OBJECTIVES: This study was designed to assess the sensitivity of three Ovid MEDLINE search filters developed to identify studies reporting health state utility values (HSUVs), to improve the performance of the best performing filter, and to validate resulting search filters. METHODS: Three quasi-gold standard sets (QGS1, QGS2, QGS3) of relevant studies were harvested from reviews of studies reporting HSUVs. The performance of three initial filters was assessed by measuring their relative recall of studies in QGS1. The best performing filter was then developed further using QGS2. This resulted in three final search filters (FSF1, FSF2, and FSF3), which were validated using QGS3. RESULTS: FSF1 (sensitivity maximizing) retrieved 132/139 records (sensitivity: 95 percent) in the QGS3 validation set. FSF1 had a number needed to read (NNR) of 842. FSF2 (balancing sensitivity and precision) retrieved 128/139 records (sensitivity: 92 percent) with a NNR of 502. FSF3 (precision maximizing) retrieved 123/139 records (sensitivity: 88 percent) with a NNR of 383. CONCLUSIONS: We have developed and validated a search filter (FSF1) to identify studies reporting HSUVs with high sensitivity (95 percent) and two other search filters (FSF2 and FSF3) with reasonably high sensitivity (92 percent and 88 percent) but greater precision, resulting in a lower NNR. These seem to be the first validated filters available for HSUVs. The availability of filters with a range of sensitivity and precision options enables researchers to choose the filter which is most appropriate to the resources available for their specific research.
OBJECTIVES: This study was designed to assess the sensitivity of three Ovid MEDLINE search filters developed to identify studies reporting health state utility values (HSUVs), to improve the performance of the best performing filter, and to validate resulting search filters. METHODS: Three quasi-gold standard sets (QGS1, QGS2, QGS3) of relevant studies were harvested from reviews of studies reporting HSUVs. The performance of three initial filters was assessed by measuring their relative recall of studies in QGS1. The best performing filter was then developed further using QGS2. This resulted in three final search filters (FSF1, FSF2, and FSF3), which were validated using QGS3. RESULTS: FSF1 (sensitivity maximizing) retrieved 132/139 records (sensitivity: 95 percent) in the QGS3 validation set. FSF1 had a number needed to read (NNR) of 842. FSF2 (balancing sensitivity and precision) retrieved 128/139 records (sensitivity: 92 percent) with a NNR of 502. FSF3 (precision maximizing) retrieved 123/139 records (sensitivity: 88 percent) with a NNR of 383. CONCLUSIONS: We have developed and validated a search filter (FSF1) to identify studies reporting HSUVs with high sensitivity (95 percent) and two other search filters (FSF2 and FSF3) with reasonably high sensitivity (92 percent and 88 percent) but greater precision, resulting in a lower NNR. These seem to be the first validated filters available for HSUVs. The availability of filters with a range of sensitivity and precision options enables researchers to choose the filter which is most appropriate to the resources available for their specific research.
Keywords:
Bibliographic; Cost-Benefit Analysis; Databases; Information storage and retrieval; Quality of life; Quality-adjusted life-years
Authors: Tristan M Snowsill; Neil A J Ryan; Emma J Crosbie; Ian M Frayling; D Gareth Evans; Chris J Hyde Journal: PLoS One Date: 2019-08-30 Impact factor: 3.240
Authors: Manraj N Kaur; Jiajun Yan; Anne F Klassen; Justin P David; Dilshan Pieris; Manraj Sharma; Louise Bordeleau; Feng Xie Journal: Med Decis Making Date: 2022-01-18 Impact factor: 2.749