Although composite indicators are widely used to inform health system performance comparisons, such measures typically embed contentious assumptions, for instance about the weights assigned to constituent indicators. Moreover, although many comparative measures are constructed as ratios, the choice of denominator is not always straightforward. The conventional approach is to determine a single set of weights and to choose a single denominator, even though this involves considerable methodological difficulties. This study proposes an alternative approach to handle incomplete information about an appropriate set of weights and about a defensible denominator in composite indicators which considers all feasible weights and can incorporate multiple denominators. We illustrate this approach for comparative quality assessments of Scottish Health Boards. The results (displayed as ranking intervals and dominance relations) help identify Boards which cannot be ranked, say, worse than 4th or better than 7th. Such rankings give policy-makers a sense of the uncertainty around ranks, indicating the extent to which action is warranted. By identifying the full range of rankings that the organizations under comparison may attain, the approach proposed here acknowledges imperfect information about the "correct" set of weights and the appropriate denominator and may thus help to increase transparency of and confidence in health system performance comparisons.
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http://dx.doi.org/10.1016/j.socscimed.2016.06.026 | DOI Listing |
Forensic Sci Int Genet
January 2025
Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA; Department of Computer Science, Rutgers University, Camden, NJ 08102, USA.
Recent developments in single-cell analysis have revolutionized basic research and have garnered the attention of the forensic domain. Though single-cell analysis is not new to forensics, the ways in which these data can be generated and interpreted are. Modern interpretation strategies report likelihood ratios that rely on a model of the world that is a simplification of it.
View Article and Find Full Text PDFJ Sports Med Phys Fitness
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Department of Orthopedics and Trauma Surgery, BG University Hospital, Bochum, Germany.
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View Article and Find Full Text PDFTransl Cancer Res
December 2024
Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, China.
Background: The pathological sub-classification of lung cancer is crucial in diagnosis, treatment and prognosis for patients. Quick and timely identification of pathological subtypes from imaging examinations rather than histological tests could help guiding therapeutic strategies. The aim of the study is to construct a non-invasive radiomics-based model for predicting the subtypes of lung cancer on brain metastases (BMs) from multiple magnetic resonance imaging (MRI) sequences.
View Article and Find Full Text PDFTransl Cancer Res
December 2024
Department of Urology, Affiliated Hospital of Chifeng University, Chifeng, China.
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View Article and Find Full Text PDFTransl Cancer Res
December 2024
Department of Radiation Oncology, The Second Hospital of Lanzhou University, Lanzhou, China.
Background: Within the realm of primary brain tumors, specifically glioblastoma (GBM), presents a notable obstacle due to their unfavorable prognosis and differing median survival rates contingent upon tumor grade and subtype. Despite a plethora of research connecting cardiotrophin-1 (CTF1) modifications to a range of illnesses, its correlation with glioma remains uncertain. This study investigated the clinical value of CTF1 in glioma and its potential as a biomarker of the disease.
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