Optimal search strategies for identifying moderators and predictors of treatment effects in PubMed

BACKGROUND: Treatment effects differ across patients. To guide selection of treatments for patients, it is essential to acknowledge these differences and identify moderators or predictors. Our aim was to generate optimal search strategies (commonly known as filters) for PubMed to retrieve papers identifying moderators and predictors of treatment effects. METHODS: Six journals were hand-searched for articles on moderators or predictors. Selected articles were randomly allocated to a development and validation set. Search terms were extracted from the development set and tested for their performance. Search filters were created from combinations of these terms and tested in the validation set. RESULTS: Of 4407 articles, 198 were considered to be relevant. The most sensitive filter in the development set '("Epidemiologic Methods" [MeSH] OR assign* OR control*[tiab] OR trial*[tiab]) AND therapy*[sh]' yielded in the validation set a sensitivity of 89% [88%-90%] and a specificity of 80% [79%-82%]. CONCLUSIONS: The search filters created in this study can help to efficiently retrieve evidence on moderators and predictors of treatment effect. Testing of the filters in multiple domains should reveal robustness across disciplines. These filters can facilitate the retrieval of evidence on moderators and predictors of treatment effects, helping the implementation of stratified or personalised health care.