Study filters for non-randomized studies of interventions consistently lacked sensitivity upon external validation

Little evidence is available on searches for non-randomized studies (NRS) in bibliographic databases within the framework of systematic reviews. For instance, it is currently unclear whether, when searching for NRS, effective restriction of the search strategy to certain study types is possible. The following challenges need to be considered: 1) For non-randomized controlled trials (NRCTs): whether they can be identified by established filters for randomized controlled trials (RCTs). 2) For other NRS types (such as cohort studies): whether study filters exist for each study type and, if so, which performance measures they have. The aims of the present analysis were to identify and validate existing NRS filters in MEDLINE as well as to evaluate established RCT filters using a set of MEDLINE citations.

Our analysis is a retrospective analysis of study filters based on MEDLINE citations of NRS from Cochrane reviews. In a first step we identified existing NRS filters. For the generation of the reference set, we screened Cochrane reviews evaluating NRS, which covered a broad range of study types. The citations of the studies included in the Cochrane reviews were identified via the reviews' bibliographies and the corresponding PubMed identification numbers (PMIDs) were extracted from PubMed. Random samples comprising up to 200 citations (i.e. 200 PMIDs) each were created for each study type to generate the test sets.

A total of 271 Cochrane reviews from 41 different Cochrane groups were eligible for data extraction. We identified 14 NRS filters published since 2001. The study filters generated between 660,000 and 9.5 million hits in MEDLINE. Most filters covered several study types. The reference set included 2890 publications classified as NRS for the generation of the test sets. Twelve test sets were generated (one for each study type), of which 8 included 200 citations each. None of the study filters achieved sufficient sensitivity (≥ 92%) for all of the study types targeted.

The performance of current NRS filters is insufficient for effective use in daily practice. It is therefore necessary to develop new strategies (e.g. new NRS filters in combination with other search techniques). The challenges related to NRS should be taken into account.