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

Abstract

Background: Interrupted 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.

Methods: A 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.

Results: Three 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.

Conclusion: Our 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.

Keywords: interrupted time series; literature search; search filter; search strategy; sensitivity; specificity.