew B Forbes Joanne E McKenzie |
| author_sort | Simon L Turner |
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| description | Abstract Objectives The interrupted time series (ITS) design is commonly used to investigate the impact of an intervention or exposure in public health. There are many statistical methods that can be used to analyse ITS data and to meta-analyse their results. We undertook two empirical studies to investigate: (i) how effect estimates (and associated statistics) compared when six statistical methods were applied to 190 real-world datasets; and (ii) how meta-analysis effect estimates (and associated statistics) compared when the combinations of two ITS analysis methods and five meta-analysis methods were applied to 17 real-world meta-analyses including 283 ITS datasets. Here we present a curated repository of a subset of ITS datasets from these studies. Data description The repository includes 430 ITS datasets curated from the two empirical studies. The datasets are diverse in the populations, interruptions and outcomes examined, and are methodologically diverse in the outcome types, aggregation time intervals, number of timepoints and segments. Most of the datasets are from public health. For each dataset, we provide the outcome value at each timepoint and the segment (indicating different interruptions), along with characteristics of the dataset. This repository may be of value for future research of ITS studies, and as a source of examples of ITS for use in teaching. |
| format | Article |
| id | doaj-art-ffe81d6756764c5eb91a7e36258a87c9 |
| institution | Kabale University |
| issn | 1756-0500 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
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| series | BMC Research Notes |
| spelling | doaj-art-ffe81d6756764c5eb91a7e36258a87c92025-01-26T12:13:19ZengBMCBMC Research Notes1756-05002025-01-011811410.1186/s13104-024-07055-5Interrupted time series datasets from studies investigating the impact of interventions or exposures in public health and social science: a data noteSimon L Turner0Elizabeth Korevaar1Amalia Karahalios2Andrew B Forbes3Joanne E McKenzie4School of Public Health and Preventive Medicine, Monash UniversitySchool of Public Health and Preventive Medicine, Monash UniversityCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of MelbourneSchool of Public Health and Preventive Medicine, Monash UniversitySchool of Public Health and Preventive Medicine, Monash UniversityAbstract Objectives The interrupted time series (ITS) design is commonly used to investigate the impact of an intervention or exposure in public health. There are many statistical methods that can be used to analyse ITS data and to meta-analyse their results. We undertook two empirical studies to investigate: (i) how effect estimates (and associated statistics) compared when six statistical methods were applied to 190 real-world datasets; and (ii) how meta-analysis effect estimates (and associated statistics) compared when the combinations of two ITS analysis methods and five meta-analysis methods were applied to 17 real-world meta-analyses including 283 ITS datasets. Here we present a curated repository of a subset of ITS datasets from these studies. Data description The repository includes 430 ITS datasets curated from the two empirical studies. The datasets are diverse in the populations, interruptions and outcomes examined, and are methodologically diverse in the outcome types, aggregation time intervals, number of timepoints and segments. Most of the datasets are from public health. For each dataset, we provide the outcome value at each timepoint and the segment (indicating different interruptions), along with characteristics of the dataset. This repository may be of value for future research of ITS studies, and as a source of examples of ITS for use in teaching.https://doi.org/10.1186/s13104-024-07055-5Interrupted time seriesMeta-analysisPublic healthEmpirical evaluation |
| spellingShingle | Simon L Turner Elizabeth Korevaar Amalia Karahalios Andrew B Forbes Joanne E McKenzie Interrupted time series datasets from studies investigating the impact of interventions or exposures in public health and social science: a data note BMC Research Notes Interrupted time series Meta-analysis Public health Empirical evaluation |
| title | Interrupted time series datasets from studies investigating the impact of interventions or exposures in public health and social science: a data note |
| title_full | Interrupted time series datasets from studies investigating the impact of interventions or exposures in public health and social science: a data note |
| title_fullStr | Interrupted time series datasets from studies investigating the impact of interventions or exposures in public health and social science: a data note |
| title_full_unstemmed | Interrupted time series datasets from studies investigating the impact of interventions or exposures in public health and social science: a data note |
| title_short | Interrupted time series datasets from studies investigating the impact of interventions or exposures in public health and social science: a data note |
| title_sort | interrupted time series datasets from studies investigating the impact of interventions or exposures in public health and social science a data note |
| topic | Interrupted time series Meta-analysis Public health Empirical evaluation |
| url | https://doi.org/10.1186/s13104-024-07055-5 |
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