Christoph Paul Klapproth , Felix Fischer , Annika Doehmen , Milan Kock , Jens Rohde , Kathrin Rieger , Ullrich Keilholz , Matthias Rose , Alexander Obbarius
{"title":"The PROPr can be measured using different PROMIS domain item sets","authors":"Christoph Paul Klapproth , Felix Fischer , Annika Doehmen , Milan Kock , Jens Rohde , Kathrin Rieger , Ullrich Keilholz , Matthias Rose , Alexander Obbarius","doi":"10.1016/j.canep.2024.102658","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>The Patient-Reported Outcomes Measurement Information System (PROMIS) Preference Score (PROPr) is estimated from descriptive health assessments within the PROMIS framework. The underlying item response theory (IRT) allows researchers to measure PROMIS health domains with any subset of items that are calibrated to this domain. Consequently, this should also be true for the PROPr. We aimed to test this assumption using both an empirical and a simulation approach.</p></div><div><h3>Methods</h3><p>Empirically, we estimated 3 PROMIS Pain inference (PI) scores from 3 different item subsets in a sample of n=199 cancer patients: 4 PROMIS-29 items (estimate: θ<sub>4</sub>), the 2 original PROPr items (θ<sub>2</sub>), and 10 different items (θ<sub>10</sub>). We calculated mean differences and agreement between θ<sub>4</sub>, and θ<sub>2</sub> and θ<sub>10</sub>, respectively, and between their resulting PROPr<sub>4</sub>, PROPr<sub>2</sub>, PROPr<sub>10</sub>, using intraclass correlation coefficients (ICC) and Bland-Altman (B-A) plots with 95 %-Limits of Agreement (LoA). For the simulation, we used the IRT-model to calculate all item responses of the entire 7 PROPr domain item banks from the empirically observed PROMIS-29+cognition θ. From these simulated item banks, we chose the 2 original PROPr items per domain to calculate PROPr<sub>sim</sub> and compared it to PROPr<sub>4</sub> again using ICC and B-A plots.</p></div><div><h3>Results</h3><p>θ<sub>4</sub> vs θ<sub>10</sub> showed smaller bias (-0.012, 95 %-LoA −0.88;0.85) than θ<sub>4</sub> vs θ<sub>2</sub> (0.025, 95 %-LoA −0.95;1.00. ICC>0.85 (p<0.001) in both θ-comparisons. PROPr<sub>4</sub> vs PROPr<sub>10</sub> showed lower bias (0.0012, 95 %-LoA −0.039;0.042) than PROPr<sub>4</sub> vs PROPr<sub>2</sub> (-0.0029, 95 %-LoA −0.049;0.044). ICC>0.98 (p<0.0001) on both PROPr-comparisons. Mean PROPr<sub>sim</sub> was larger than mean PROPr<sub>4</sub> (0.0228, 95 %-LoA −0.1103; 0.1558) and ICC was 0.95 (95 %CI 0.93; 0.97).</p></div><div><h3>Conclusion</h3><p>Different item subsets can be used to estimate the PROMIS PI for calculation of the PROPr. Reduction to 2 items per domain rather than 4 does not significantly change the PROPr estimate on average. Agreements differ across the spectrum and in individual comparisons.</p></div>","PeriodicalId":56322,"journal":{"name":"Cancer Epidemiology","volume":"93 ","pages":"Article 102658"},"PeriodicalIF":2.4000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1877782124001371/pdfft?md5=ce62914936b18250df9ea55f5e9b1419&pid=1-s2.0-S1877782124001371-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer Epidemiology","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1877782124001371","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Background
The Patient-Reported Outcomes Measurement Information System (PROMIS) Preference Score (PROPr) is estimated from descriptive health assessments within the PROMIS framework. The underlying item response theory (IRT) allows researchers to measure PROMIS health domains with any subset of items that are calibrated to this domain. Consequently, this should also be true for the PROPr. We aimed to test this assumption using both an empirical and a simulation approach.
Methods
Empirically, we estimated 3 PROMIS Pain inference (PI) scores from 3 different item subsets in a sample of n=199 cancer patients: 4 PROMIS-29 items (estimate: θ4), the 2 original PROPr items (θ2), and 10 different items (θ10). We calculated mean differences and agreement between θ4, and θ2 and θ10, respectively, and between their resulting PROPr4, PROPr2, PROPr10, using intraclass correlation coefficients (ICC) and Bland-Altman (B-A) plots with 95 %-Limits of Agreement (LoA). For the simulation, we used the IRT-model to calculate all item responses of the entire 7 PROPr domain item banks from the empirically observed PROMIS-29+cognition θ. From these simulated item banks, we chose the 2 original PROPr items per domain to calculate PROPrsim and compared it to PROPr4 again using ICC and B-A plots.
Results
θ4 vs θ10 showed smaller bias (-0.012, 95 %-LoA −0.88;0.85) than θ4 vs θ2 (0.025, 95 %-LoA −0.95;1.00. ICC>0.85 (p<0.001) in both θ-comparisons. PROPr4 vs PROPr10 showed lower bias (0.0012, 95 %-LoA −0.039;0.042) than PROPr4 vs PROPr2 (-0.0029, 95 %-LoA −0.049;0.044). ICC>0.98 (p<0.0001) on both PROPr-comparisons. Mean PROPrsim was larger than mean PROPr4 (0.0228, 95 %-LoA −0.1103; 0.1558) and ICC was 0.95 (95 %CI 0.93; 0.97).
Conclusion
Different item subsets can be used to estimate the PROMIS PI for calculation of the PROPr. Reduction to 2 items per domain rather than 4 does not significantly change the PROPr estimate on average. Agreements differ across the spectrum and in individual comparisons.
期刊介绍:
Cancer Epidemiology is dedicated to increasing understanding about cancer causes, prevention and control. The scope of the journal embraces all aspects of cancer epidemiology including:
• Descriptive epidemiology
• Studies of risk factors for disease initiation, development and prognosis
• Screening and early detection
• Prevention and control
• Methodological issues
The journal publishes original research articles (full length and short reports), systematic reviews and meta-analyses, editorials, commentaries and letters to the editor commenting on previously published research.