Introduction: The INTEGRATE-HTA project provided methodology to evaluate complex technologies.

BackgroundThe importance of respecting patients’ preferences when making treatment decisions is increasingly recognized. Efficiently retrieving papers from the scientific literature reporting on the presence and nature of such preferences can help to achieve this goal. The objective of this study was to create a search filter for PubMed to help retrieve evidence on patient preferences for treatment outcomes.MethodsA total of 27 journals were hand-searched for articles on patient preferences for treatment outcomes published in 2011. Selected articles served as a reference set. To develop optimal search strategies to retrieve this set, all articles in the reference set were randomly split into a development and a validation set. MeSH-terms and keywords retrieved using PubReMiner were tested individually and as combinations in PubMed and evaluated for retrieval performance (e.g. sensitivity (Se) and specificity (Sp)).ResultsOf 8238 articles, 22 were considered to report empirical evidence on patient preferences for specific treatment outcomes. The best search filters reached Se of 100 % [95 % CI 100-100] with Sp of 95 % [94–95 %] and Sp of 97 % [97–98 %] with 75 % Se [74–76 %]. In the validation set these queries reached values of Se of 90 % [89–91 %] with Sp 94 % [93–95 %] and Se of 80 % [79–81 %] with Sp of 97 % [96–96 %], respectively.ConclusionsNarrow and broad search queries were developed which can help in retrieving literature on patient preferences for treatment outcomes. Identifying such evidence may in turn enhance the incorporation of patient preferences in clinical decision making and health technology assessment.Electronic supplementary materialThe online version of this article (doi:10.1186/s12874-016-0192-5) contains supplementary material, which is available to authorized users.

BackgroundPersonalized healthcare relies on the identification of factors explaining why individuals respond differently to the same intervention. Analyses identifying such factors, so called predictors and moderators, have their own set of assumptions and limitations which, when violated, can result in misleading claims, and incorrect actions. The aim of this study was to develop a checklist for critically appraising the results of predictor and moderator analyses by combining recommendations from published guidelines and experts in the field.MethodsCandidate criteria for the checklist were retrieved through systematic searches of the literature. These criteria were evaluated for appropriateness using a Delphi procedure. Two Delphi rounds yielded a pilot checklist, which was tested on a set of papers included in a systematic review on reinforced home-based palliative care. The results of the pilot informed a third Delphi round, which served to finalize the checklist.ResultsForty-nine appraisal criteria were identified in the literature. Feedback was obtained from fourteen experts from (bio)statistics, epidemiology and other associated fields elicited via three Delphi rounds. Additional feedback from other researchers was collected in a pilot test. The final version of our checklist included seventeen criteria, covering the design (e.g. a priori plausibility), analysis (e.g. use of interaction tests) and results (e.g. complete reporting) of moderator and predictor analysis, together with the transferability of the results (e.g. clinical importance). There are criteria both for individual papers and for bodies of evidence.ConclusionsThe proposed checklist can be used for critical appraisal of reported moderator and predictor effects, as assessed in randomized or non-randomized studies using individual participant or aggregate data. This checklist is accompanied by a user’s guide to facilitate implementation. Its future use across a wide variety of research domains and study types will provide insights about its usability and feasibility.Electronic supplementary materialThe online version of this article (10.1186/s12874-017-0451-0) contains supplementary material, which is available to authorized users.

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.