Quality appraisal for systematic literature reviews of health state utility values: a descriptive analysis
Health state utility values (HSUVs) are an essential input parameter to cost-utility analysis (CUA). Systematic literature reviews (SLRs) provide summarized information for selecting utility values from an increasing number of primary studies eliciting HSUVs. Quality appraisal (QA) of such SLRs is a...
Gespeichert in:
| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
25 November 2022
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| In: |
BMC medical research methodology
Year: 2022, Jahrgang: 22, Pages: 1-20 |
| ISSN: | 1471-2288 |
| DOI: | 10.1186/s12874-022-01784-6 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1186/s12874-022-01784-6 Verlag, kostenfrei, Volltext: http://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-022-01784-6 
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| Verfasserangaben: | Muchandifunga Trust Muchadeyi, Karla Hernandez-Villafuerte and Michael Schlander |
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| 520 | |a Health state utility values (HSUVs) are an essential input parameter to cost-utility analysis (CUA). Systematic literature reviews (SLRs) provide summarized information for selecting utility values from an increasing number of primary studies eliciting HSUVs. Quality appraisal (QA) of such SLRs is an important process towards the credibility of HSUVs estimates; yet, authors often overlook this crucial process. A scientifically developed and widely accepted QA tool for this purpose is lacking and warranted. To comprehensively describe the nature of QA in published SRLs of studies eliciting HSUVs and generate a list of commonly used items. A comprehensive literature search was conducted in PubMed and Embase from 01.01.2015 to 15.05.2021. SLRs of empirical studies eliciting HSUVs that were published in English were included. We extracted descriptive data, which included QA tools checklists or good practice recommendations used or cited, items used, and the methods of incorporating QA results into study findings. Descriptive statistics (frequencies of use and occurrences of items, acceptance and counterfactual acceptance rates) were computed and a comprehensive list of QA items was generated. A total of 73 SLRs were included, comprising 93 items and 35 QA tools and good recommendation practices. The prevalence of QA was 55% (40/73). Recommendations by NICE and ISPOR guidelines appeared in 42% (16/40) of the SLRs that appraised quality. The most commonly used QA items in SLRs were response rates (27/40), statistical analysis (22/40), sample size (21/40) and loss of follow up (21/40). Yet, the most commonly featured items in QA tools and GPRs were statistical analysis (23/35), confounding or baseline equivalency (20/35), and blinding (14/35). Only 5% of the SLRS used QA to inform the data analysis, with acceptance rates of 100% (in two studies) 67%, 53% and 33%. The mean counterfactual acceptance rate was 55% (median 53% and IQR 56%). There is a considerably low prevalence of QA in the SLRs of HSUVs. Also, there is a wide variation in the QA dimensions and items included in both SLRs and extracted tools. This underscores the need for a scientifically developed QA tool for multi-variable primary studies of HSUVs. | ||
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