Improving Power and Accuracy in Randomized Controlled Trials of Pain Treatments by Accounting for Concurrent Analgesic Use
•Not accounting for analgesic use may decrease power and accuracy in pain trials.•This study examined several methods to account for analgesic use.•Most currently used methods resulted in decreased power and accuracy.•A novel outcome that accounts for analgesic use optimized power and minimized bias...
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| Veröffentlicht in: | The journal of pain 2023-02, Vol.24 (2), p.332-344 |
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| container_issue | 2 |
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| container_title | The journal of pain |
| container_volume | 24 |
| creator | Suri, Pradeep Heagerty, Patrick J. Korpak, Anna Jensen, Mark P. Gold, Laura S. Chan, Kwun C.G. Timmons, Andrew Friedly, Janna Jarvik, Jeffrey G. Baraff, Aaron |
| description | •Not accounting for analgesic use may decrease power and accuracy in pain trials.•This study examined several methods to account for analgesic use.•Most currently used methods resulted in decreased power and accuracy.•A novel outcome that accounts for analgesic use optimized power and minimized bias.
The 0 to 10 numeric rating scale of pain intensity is a standard outcome in randomized controlled trials (RCTs) of pain treatments. For individuals taking analgesics, there may be a disparity between “observed” pain intensity (pain intensity with concurrent analgesic use) and pain intensity without concurrent analgesic use (what the numeric rating scale would be had analgesics not been taken). Using a contemporary causal inference framework, we compare analytic methods that can potentially account for concurrent analgesic use, first in statistical simulations, and second in analyses of real (non-simulated) data from an RCT of lumbar epidural steroid injections. The default analytic method was ignoring analgesic use, which is the most common approach in pain RCTs. Compared to ignoring analgesic use and other analytic methods, simulations showed that a quantitative pain and analgesia composite outcome based on adding 1.5 points to pain intensity for those who were taking an analgesic (the QPAC1.5) optimized power and minimized bias. Analyses of real RCT data supported the results of the simulations, showing greater power with analysis of the QPAC1.5 as compared to ignoring analgesic use and most other methods examined. We propose alternative methods that should be considered in the analysis of pain RCTs.
This article presents the conceptual framework behind a new quantitative pain and analgesia composite outcome, the QPAC1.5, and the results of statistical simulations and analyses of trial data supporting improvements in power and bias using the QPAC1.5. Methods of this type should be considered in the analysis of pain RCTs. |
| doi_str_mv | 10.1016/j.jpain.2022.09.017 |
| format | Article |
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The 0 to 10 numeric rating scale of pain intensity is a standard outcome in randomized controlled trials (RCTs) of pain treatments. For individuals taking analgesics, there may be a disparity between “observed” pain intensity (pain intensity with concurrent analgesic use) and pain intensity without concurrent analgesic use (what the numeric rating scale would be had analgesics not been taken). Using a contemporary causal inference framework, we compare analytic methods that can potentially account for concurrent analgesic use, first in statistical simulations, and second in analyses of real (non-simulated) data from an RCT of lumbar epidural steroid injections. The default analytic method was ignoring analgesic use, which is the most common approach in pain RCTs. Compared to ignoring analgesic use and other analytic methods, simulations showed that a quantitative pain and analgesia composite outcome based on adding 1.5 points to pain intensity for those who were taking an analgesic (the QPAC1.5) optimized power and minimized bias. Analyses of real RCT data supported the results of the simulations, showing greater power with analysis of the QPAC1.5 as compared to ignoring analgesic use and most other methods examined. We propose alternative methods that should be considered in the analysis of pain RCTs.
This article presents the conceptual framework behind a new quantitative pain and analgesia composite outcome, the QPAC1.5, and the results of statistical simulations and analyses of trial data supporting improvements in power and bias using the QPAC1.5. Methods of this type should be considered in the analysis of pain RCTs.</description><identifier>ISSN: 1526-5900</identifier><identifier>EISSN: 1528-8447</identifier><identifier>DOI: 10.1016/j.jpain.2022.09.017</identifier><identifier>PMID: 36220482</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Analgesics - therapeutic use ; Analgesics, Opioid ; Humans ; low back pain ; opioids ; outcomes ; Pain ; Pain - drug therapy ; Pain Management - methods ; Pain, Postoperative - drug therapy ; power ; Randomized Controlled Trials as Topic</subject><ispartof>The journal of pain, 2023-02, Vol.24 (2), p.332-344</ispartof><rights>2022</rights><rights>Published by Elsevier Inc.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c459t-bfeaa5401d227818c623b8daafdcd51d7248efcb92c41f640f698e81e2ee228f3</citedby><cites>FETCH-LOGICAL-c459t-bfeaa5401d227818c623b8daafdcd51d7248efcb92c41f640f698e81e2ee228f3</cites><orcidid>0000-0002-3403-6007 ; 0000-0002-4289-5231 ; 0000-0002-9449-4165</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.jpain.2022.09.017$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>230,315,782,786,887,3554,27933,27934,46004</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36220482$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Suri, Pradeep</creatorcontrib><creatorcontrib>Heagerty, Patrick J.</creatorcontrib><creatorcontrib>Korpak, Anna</creatorcontrib><creatorcontrib>Jensen, Mark P.</creatorcontrib><creatorcontrib>Gold, Laura S.</creatorcontrib><creatorcontrib>Chan, Kwun C.G.</creatorcontrib><creatorcontrib>Timmons, Andrew</creatorcontrib><creatorcontrib>Friedly, Janna</creatorcontrib><creatorcontrib>Jarvik, Jeffrey G.</creatorcontrib><creatorcontrib>Baraff, Aaron</creatorcontrib><title>Improving Power and Accuracy in Randomized Controlled Trials of Pain Treatments by Accounting for Concurrent Analgesic Use</title><title>The journal of pain</title><addtitle>J Pain</addtitle><description>•Not accounting for analgesic use may decrease power and accuracy in pain trials.•This study examined several methods to account for analgesic use.•Most currently used methods resulted in decreased power and accuracy.•A novel outcome that accounts for analgesic use optimized power and minimized bias.
The 0 to 10 numeric rating scale of pain intensity is a standard outcome in randomized controlled trials (RCTs) of pain treatments. For individuals taking analgesics, there may be a disparity between “observed” pain intensity (pain intensity with concurrent analgesic use) and pain intensity without concurrent analgesic use (what the numeric rating scale would be had analgesics not been taken). Using a contemporary causal inference framework, we compare analytic methods that can potentially account for concurrent analgesic use, first in statistical simulations, and second in analyses of real (non-simulated) data from an RCT of lumbar epidural steroid injections. The default analytic method was ignoring analgesic use, which is the most common approach in pain RCTs. Compared to ignoring analgesic use and other analytic methods, simulations showed that a quantitative pain and analgesia composite outcome based on adding 1.5 points to pain intensity for those who were taking an analgesic (the QPAC1.5) optimized power and minimized bias. Analyses of real RCT data supported the results of the simulations, showing greater power with analysis of the QPAC1.5 as compared to ignoring analgesic use and most other methods examined. We propose alternative methods that should be considered in the analysis of pain RCTs.
This article presents the conceptual framework behind a new quantitative pain and analgesia composite outcome, the QPAC1.5, and the results of statistical simulations and analyses of trial data supporting improvements in power and bias using the QPAC1.5. Methods of this type should be considered in the analysis of pain RCTs.</description><subject>Analgesics - therapeutic use</subject><subject>Analgesics, Opioid</subject><subject>Humans</subject><subject>low back pain</subject><subject>opioids</subject><subject>outcomes</subject><subject>Pain</subject><subject>Pain - drug therapy</subject><subject>Pain Management - methods</subject><subject>Pain, Postoperative - drug therapy</subject><subject>power</subject><subject>Randomized Controlled Trials as Topic</subject><issn>1526-5900</issn><issn>1528-8447</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9UU1r3DAQNaWlSZP8gkLRsRe7kizb0qGFZelHINBQkrOQpdFWiy1tJXvD5tdXzqYhufQ0M5r33jz0iuI9wRXBpP20rbY75XxFMaUVFhUm3avilDSUl5yx7vVD35aNwPikeJfSFmNCmq57W5zULaWYcXpa3F-Ouxj2zm_QdbiDiJQ3aKX1HJU-IOfRr_wQRncPBq2Dn2IYhtzeRKeGhIJF19lCHkFNI_gpof6w0MPsp0XThrjQslzMW7TyathAchrdJjgv3tgsAheP9ay4_fb1Zv2jvPr5_XK9uio1a8RU9haUahgmhtKOE65bWvfcKGWNNg0xHWUcrO4F1YzYlmHbCg6cAAWglNv6rPhy1N3N_QhGZyNRDXIX3ajiQQbl5MuNd7_lJuyl4IJj0WSBj48CMfyZIU1ydEnDMCgPYU6SZguUEczrDK2PUB1DShHs0xmC5ZKa3MqH1OSSmsRC5tQy68Nzh0-cfzFlwOcjAPI_7R1EmbQDr8G4CHqSJrj_HvgLx-ytUw</recordid><startdate>20230201</startdate><enddate>20230201</enddate><creator>Suri, Pradeep</creator><creator>Heagerty, Patrick J.</creator><creator>Korpak, Anna</creator><creator>Jensen, Mark P.</creator><creator>Gold, Laura S.</creator><creator>Chan, Kwun C.G.</creator><creator>Timmons, Andrew</creator><creator>Friedly, Janna</creator><creator>Jarvik, Jeffrey G.</creator><creator>Baraff, Aaron</creator><general>Elsevier Inc</general><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>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-3403-6007</orcidid><orcidid>https://orcid.org/0000-0002-4289-5231</orcidid><orcidid>https://orcid.org/0000-0002-9449-4165</orcidid></search><sort><creationdate>20230201</creationdate><title>Improving Power and Accuracy in Randomized Controlled Trials of Pain Treatments by Accounting for Concurrent Analgesic Use</title><author>Suri, Pradeep ; Heagerty, Patrick J. ; Korpak, Anna ; Jensen, Mark P. ; Gold, Laura S. ; Chan, Kwun C.G. ; Timmons, Andrew ; Friedly, Janna ; Jarvik, Jeffrey G. ; Baraff, Aaron</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c459t-bfeaa5401d227818c623b8daafdcd51d7248efcb92c41f640f698e81e2ee228f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Analgesics - therapeutic use</topic><topic>Analgesics, Opioid</topic><topic>Humans</topic><topic>low back pain</topic><topic>opioids</topic><topic>outcomes</topic><topic>Pain</topic><topic>Pain - drug therapy</topic><topic>Pain Management - methods</topic><topic>Pain, Postoperative - drug therapy</topic><topic>power</topic><topic>Randomized Controlled Trials as Topic</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Suri, Pradeep</creatorcontrib><creatorcontrib>Heagerty, Patrick J.</creatorcontrib><creatorcontrib>Korpak, Anna</creatorcontrib><creatorcontrib>Jensen, Mark P.</creatorcontrib><creatorcontrib>Gold, Laura S.</creatorcontrib><creatorcontrib>Chan, Kwun C.G.</creatorcontrib><creatorcontrib>Timmons, Andrew</creatorcontrib><creatorcontrib>Friedly, Janna</creatorcontrib><creatorcontrib>Jarvik, Jeffrey G.</creatorcontrib><creatorcontrib>Baraff, Aaron</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>The journal of pain</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Suri, Pradeep</au><au>Heagerty, Patrick J.</au><au>Korpak, Anna</au><au>Jensen, Mark P.</au><au>Gold, Laura S.</au><au>Chan, Kwun C.G.</au><au>Timmons, Andrew</au><au>Friedly, Janna</au><au>Jarvik, Jeffrey G.</au><au>Baraff, Aaron</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Improving Power and Accuracy in Randomized Controlled Trials of Pain Treatments by Accounting for Concurrent Analgesic Use</atitle><jtitle>The journal of pain</jtitle><addtitle>J Pain</addtitle><date>2023-02-01</date><risdate>2023</risdate><volume>24</volume><issue>2</issue><spage>332</spage><epage>344</epage><pages>332-344</pages><issn>1526-5900</issn><eissn>1528-8447</eissn><abstract>•Not accounting for analgesic use may decrease power and accuracy in pain trials.•This study examined several methods to account for analgesic use.•Most currently used methods resulted in decreased power and accuracy.•A novel outcome that accounts for analgesic use optimized power and minimized bias.
The 0 to 10 numeric rating scale of pain intensity is a standard outcome in randomized controlled trials (RCTs) of pain treatments. For individuals taking analgesics, there may be a disparity between “observed” pain intensity (pain intensity with concurrent analgesic use) and pain intensity without concurrent analgesic use (what the numeric rating scale would be had analgesics not been taken). Using a contemporary causal inference framework, we compare analytic methods that can potentially account for concurrent analgesic use, first in statistical simulations, and second in analyses of real (non-simulated) data from an RCT of lumbar epidural steroid injections. The default analytic method was ignoring analgesic use, which is the most common approach in pain RCTs. Compared to ignoring analgesic use and other analytic methods, simulations showed that a quantitative pain and analgesia composite outcome based on adding 1.5 points to pain intensity for those who were taking an analgesic (the QPAC1.5) optimized power and minimized bias. Analyses of real RCT data supported the results of the simulations, showing greater power with analysis of the QPAC1.5 as compared to ignoring analgesic use and most other methods examined. We propose alternative methods that should be considered in the analysis of pain RCTs.
This article presents the conceptual framework behind a new quantitative pain and analgesia composite outcome, the QPAC1.5, and the results of statistical simulations and analyses of trial data supporting improvements in power and bias using the QPAC1.5. Methods of this type should be considered in the analysis of pain RCTs.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>36220482</pmid><doi>10.1016/j.jpain.2022.09.017</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0002-3403-6007</orcidid><orcidid>https://orcid.org/0000-0002-4289-5231</orcidid><orcidid>https://orcid.org/0000-0002-9449-4165</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | The journal of pain, 2023-02, Vol.24 (2), p.332-344 |
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| subjects | Analgesics - therapeutic use Analgesics, Opioid Humans low back pain opioids outcomes Pain Pain - drug therapy Pain Management - methods Pain, Postoperative - drug therapy power Randomized Controlled Trials as Topic |
| title | Improving Power and Accuracy in Randomized Controlled Trials of Pain Treatments by Accounting for Concurrent Analgesic Use |
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