The EORTC QLU-C10D distinguished better between cancer patients and the general population than PROPr and EQ-5D-5L in a cross-sectional study
Health state utility (HSU) instruments for calculating quality-adjusted life years, such as the European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Utility - Core 10 Dimensions (QLU-C10D), derived from the EORTC QLQ-30 questionnaire, the Patient-Reported Outcome Measur...
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| Veröffentlicht in: | Journal of clinical epidemiology 2025-01, Vol.177, p.111592, Article 111592 |
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| creator | Döhmen, Annika Obbarius, Alexander Kock, Milan Nolte, Sandra Sidey-Gibbons, Christopher J. Valderas, José M. Rohde, Jens Rieger, Kathrin Fischer, Felix Keilholz, Ulrich Rose, Matthias Klapproth, Christoph Paul |
| description | Health state utility (HSU) instruments for calculating quality-adjusted life years, such as the European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Utility - Core 10 Dimensions (QLU-C10D), derived from the EORTC QLQ-30 questionnaire, the Patient-Reported Outcome Measurement Information System (PROMIS) preference score (PROPr), and the EuroQoL-5-Dimensions-5-Levels (EQ-5D-5L), yield different HSU values due to different modeling and different underlying descriptive scales. For example the QLU-C10D includes cancer-relevant dimensions such as nausea. This study aimed to investigate how these differences in descriptive scales contribute to differences in HSU scores by comparing scores of cancer patients receiving chemotherapy to those of the general population.
EORTC QLU-C10D, PROPr, and EQ-5D-5L scores were obtained for a convenience sample of 484 outpatients of the Department of Oncology, Charité – Universitätsmedizin Berlin, Germany. Convergent and known group’s validity were assessed using Pearson's correlation and intraclass correlation coefficients (ICC). We assessed each descriptive dimension score's discriminatory power and compared them to those of the general population (n > 1000) using effect size (ES; Cohen's d) and area under the curve (AUC).
The mean scores of QLU-C10D (0.64; 95% CI 0.62-0.67), PROPr (0.38; 95% CI 0.36-0.40), and EQ-5D-5L (0.72; 95% CI 0.70-0.75) differed significantly, irrespective of sociodemographic factors, condition, or treatment. Conceptually similar descriptive scores as obtained from the HSU instruments showed varying degrees of discrimination in terms of ES and AUC between patients and the general population. The QLU-C10D and its dimensions showed the largest ES and AUC.
The QLU-C10D and its domains distinguished best between health states of the two populations, compared to the PROPr and EQ-5D-5L. As the EORTC Core Quality of Life Questionnaire (QLQ-C30) is widely used in clinical practice, its data are available for economic evaluation.
The assessment of dimensions of health-related quality of life (HRQoL), such as physical functioning or depression, is important to cancer patients and physicians for treatment and side effect monitoring. Descriptive HRQoL is measured by patient-reported outcomes measures (PROM). The European Organisation for Research and Treatment of Cancer (EORTC) QLQ-C30 questionnaire and the Patient-Reported Outcome Measurement Information System (PROMIS) are the most |
| doi_str_mv | 10.1016/j.jclinepi.2024.111592 |
| format | Article |
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EORTC QLU-C10D, PROPr, and EQ-5D-5L scores were obtained for a convenience sample of 484 outpatients of the Department of Oncology, Charité – Universitätsmedizin Berlin, Germany. Convergent and known group’s validity were assessed using Pearson's correlation and intraclass correlation coefficients (ICC). We assessed each descriptive dimension score's discriminatory power and compared them to those of the general population (n > 1000) using effect size (ES; Cohen's d) and area under the curve (AUC).
The mean scores of QLU-C10D (0.64; 95% CI 0.62-0.67), PROPr (0.38; 95% CI 0.36-0.40), and EQ-5D-5L (0.72; 95% CI 0.70-0.75) differed significantly, irrespective of sociodemographic factors, condition, or treatment. Conceptually similar descriptive scores as obtained from the HSU instruments showed varying degrees of discrimination in terms of ES and AUC between patients and the general population. The QLU-C10D and its dimensions showed the largest ES and AUC.
The QLU-C10D and its domains distinguished best between health states of the two populations, compared to the PROPr and EQ-5D-5L. As the EORTC Core Quality of Life Questionnaire (QLQ-C30) is widely used in clinical practice, its data are available for economic evaluation.
The assessment of dimensions of health-related quality of life (HRQoL), such as physical functioning or depression, is important to cancer patients and physicians for treatment and side effect monitoring. Descriptive HRQoL is measured by patient-reported outcomes measures (PROM). The European Organisation for Research and Treatment of Cancer (EORTC) QLQ-C30 questionnaire and the Patient-Reported Outcome Measurement Information System (PROMIS) are the most common PROM in the clinical HRQoL assessment. In recent years, multidimensional preference-based HRQoL measures were developed using these PROM as dimensions. These preference-based measures, also referred to as health state utility (HSU) scores, are needed for economic evaluations of treatments. The QLQ-C30's corresponding HSU score is the quality-of-life utility measure-core 10 dimensions (QLU-C10D), and PROMIS’ HSU score is the PROMIS preference score (PROPr). Both new HSU scores are frequently compared to the well-established EuroQoL-5-dimensions-5-levels (EQ-5D-5L). They all conceptualize HSU differently, as they assess different dimensions of HRQoL und use different models. Both the QLU-C10D and the PROPr have thus shown systematic differences to the EQ-5D-5L but these were largely consistent across the subgroups. Convergent and known groups validity can therefore be considered established. However, as HSU is a multidimensional construct, it remains unclear how differences in its dimensions, for example, its descriptive scales, contribute to differences in HSU scores. This is of importance as it is the descriptive scales that measure clinical HRQoL. We investigated this question by assessing each dimension’s ability to distinguish between a sample of 484 cancer patients and the German general population. We could show that the ability to distinguish depended on the domain: for example, for depression, the QLU-C10D and EQ-5D-5L distinguished clearer, while for physical function, PROMIS did. Overall, the QLU-C10D and its dimensions distinguish best between cancer patients and general population.</description><identifier>ISSN: 0895-4356</identifier><identifier>ISSN: 1878-5921</identifier><identifier>EISSN: 1878-5921</identifier><identifier>DOI: 10.1016/j.jclinepi.2024.111592</identifier><identifier>PMID: 39515489</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Adult ; Aged ; Cancer ; Chemotherapy ; Cognition & reasoning ; Cognitive ability ; Correlation coefficient ; Correlation coefficients ; Cost analysis ; Cross-Sectional Studies ; Economics ; EORTC QLQ-C30 ; EQ-5D-5L ; Evaluation ; Fatigue ; Female ; Germany ; Health services ; Health Status ; Health-related quality of life ; Humans ; Information systems ; Likert scale ; Male ; Middle Aged ; Neoplasms - psychology ; Patient Reported Outcome Measures ; Patient reported outcomes ; Patients ; Physical training ; Population studies ; Preferences ; PROMIS-29 ; PROPr ; Psychometrics ; QLU-C10D ; Quality of Life ; Quality-Adjusted Life Years ; Questionnaires ; Regression analysis ; Reproducibility of Results ; Sociodemographics ; Subgroups ; Surveys and Questionnaires - standards ; Validity</subject><ispartof>Journal of clinical epidemiology, 2025-01, Vol.177, p.111592, Article 111592</ispartof><rights>2024 The Author(s)</rights><rights>Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.</rights><rights>2024. The Author(s)</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c273t-83842312cea5e7fca80340992a9be855c23ad30ce2a5638a3412400e9ce60e583</cites><orcidid>0000-0001-6185-9423 ; 0000-0002-4732-7305 ; 0000-0002-9836-2557 ; 0000-0002-2754-8581 ; 0000-0002-8319-8379</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0895435624003482$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39515489$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Döhmen, Annika</creatorcontrib><creatorcontrib>Obbarius, Alexander</creatorcontrib><creatorcontrib>Kock, Milan</creatorcontrib><creatorcontrib>Nolte, Sandra</creatorcontrib><creatorcontrib>Sidey-Gibbons, Christopher J.</creatorcontrib><creatorcontrib>Valderas, José M.</creatorcontrib><creatorcontrib>Rohde, Jens</creatorcontrib><creatorcontrib>Rieger, Kathrin</creatorcontrib><creatorcontrib>Fischer, Felix</creatorcontrib><creatorcontrib>Keilholz, Ulrich</creatorcontrib><creatorcontrib>Rose, Matthias</creatorcontrib><creatorcontrib>Klapproth, Christoph Paul</creatorcontrib><title>The EORTC QLU-C10D distinguished better between cancer patients and the general population than PROPr and EQ-5D-5L in a cross-sectional study</title><title>Journal of clinical epidemiology</title><addtitle>J Clin Epidemiol</addtitle><description>Health state utility (HSU) instruments for calculating quality-adjusted life years, such as the European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Utility - Core 10 Dimensions (QLU-C10D), derived from the EORTC QLQ-30 questionnaire, the Patient-Reported Outcome Measurement Information System (PROMIS) preference score (PROPr), and the EuroQoL-5-Dimensions-5-Levels (EQ-5D-5L), yield different HSU values due to different modeling and different underlying descriptive scales. For example the QLU-C10D includes cancer-relevant dimensions such as nausea. This study aimed to investigate how these differences in descriptive scales contribute to differences in HSU scores by comparing scores of cancer patients receiving chemotherapy to those of the general population.
EORTC QLU-C10D, PROPr, and EQ-5D-5L scores were obtained for a convenience sample of 484 outpatients of the Department of Oncology, Charité – Universitätsmedizin Berlin, Germany. Convergent and known group’s validity were assessed using Pearson's correlation and intraclass correlation coefficients (ICC). We assessed each descriptive dimension score's discriminatory power and compared them to those of the general population (n > 1000) using effect size (ES; Cohen's d) and area under the curve (AUC).
The mean scores of QLU-C10D (0.64; 95% CI 0.62-0.67), PROPr (0.38; 95% CI 0.36-0.40), and EQ-5D-5L (0.72; 95% CI 0.70-0.75) differed significantly, irrespective of sociodemographic factors, condition, or treatment. Conceptually similar descriptive scores as obtained from the HSU instruments showed varying degrees of discrimination in terms of ES and AUC between patients and the general population. The QLU-C10D and its dimensions showed the largest ES and AUC.
The QLU-C10D and its domains distinguished best between health states of the two populations, compared to the PROPr and EQ-5D-5L. As the EORTC Core Quality of Life Questionnaire (QLQ-C30) is widely used in clinical practice, its data are available for economic evaluation.
The assessment of dimensions of health-related quality of life (HRQoL), such as physical functioning or depression, is important to cancer patients and physicians for treatment and side effect monitoring. Descriptive HRQoL is measured by patient-reported outcomes measures (PROM). The European Organisation for Research and Treatment of Cancer (EORTC) QLQ-C30 questionnaire and the Patient-Reported Outcome Measurement Information System (PROMIS) are the most common PROM in the clinical HRQoL assessment. In recent years, multidimensional preference-based HRQoL measures were developed using these PROM as dimensions. These preference-based measures, also referred to as health state utility (HSU) scores, are needed for economic evaluations of treatments. The QLQ-C30's corresponding HSU score is the quality-of-life utility measure-core 10 dimensions (QLU-C10D), and PROMIS’ HSU score is the PROMIS preference score (PROPr). Both new HSU scores are frequently compared to the well-established EuroQoL-5-dimensions-5-levels (EQ-5D-5L). They all conceptualize HSU differently, as they assess different dimensions of HRQoL und use different models. Both the QLU-C10D and the PROPr have thus shown systematic differences to the EQ-5D-5L but these were largely consistent across the subgroups. Convergent and known groups validity can therefore be considered established. However, as HSU is a multidimensional construct, it remains unclear how differences in its dimensions, for example, its descriptive scales, contribute to differences in HSU scores. This is of importance as it is the descriptive scales that measure clinical HRQoL. We investigated this question by assessing each dimension’s ability to distinguish between a sample of 484 cancer patients and the German general population. We could show that the ability to distinguish depended on the domain: for example, for depression, the QLU-C10D and EQ-5D-5L distinguished clearer, while for physical function, PROMIS did. Overall, the QLU-C10D and its dimensions distinguish best between cancer patients and general population.</description><subject>Adult</subject><subject>Aged</subject><subject>Cancer</subject><subject>Chemotherapy</subject><subject>Cognition & reasoning</subject><subject>Cognitive ability</subject><subject>Correlation coefficient</subject><subject>Correlation coefficients</subject><subject>Cost analysis</subject><subject>Cross-Sectional Studies</subject><subject>Economics</subject><subject>EORTC QLQ-C30</subject><subject>EQ-5D-5L</subject><subject>Evaluation</subject><subject>Fatigue</subject><subject>Female</subject><subject>Germany</subject><subject>Health services</subject><subject>Health Status</subject><subject>Health-related quality of life</subject><subject>Humans</subject><subject>Information systems</subject><subject>Likert scale</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Neoplasms - psychology</subject><subject>Patient Reported Outcome Measures</subject><subject>Patient reported outcomes</subject><subject>Patients</subject><subject>Physical training</subject><subject>Population studies</subject><subject>Preferences</subject><subject>PROMIS-29</subject><subject>PROPr</subject><subject>Psychometrics</subject><subject>QLU-C10D</subject><subject>Quality of Life</subject><subject>Quality-Adjusted Life Years</subject><subject>Questionnaires</subject><subject>Regression analysis</subject><subject>Reproducibility of Results</subject><subject>Sociodemographics</subject><subject>Subgroups</subject><subject>Surveys and Questionnaires - 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psychology</topic><topic>Patient Reported Outcome Measures</topic><topic>Patient reported outcomes</topic><topic>Patients</topic><topic>Physical training</topic><topic>Population studies</topic><topic>Preferences</topic><topic>PROMIS-29</topic><topic>PROPr</topic><topic>Psychometrics</topic><topic>QLU-C10D</topic><topic>Quality of Life</topic><topic>Quality-Adjusted Life Years</topic><topic>Questionnaires</topic><topic>Regression analysis</topic><topic>Reproducibility of Results</topic><topic>Sociodemographics</topic><topic>Subgroups</topic><topic>Surveys and Questionnaires - standards</topic><topic>Validity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Döhmen, Annika</creatorcontrib><creatorcontrib>Obbarius, Alexander</creatorcontrib><creatorcontrib>Kock, Milan</creatorcontrib><creatorcontrib>Nolte, Sandra</creatorcontrib><creatorcontrib>Sidey-Gibbons, Christopher J.</creatorcontrib><creatorcontrib>Valderas, José M.</creatorcontrib><creatorcontrib>Rohde, Jens</creatorcontrib><creatorcontrib>Rieger, Kathrin</creatorcontrib><creatorcontrib>Fischer, Felix</creatorcontrib><creatorcontrib>Keilholz, Ulrich</creatorcontrib><creatorcontrib>Rose, Matthias</creatorcontrib><creatorcontrib>Klapproth, Christoph Paul</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Health and Safety Science Abstracts (Full archive)</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Neurosciences Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Nursing & Allied Health Premium</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of clinical epidemiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Döhmen, Annika</au><au>Obbarius, Alexander</au><au>Kock, Milan</au><au>Nolte, Sandra</au><au>Sidey-Gibbons, Christopher J.</au><au>Valderas, José M.</au><au>Rohde, Jens</au><au>Rieger, Kathrin</au><au>Fischer, Felix</au><au>Keilholz, Ulrich</au><au>Rose, Matthias</au><au>Klapproth, Christoph Paul</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The EORTC QLU-C10D distinguished better between cancer patients and the general population than PROPr and EQ-5D-5L in a cross-sectional study</atitle><jtitle>Journal of clinical epidemiology</jtitle><addtitle>J Clin Epidemiol</addtitle><date>2025-01-01</date><risdate>2025</risdate><volume>177</volume><spage>111592</spage><pages>111592-</pages><artnum>111592</artnum><issn>0895-4356</issn><issn>1878-5921</issn><eissn>1878-5921</eissn><abstract>Health state utility (HSU) instruments for calculating quality-adjusted life years, such as the European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Utility - Core 10 Dimensions (QLU-C10D), derived from the EORTC QLQ-30 questionnaire, the Patient-Reported Outcome Measurement Information System (PROMIS) preference score (PROPr), and the EuroQoL-5-Dimensions-5-Levels (EQ-5D-5L), yield different HSU values due to different modeling and different underlying descriptive scales. For example the QLU-C10D includes cancer-relevant dimensions such as nausea. This study aimed to investigate how these differences in descriptive scales contribute to differences in HSU scores by comparing scores of cancer patients receiving chemotherapy to those of the general population.
EORTC QLU-C10D, PROPr, and EQ-5D-5L scores were obtained for a convenience sample of 484 outpatients of the Department of Oncology, Charité – Universitätsmedizin Berlin, Germany. Convergent and known group’s validity were assessed using Pearson's correlation and intraclass correlation coefficients (ICC). We assessed each descriptive dimension score's discriminatory power and compared them to those of the general population (n > 1000) using effect size (ES; Cohen's d) and area under the curve (AUC).
The mean scores of QLU-C10D (0.64; 95% CI 0.62-0.67), PROPr (0.38; 95% CI 0.36-0.40), and EQ-5D-5L (0.72; 95% CI 0.70-0.75) differed significantly, irrespective of sociodemographic factors, condition, or treatment. Conceptually similar descriptive scores as obtained from the HSU instruments showed varying degrees of discrimination in terms of ES and AUC between patients and the general population. The QLU-C10D and its dimensions showed the largest ES and AUC.
The QLU-C10D and its domains distinguished best between health states of the two populations, compared to the PROPr and EQ-5D-5L. As the EORTC Core Quality of Life Questionnaire (QLQ-C30) is widely used in clinical practice, its data are available for economic evaluation.
The assessment of dimensions of health-related quality of life (HRQoL), such as physical functioning or depression, is important to cancer patients and physicians for treatment and side effect monitoring. Descriptive HRQoL is measured by patient-reported outcomes measures (PROM). The European Organisation for Research and Treatment of Cancer (EORTC) QLQ-C30 questionnaire and the Patient-Reported Outcome Measurement Information System (PROMIS) are the most common PROM in the clinical HRQoL assessment. In recent years, multidimensional preference-based HRQoL measures were developed using these PROM as dimensions. These preference-based measures, also referred to as health state utility (HSU) scores, are needed for economic evaluations of treatments. The QLQ-C30's corresponding HSU score is the quality-of-life utility measure-core 10 dimensions (QLU-C10D), and PROMIS’ HSU score is the PROMIS preference score (PROPr). Both new HSU scores are frequently compared to the well-established EuroQoL-5-dimensions-5-levels (EQ-5D-5L). They all conceptualize HSU differently, as they assess different dimensions of HRQoL und use different models. Both the QLU-C10D and the PROPr have thus shown systematic differences to the EQ-5D-5L but these were largely consistent across the subgroups. Convergent and known groups validity can therefore be considered established. However, as HSU is a multidimensional construct, it remains unclear how differences in its dimensions, for example, its descriptive scales, contribute to differences in HSU scores. This is of importance as it is the descriptive scales that measure clinical HRQoL. We investigated this question by assessing each dimension’s ability to distinguish between a sample of 484 cancer patients and the German general population. We could show that the ability to distinguish depended on the domain: for example, for depression, the QLU-C10D and EQ-5D-5L distinguished clearer, while for physical function, PROMIS did. Overall, the QLU-C10D and its dimensions distinguish best between cancer patients and general population.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>39515489</pmid><doi>10.1016/j.jclinepi.2024.111592</doi><orcidid>https://orcid.org/0000-0001-6185-9423</orcidid><orcidid>https://orcid.org/0000-0002-4732-7305</orcidid><orcidid>https://orcid.org/0000-0002-9836-2557</orcidid><orcidid>https://orcid.org/0000-0002-2754-8581</orcidid><orcidid>https://orcid.org/0000-0002-8319-8379</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0895-4356 |
| ispartof | Journal of clinical epidemiology, 2025-01, Vol.177, p.111592, Article 111592 |
| issn | 0895-4356 1878-5921 1878-5921 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_3128741442 |
| source | MEDLINE; Elsevier ScienceDirect Journals |
| subjects | Adult Aged Cancer Chemotherapy Cognition & reasoning Cognitive ability Correlation coefficient Correlation coefficients Cost analysis Cross-Sectional Studies Economics EORTC QLQ-C30 EQ-5D-5L Evaluation Fatigue Female Germany Health services Health Status Health-related quality of life Humans Information systems Likert scale Male Middle Aged Neoplasms - psychology Patient Reported Outcome Measures Patient reported outcomes Patients Physical training Population studies Preferences PROMIS-29 PROPr Psychometrics QLU-C10D Quality of Life Quality-Adjusted Life Years Questionnaires Regression analysis Reproducibility of Results Sociodemographics Subgroups Surveys and Questionnaires - standards Validity |
| title | The EORTC QLU-C10D distinguished better between cancer patients and the general population than PROPr and EQ-5D-5L in a cross-sectional study |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-06T18%3A55%3A54IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=The%20EORTC%20QLU-C10D%20distinguished%20better%20between%20cancer%20patients%20and%20the%20general%20population%20than%20PROPr%20and%20EQ-5D-5L%20in%20a%20cross-sectional%20study&rft.jtitle=Journal%20of%20clinical%20epidemiology&rft.au=D%C3%B6hmen,%20Annika&rft.date=2025-01-01&rft.volume=177&rft.spage=111592&rft.pages=111592-&rft.artnum=111592&rft.issn=0895-4356&rft.eissn=1878-5921&rft_id=info:doi/10.1016/j.jclinepi.2024.111592&rft_dat=%3Cproquest_cross%3E3159992720%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3159992720&rft_id=info:pmid/39515489&rft_els_id=S0895435624003482&rfr_iscdi=true |