Assessing baseline dependency of anchor-based minimal important change (MIC): don’t stratify on the baseline score
Purpose The minimal important change (MIC) of a patient-reported outcome measure (PROM) is often suspected to be baseline dependent, typically in the sense that patients who are in a poorer baseline health condition need greater improvement to qualify as minimally important. Testing MIC baseline dep...
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Veröffentlicht in: | Quality of life research 2021-10, Vol.30 (10), p.2773-2782 |
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creator | Terluin, Berend Roos, Ewa M. Terwee, Caroline B. Thorlund, Jonas B. Ingelsrud, Lina H. |
description | Purpose
The minimal important change (MIC) of a patient-reported outcome measure (PROM) is often suspected to be baseline dependent, typically in the sense that patients who are in a poorer baseline health condition need greater improvement to qualify as minimally important. Testing MIC baseline dependency is commonly performed by creating two or more subgroups, stratified on the baseline PROM score. This study’s purpose was to show that this practice produces biased subgroup MIC estimates resulting in spurious MIC baseline dependency, and to develop alternative methods to evaluate MIC baseline dependency.
Methods
Datasets with PROM baseline and follow-up scores and transition ratings were simulated with and without MIC baseline dependency. Mean change MICs, ROC-based MICs, predictive MICs, and adjusted MICs were estimated before and after stratification on the baseline score. Three alternative methods were developed and evaluated. The methods were applied in a real data example for illustration.
Results
Baseline stratification resulted in biased subgroup MIC estimates and the false impression of MIC baseline dependency, due to redistribution of measurement error. Two of the alternative methods require a second baseline measurement with the same PROM or another correlated PROM. The third method involves the construction of two parallel tests based on splitting the PROM’s item set. Two methods could be applied to the real data.
Conclusion
MIC baseline dependency should not be tested in subgroups based on stratification on the baseline PROM score. Instead, one or more of the suggested alternative methods should be used. |
doi_str_mv | 10.1007/s11136-021-02886-2 |
format | Article |
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The minimal important change (MIC) of a patient-reported outcome measure (PROM) is often suspected to be baseline dependent, typically in the sense that patients who are in a poorer baseline health condition need greater improvement to qualify as minimally important. Testing MIC baseline dependency is commonly performed by creating two or more subgroups, stratified on the baseline PROM score. This study’s purpose was to show that this practice produces biased subgroup MIC estimates resulting in spurious MIC baseline dependency, and to develop alternative methods to evaluate MIC baseline dependency.
Methods
Datasets with PROM baseline and follow-up scores and transition ratings were simulated with and without MIC baseline dependency. Mean change MICs, ROC-based MICs, predictive MICs, and adjusted MICs were estimated before and after stratification on the baseline score. Three alternative methods were developed and evaluated. The methods were applied in a real data example for illustration.
Results
Baseline stratification resulted in biased subgroup MIC estimates and the false impression of MIC baseline dependency, due to redistribution of measurement error. Two of the alternative methods require a second baseline measurement with the same PROM or another correlated PROM. The third method involves the construction of two parallel tests based on splitting the PROM’s item set. Two methods could be applied to the real data.
Conclusion
MIC baseline dependency should not be tested in subgroups based on stratification on the baseline PROM score. Instead, one or more of the suggested alternative methods should be used.</description><identifier>ISSN: 0962-9343</identifier><identifier>EISSN: 1573-2649</identifier><identifier>DOI: 10.1007/s11136-021-02886-2</identifier><identifier>PMID: 34041680</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Estimates ; Humans ; Medical research ; Medicine ; Medicine & Public Health ; Methods ; Patients ; Public Health ; Quality of life ; Quality of Life - psychology ; Quality of Life Research ; Random variables ; Range of Motion, Articular ; ROC Curve ; Sociology</subject><ispartof>Quality of life research, 2021-10, Vol.30 (10), p.2773-2782</ispartof><rights>The Author(s) 2021</rights><rights>2021. The Author(s).</rights><rights>The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c474t-d006e08c20928311fe62cecf988f2cd12e1888c6f1fc36163c4f25513fdb848c3</citedby><cites>FETCH-LOGICAL-c474t-d006e08c20928311fe62cecf988f2cd12e1888c6f1fc36163c4f25513fdb848c3</cites><orcidid>0000-0002-8944-5238</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11136-021-02886-2$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11136-021-02886-2$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>230,314,780,784,885,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34041680$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Terluin, Berend</creatorcontrib><creatorcontrib>Roos, Ewa M.</creatorcontrib><creatorcontrib>Terwee, Caroline B.</creatorcontrib><creatorcontrib>Thorlund, Jonas B.</creatorcontrib><creatorcontrib>Ingelsrud, Lina H.</creatorcontrib><title>Assessing baseline dependency of anchor-based minimal important change (MIC): don’t stratify on the baseline score</title><title>Quality of life research</title><addtitle>Qual Life Res</addtitle><addtitle>Qual Life Res</addtitle><description>Purpose
The minimal important change (MIC) of a patient-reported outcome measure (PROM) is often suspected to be baseline dependent, typically in the sense that patients who are in a poorer baseline health condition need greater improvement to qualify as minimally important. Testing MIC baseline dependency is commonly performed by creating two or more subgroups, stratified on the baseline PROM score. This study’s purpose was to show that this practice produces biased subgroup MIC estimates resulting in spurious MIC baseline dependency, and to develop alternative methods to evaluate MIC baseline dependency.
Methods
Datasets with PROM baseline and follow-up scores and transition ratings were simulated with and without MIC baseline dependency. Mean change MICs, ROC-based MICs, predictive MICs, and adjusted MICs were estimated before and after stratification on the baseline score. Three alternative methods were developed and evaluated. The methods were applied in a real data example for illustration.
Results
Baseline stratification resulted in biased subgroup MIC estimates and the false impression of MIC baseline dependency, due to redistribution of measurement error. Two of the alternative methods require a second baseline measurement with the same PROM or another correlated PROM. The third method involves the construction of two parallel tests based on splitting the PROM’s item set. Two methods could be applied to the real data.
Conclusion
MIC baseline dependency should not be tested in subgroups based on stratification on the baseline PROM score. Instead, one or more of the suggested alternative methods should be used.</description><subject>Estimates</subject><subject>Humans</subject><subject>Medical research</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Methods</subject><subject>Patients</subject><subject>Public Health</subject><subject>Quality of life</subject><subject>Quality of Life - psychology</subject><subject>Quality of Life Research</subject><subject>Random variables</subject><subject>Range of Motion, Articular</subject><subject>ROC Curve</subject><subject>Sociology</subject><issn>0962-9343</issn><issn>1573-2649</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kb9uFDEQhy0EIkfgBSiQJZpQLIz_rNdLESk6BYgURAO15fOO7zbatQ_bh5SO1-D1eJL4uJAABYXlYr75POMfIc8ZvGYA3ZvMGBOqAc7q0Vo1_AFZsLYTDVeyf0gW0Cve9EKKI_Ik5ysA0D3wx-RISJBMaViQcpYz5jyGNV3ZjNMYkA64xTBgcNc0emqD28TU7KsDnccwznai47yNqdhQqNvYsEZ68vFi-eotHWL4-f1HobkkW0ZfBYGWDd67s4sJn5JH3k4Zn93ex-TLu_PPyw_N5af3F8uzy8bJTpZmAFAI2nHouRaMeVTcofO91p67gXFkWmunPPNOKKaEk563LRN-WGmpnTgmpwfvdreacXAY6liT2aa6Q7o20Y7m70oYN2Ydv5nazZjuquDkVpDi1x3mYuYxO5wmGzDusuGtEGL_47yiL_9Br-IuhbpepbpOgZQtVIofKJdizgn93TAMzD5UcwjV1FDNr1DNXv3izzXuWn6nWAFxAHIt1TTS_dv_0d4AHqSu6A</recordid><startdate>20211001</startdate><enddate>20211001</enddate><creator>Terluin, Berend</creator><creator>Roos, Ewa M.</creator><creator>Terwee, Caroline B.</creator><creator>Thorlund, Jonas B.</creator><creator>Ingelsrud, Lina H.</creator><general>Springer International Publishing</general><general>Springer Nature B.V</general><scope>C6C</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7WY</scope><scope>7WZ</scope><scope>7X7</scope><scope>7XB</scope><scope>87Z</scope><scope>88E</scope><scope>88G</scope><scope>8AO</scope><scope>8C1</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8FL</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FRNLG</scope><scope>FYUFA</scope><scope>F~G</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>K60</scope><scope>K6~</scope><scope>K9.</scope><scope>KB0</scope><scope>L.-</scope><scope>M0C</scope><scope>M0S</scope><scope>M1P</scope><scope>M2M</scope><scope>NAPCQ</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PSYQQ</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-8944-5238</orcidid></search><sort><creationdate>20211001</creationdate><title>Assessing baseline dependency of anchor-based minimal important change (MIC): don’t stratify on the baseline score</title><author>Terluin, Berend ; Roos, Ewa M. ; Terwee, Caroline B. ; Thorlund, Jonas B. ; Ingelsrud, Lina H.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c474t-d006e08c20928311fe62cecf988f2cd12e1888c6f1fc36163c4f25513fdb848c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Estimates</topic><topic>Humans</topic><topic>Medical research</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Methods</topic><topic>Patients</topic><topic>Public Health</topic><topic>Quality of life</topic><topic>Quality of Life - psychology</topic><topic>Quality of Life Research</topic><topic>Random variables</topic><topic>Range of Motion, Articular</topic><topic>ROC Curve</topic><topic>Sociology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Terluin, Berend</creatorcontrib><creatorcontrib>Roos, Ewa M.</creatorcontrib><creatorcontrib>Terwee, Caroline B.</creatorcontrib><creatorcontrib>Thorlund, Jonas B.</creatorcontrib><creatorcontrib>Ingelsrud, Lina H.</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database (ProQuest)</collection><collection>ABI/INFORM Collection (ProQuest)</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Health and Medical</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection</collection><collection>Medical Database (Alumni Edition)</collection><collection>Psychology Database (Alumni)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database (Proquest)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Business Premium Collection (Alumni)</collection><collection>Health Research Premium Collection</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Global</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>Psychology Database (ProQuest)</collection><collection>Nursing & Allied Health Premium</collection><collection>One Business (ProQuest)</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest One Psychology</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Quality of life research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Terluin, Berend</au><au>Roos, Ewa M.</au><au>Terwee, Caroline B.</au><au>Thorlund, Jonas B.</au><au>Ingelsrud, Lina H.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Assessing baseline dependency of anchor-based minimal important change (MIC): don’t stratify on the baseline score</atitle><jtitle>Quality of life research</jtitle><stitle>Qual Life Res</stitle><addtitle>Qual Life Res</addtitle><date>2021-10-01</date><risdate>2021</risdate><volume>30</volume><issue>10</issue><spage>2773</spage><epage>2782</epage><pages>2773-2782</pages><issn>0962-9343</issn><eissn>1573-2649</eissn><abstract>Purpose
The minimal important change (MIC) of a patient-reported outcome measure (PROM) is often suspected to be baseline dependent, typically in the sense that patients who are in a poorer baseline health condition need greater improvement to qualify as minimally important. Testing MIC baseline dependency is commonly performed by creating two or more subgroups, stratified on the baseline PROM score. This study’s purpose was to show that this practice produces biased subgroup MIC estimates resulting in spurious MIC baseline dependency, and to develop alternative methods to evaluate MIC baseline dependency.
Methods
Datasets with PROM baseline and follow-up scores and transition ratings were simulated with and without MIC baseline dependency. Mean change MICs, ROC-based MICs, predictive MICs, and adjusted MICs were estimated before and after stratification on the baseline score. Three alternative methods were developed and evaluated. The methods were applied in a real data example for illustration.
Results
Baseline stratification resulted in biased subgroup MIC estimates and the false impression of MIC baseline dependency, due to redistribution of measurement error. Two of the alternative methods require a second baseline measurement with the same PROM or another correlated PROM. The third method involves the construction of two parallel tests based on splitting the PROM’s item set. Two methods could be applied to the real data.
Conclusion
MIC baseline dependency should not be tested in subgroups based on stratification on the baseline PROM score. Instead, one or more of the suggested alternative methods should be used.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><pmid>34041680</pmid><doi>10.1007/s11136-021-02886-2</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-8944-5238</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Estimates Humans Medical research Medicine Medicine & Public Health Methods Patients Public Health Quality of life Quality of Life - psychology Quality of Life Research Random variables Range of Motion, Articular ROC Curve Sociology |
title | Assessing baseline dependency of anchor-based minimal important change (MIC): don’t stratify on the baseline score |
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