Pharmaceutical policies: effects of regulating drug insurance schemes

Pantoja, Tomás; Peñaloza, Blanca; Cid, Camilo; Herrera, Cristian A; Ramsay, Craig R; Hudson, Jemma

doi:10.1002/14651858.cd011703.pub2

Cited by 4 publications

(2 citation statements)

References 221 publications

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“…12,13 However, these do not include terms specific to the ITS design and their performance has not been evaluated against a set of ITS studies. Ad hoc search filters have also been developed in the context of methodological studies [2][3][4]6,[8][9][10][11]14 and systematic reviews of ITS studies 15,16 ; however, the performance of these has not been evaluated. Therefore, in this study, we aimed to develop and examine the performance of search filters to retrieve ITS studies in MED-LINE and PubMed.…”

Section: Introductionmentioning

confidence: 99%

Development of a search filter to retrieve reports of interrupted time series studies from MEDLINE and PubMed

Nguyen,

McKenzie,

Turner

et al. 2024

Research Synthesis Methods

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BackgroundInterrupted time series (ITS) studies contribute importantly to systematic reviews of population‐level interventions. We aimed to develop and validate search filters to retrieve ITS studies in MEDLINE and PubMed.MethodsA total of 1017 known ITS studies (published 2013–2017) were analysed using text mining to generate candidate terms. A control set of 1398 time‐series studies were used to select differentiating terms. Various combinations of candidate terms were iteratively tested to generate three search filters. An independent set of 700 ITS studies was used to validate the filters' sensitivities. The filters were test‐run in Ovid MEDLINE and the records randomly screened for ITS studies to determine their precision. Finally, all MEDLINE filters were translated to PubMed format and their sensitivities in PubMed were estimated.ResultsThree search filters were created in MEDLINE: a precision‐maximising filter with high precision (78%; 95% CI 74%–82%) but moderate sensitivity (63%; 59%–66%), most appropriate when there are limited resources to screen studies; a sensitivity‐and‐precision‐maximising filter with higher sensitivity (81%; 77%–83%) but lower precision (32%; 28%–36%), providing a balance between expediency and comprehensiveness; and a sensitivity‐maximising filter with high sensitivity (88%; 85%–90%) but likely very low precision, useful when combined with specific content terms. Similar sensitivity estimates were found for PubMed versions.ConclusionOur filters strike different balances between comprehensiveness and screening workload and suit different research needs. Retrieval of ITS studies would be improved if authors identified the ITS design in the titles.

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Section: Introductionmentioning

confidence: 99%

Development of a search filter to retrieve reports of interrupted time series studies from MEDLINE and PubMed

Nguyen,

McKenzie,

Turner

et al. 2024

Research Synthesis Methods

View full text Add to dashboard Cite

show abstract

“…However, these do not include terms specific to the ITS design and their performance has not been evaluated against a set of ITS studies. Ad hoc search filters have also been developed in the context of methodological studies [1][2][3][4]6,[8][9][10][11] and systematic reviews of ITS studies 14,15 ; however, the performance of these has not been evaluated. Therefore, in this study, we aimed to develop and examine the performance of search filters to retrieve ITS studies in MEDLINE and PubMed.…”

Section: Introductionmentioning

confidence: 99%

Development of a search filter to retrieve reports of interrupted time series studies from MEDLINE and PubMed

Nguyen,

McKenzie,

Turner

et al. 2023

Preprint

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Background: Interrupted time series (ITS) studies contribute importantly to systematic reviews of population-level interventions. However, there is no search filter designed to identify only ITS studies from bibliographic databases. We aimed to develop and validate search filters to retrieve ITS studies in MEDLINE and PubMed. Methods: A set of 1,017 known ITS studies (published 2013-2017) were analysed using text mining to generate candidate terms. A population set of 1,398 time-series studies were used to select discriminating terms. Various combinations of candidate terms were iteratively tested to generate three search filters. An independent set of 700 ITS studies were used to validate the filters' sensitivities. The filters were test-run in Ovid MEDLINE; their retrieved records were randomly screened for ITS studies to determine their precision. Finally, all three MEDLINE filters were translated to PubMed format and their sensitivities in PubMed were estimated. Results: Three search filters were created: a precision-maximising filter with high precision (78%; 95% CI 74%-82%) but moderate sensitivity (63%; 59%-66%), useful for rapid reviews; a sensitivity-and-precision-maximising filter with high sensitivity (81%; 77%-83%) but lower precision (32%; 28%-36%), useful when a balance of expediency and comprehensiveness is required; and a sensitivity-maximising filter with high sensitivity (88%; 85%-90%) but likely very low precision, useful for search strategies accompanying specific content terms. Conclusion: Our filters strike different balances between comprehensiveness and screening workload and suit different research needs. To improve retrieval of ITS studies, authors need to identify ITS designs in titles more frequently.

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