Richard Sawatzky, Mathilde G E Verdam, Yseulys Dubuy, Tolulope T Sajobi, Lara Russell, Oluwagbohunmi A Awosoga, Ayoola Ademola, Jan R Böhnke, Oluwaseyi Lawal, Anita Brobbey, Amélie Anota, Lisa M Lix, Mirjam A G Sprangers, Véronique Sébille
{"title":"使用患者报告的结果测量的定量研究的反应转移结果:荟萃回归分析。","authors":"Richard Sawatzky, Mathilde G E Verdam, Yseulys Dubuy, Tolulope T Sajobi, Lara Russell, Oluwagbohunmi A Awosoga, Ayoola Ademola, Jan R Böhnke, Oluwaseyi Lawal, Anita Brobbey, Amélie Anota, Lisa M Lix, Mirjam A G Sprangers, Véronique Sébille","doi":"10.1007/s11136-024-03867-x","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Our objectives were to identify characteristics of response shift studies using patient-reported outcomes (PROMs) that explain variability in (1) the detection and (2) the magnitude of response shift effects.</p><p><strong>Methods: </strong>We conducted a systematic review of quantitative studies published before June 2023. First, two-level multivariable logistic regression models (effect- and sample-levels) were used to explain variability in the probability of finding a response shift effect. Second, variability in effect sizes (standardized mean differences) was investigated with 3-level meta-regression models (participant-, effect- and sample-levels). Explanatory variables identified via the purposeful selection methodology included response shift method and type, and population-, study design-, PROM- and study-quality characteristics.</p><p><strong>Results: </strong>First, logistic regression analysis of 5597 effects from 206 samples in 171 studies identified variables explaining 41.5% of the effect-level variance, while no variables explained sample-level variance. The average probability of response shift detection is 0.20 (95% CI: 0.17-0.28). Variation in detection was predominantly explained by response shift methods and type (recalibration vs. reprioritization/reconceptualization). Second, effect sizes were analyzed for 769 effects from 114 samples and 96 studies based on the then-test and structural equation modeling methods. Meta-regression analysis identified variables explaining 11.6% of the effect-level variance and 26.4% of the sample-level variance, with an average effect size of 0.30 (95% CI: 0.26-0.34).</p><p><strong>Conclusion: </strong>Response shift detection is influenced by study design and methods. Insights into the variables explaining response shift effects can be used to interpret results of other comparable studies using PROMs and inform the design of future response shift studies.</p>","PeriodicalId":20748,"journal":{"name":"Quality of Life Research","volume":" ","pages":"1393-1406"},"PeriodicalIF":3.3000,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12064579/pdf/","citationCount":"0","resultStr":"{\"title\":\"Response shift results of quantitative research using patient-reported outcome measures: a meta-regression analysis.\",\"authors\":\"Richard Sawatzky, Mathilde G E Verdam, Yseulys Dubuy, Tolulope T Sajobi, Lara Russell, Oluwagbohunmi A Awosoga, Ayoola Ademola, Jan R Böhnke, Oluwaseyi Lawal, Anita Brobbey, Amélie Anota, Lisa M Lix, Mirjam A G Sprangers, Véronique Sébille\",\"doi\":\"10.1007/s11136-024-03867-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>Our objectives were to identify characteristics of response shift studies using patient-reported outcomes (PROMs) that explain variability in (1) the detection and (2) the magnitude of response shift effects.</p><p><strong>Methods: </strong>We conducted a systematic review of quantitative studies published before June 2023. First, two-level multivariable logistic regression models (effect- and sample-levels) were used to explain variability in the probability of finding a response shift effect. Second, variability in effect sizes (standardized mean differences) was investigated with 3-level meta-regression models (participant-, effect- and sample-levels). Explanatory variables identified via the purposeful selection methodology included response shift method and type, and population-, study design-, PROM- and study-quality characteristics.</p><p><strong>Results: </strong>First, logistic regression analysis of 5597 effects from 206 samples in 171 studies identified variables explaining 41.5% of the effect-level variance, while no variables explained sample-level variance. The average probability of response shift detection is 0.20 (95% CI: 0.17-0.28). Variation in detection was predominantly explained by response shift methods and type (recalibration vs. reprioritization/reconceptualization). Second, effect sizes were analyzed for 769 effects from 114 samples and 96 studies based on the then-test and structural equation modeling methods. Meta-regression analysis identified variables explaining 11.6% of the effect-level variance and 26.4% of the sample-level variance, with an average effect size of 0.30 (95% CI: 0.26-0.34).</p><p><strong>Conclusion: </strong>Response shift detection is influenced by study design and methods. Insights into the variables explaining response shift effects can be used to interpret results of other comparable studies using PROMs and inform the design of future response shift studies.</p>\",\"PeriodicalId\":20748,\"journal\":{\"name\":\"Quality of Life Research\",\"volume\":\" \",\"pages\":\"1393-1406\"},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2025-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12064579/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Quality of Life Research\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s11136-024-03867-x\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/12/9 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Quality of Life Research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s11136-024-03867-x","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/12/9 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
Response shift results of quantitative research using patient-reported outcome measures: a meta-regression analysis.
Purpose: Our objectives were to identify characteristics of response shift studies using patient-reported outcomes (PROMs) that explain variability in (1) the detection and (2) the magnitude of response shift effects.
Methods: We conducted a systematic review of quantitative studies published before June 2023. First, two-level multivariable logistic regression models (effect- and sample-levels) were used to explain variability in the probability of finding a response shift effect. Second, variability in effect sizes (standardized mean differences) was investigated with 3-level meta-regression models (participant-, effect- and sample-levels). Explanatory variables identified via the purposeful selection methodology included response shift method and type, and population-, study design-, PROM- and study-quality characteristics.
Results: First, logistic regression analysis of 5597 effects from 206 samples in 171 studies identified variables explaining 41.5% of the effect-level variance, while no variables explained sample-level variance. The average probability of response shift detection is 0.20 (95% CI: 0.17-0.28). Variation in detection was predominantly explained by response shift methods and type (recalibration vs. reprioritization/reconceptualization). Second, effect sizes were analyzed for 769 effects from 114 samples and 96 studies based on the then-test and structural equation modeling methods. Meta-regression analysis identified variables explaining 11.6% of the effect-level variance and 26.4% of the sample-level variance, with an average effect size of 0.30 (95% CI: 0.26-0.34).
Conclusion: Response shift detection is influenced by study design and methods. Insights into the variables explaining response shift effects can be used to interpret results of other comparable studies using PROMs and inform the design of future response shift studies.
期刊介绍:
Quality of Life Research is an international, multidisciplinary journal devoted to the rapid communication of original research, theoretical articles and methodological reports related to the field of quality of life, in all the health sciences. The journal also offers editorials, literature, book and software reviews, correspondence and abstracts of conferences.
Quality of life has become a prominent issue in biometry, philosophy, social science, clinical medicine, health services and outcomes research. The journal''s scope reflects the wide application of quality of life assessment and research in the biological and social sciences. All original work is subject to peer review for originality, scientific quality and relevance to a broad readership.
This is an official journal of the International Society of Quality of Life Research.