Statistical Challenges When Analyzing SARS-CoV-2 RNA Measurements Below the Assay Limit of Quantification in COVID-19 Clinical Trials
Abstract Most clinical trials evaluating coronavirus disease 2019 (COVID-19) therapeutics include assessments of antiviral activity. In recently completed outpatient trials, changes in nasal severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA levels from baseline were commonly assessed...
Gespeichert in:
| Veröffentlicht in: | The Journal of infectious diseases 2023-08, Vol.228 (Supplement_2), p.S101-S110 |
|---|---|
| Hauptverfasser: | , , , , , , , , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | S110 |
|---|---|
| container_issue | Supplement_2 |
| container_start_page | S101 |
| container_title | The Journal of infectious diseases |
| container_volume | 228 |
| creator | Moser, Carlee B Chew, Kara W Giganti, Mark J Li, Jonathan Z Aga, Evgenia Ritz, Justin Greninger, Alexander L Javan, Arzhang Cyrus Bender Ignacio, Rachel Daar, Eric S Wohl, David A Currier, Judith S Eron, Joseph J Smith, Davey M Hughes, Michael D |
| description | Abstract
Most clinical trials evaluating coronavirus disease 2019 (COVID-19) therapeutics include assessments of antiviral activity. In recently completed outpatient trials, changes in nasal severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA levels from baseline were commonly assessed using analysis of covariance (ANCOVA) or mixed models for repeated measures (MMRM) with single imputation for results below assay lower limits of quantification (LLoQ). Analyzing changes in viral RNA levels with singly imputed values can lead to biased estimates of treatment effects. In this article, using an illustrative example from the ACTIV-2 trial, we highlight potential pitfalls of imputation when using ANCOVA or MMRM methods, and illustrate how these methods can be used when considering values |
| doi_str_mv | 10.1093/infdis/jiad285 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2859602673</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><oup_id>10.1093/infdis/jiad285</oup_id><sourcerecordid>2859602673</sourcerecordid><originalsourceid>FETCH-LOGICAL-c397t-1095c773060a41bfb20c766ec684f0fb3df7c7f211bae4da1d675a3f1aa5d65a3</originalsourceid><addsrcrecordid>eNqFkc1P3DAQxa2qVVmg1x4rS72UQ8AfG3tzDOkHSAuoLNBjNEls1ivH3saOquXO_13DLj30wunN4TdvNO8h9JGSY0oKfmKc7kw4WRno2Cx_gyY05zITgvK3aEIIYxmdFcUe2g9hRQiZciHfoz0uRU4YzyfocREhmhBNCxZXS7BWuXsV8K-lcrh0YDcPxt3jRXm9yCp_lzF8fVniCwVhHFSvXAz4VFn_B8elwmUIsMFz05uIvcY_R3DR6GQdjXfYOFxd3Z1_zWiBK2vc88mbwYANh-idTqI-7PQA3X7_dlOdZfOrH-dVOc9aXsiYpY_zVkpOBIEpbXTDSCuFUK2YTTXRDe-0bKVmlDagph3QTsgcuKYAeSfSdIC-bH3Xg_89qhDr3oRWWQtO-THUKcJCECYkT-jn_9CVH4cUSKg5yelMMFk8Ucdbqh18CIPS9XowPQybmpL6qaB6W1C9KygtfNrZjk2vun_4SyMJONoCfly_ZvYXveebiQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3051862793</pqid></control><display><type>article</type><title>Statistical Challenges When Analyzing SARS-CoV-2 RNA Measurements Below the Assay Limit of Quantification in COVID-19 Clinical Trials</title><source>Oxford University Press Journals All Titles (1996-Current)</source><source>MEDLINE</source><source>Alma/SFX Local Collection</source><creator>Moser, Carlee B ; Chew, Kara W ; Giganti, Mark J ; Li, Jonathan Z ; Aga, Evgenia ; Ritz, Justin ; Greninger, Alexander L ; Javan, Arzhang Cyrus ; Bender Ignacio, Rachel ; Daar, Eric S ; Wohl, David A ; Currier, Judith S ; Eron, Joseph J ; Smith, Davey M ; Hughes, Michael D</creator><creatorcontrib>Moser, Carlee B ; Chew, Kara W ; Giganti, Mark J ; Li, Jonathan Z ; Aga, Evgenia ; Ritz, Justin ; Greninger, Alexander L ; Javan, Arzhang Cyrus ; Bender Ignacio, Rachel ; Daar, Eric S ; Wohl, David A ; Currier, Judith S ; Eron, Joseph J ; Smith, Davey M ; Hughes, Michael D ; ACTIV-2/A5401 Study Team ; for the ACTIV-2/A5401 Study Team</creatorcontrib><description>Abstract
Most clinical trials evaluating coronavirus disease 2019 (COVID-19) therapeutics include assessments of antiviral activity. In recently completed outpatient trials, changes in nasal severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA levels from baseline were commonly assessed using analysis of covariance (ANCOVA) or mixed models for repeated measures (MMRM) with single imputation for results below assay lower limits of quantification (LLoQ). Analyzing changes in viral RNA levels with singly imputed values can lead to biased estimates of treatment effects. In this article, using an illustrative example from the ACTIV-2 trial, we highlight potential pitfalls of imputation when using ANCOVA or MMRM methods, and illustrate how these methods can be used when considering values <LLoQ as censored measurements. Best practices when analyzing quantitative viral RNA data should include details about the assay and its LLoQ, completeness summaries of viral RNA data, and outcomes among participants with baseline viral RNA ≥ LLoQ, as well as those with viral RNA < LLoQ.
Clinical Trials Registration. NCT04518410.</description><identifier>ISSN: 0022-1899</identifier><identifier>ISSN: 1537-6613</identifier><identifier>EISSN: 1537-6613</identifier><identifier>DOI: 10.1093/infdis/jiad285</identifier><identifier>PMID: 37650235</identifier><language>eng</language><publisher>United States: Oxford University Press</publisher><subject>Analysis of covariance ; Antiviral activity ; Antiviral Agents ; Biological Assay ; Clinical trials ; Coronaviruses ; COVID-19 ; Humans ; Ribonucleic acid ; RNA ; RNA, Viral ; SARS-CoV-2 - genetics ; Severe acute respiratory syndrome coronavirus 2</subject><ispartof>The Journal of infectious diseases, 2023-08, Vol.228 (Supplement_2), p.S101-S110</ispartof><rights>The Author(s) 2023. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com 2023</rights><rights>The Author(s) 2023. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.</rights><rights>The Author(s) 2023. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c397t-1095c773060a41bfb20c766ec684f0fb3df7c7f211bae4da1d675a3f1aa5d65a3</citedby><cites>FETCH-LOGICAL-c397t-1095c773060a41bfb20c766ec684f0fb3df7c7f211bae4da1d675a3f1aa5d65a3</cites><orcidid>0000-0003-2255-9756 ; 0000-0002-7764-0212 ; 0000-0001-5601-9112</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,1578,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37650235$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Moser, Carlee B</creatorcontrib><creatorcontrib>Chew, Kara W</creatorcontrib><creatorcontrib>Giganti, Mark J</creatorcontrib><creatorcontrib>Li, Jonathan Z</creatorcontrib><creatorcontrib>Aga, Evgenia</creatorcontrib><creatorcontrib>Ritz, Justin</creatorcontrib><creatorcontrib>Greninger, Alexander L</creatorcontrib><creatorcontrib>Javan, Arzhang Cyrus</creatorcontrib><creatorcontrib>Bender Ignacio, Rachel</creatorcontrib><creatorcontrib>Daar, Eric S</creatorcontrib><creatorcontrib>Wohl, David A</creatorcontrib><creatorcontrib>Currier, Judith S</creatorcontrib><creatorcontrib>Eron, Joseph J</creatorcontrib><creatorcontrib>Smith, Davey M</creatorcontrib><creatorcontrib>Hughes, Michael D</creatorcontrib><creatorcontrib>ACTIV-2/A5401 Study Team</creatorcontrib><creatorcontrib>for the ACTIV-2/A5401 Study Team</creatorcontrib><title>Statistical Challenges When Analyzing SARS-CoV-2 RNA Measurements Below the Assay Limit of Quantification in COVID-19 Clinical Trials</title><title>The Journal of infectious diseases</title><addtitle>J Infect Dis</addtitle><description>Abstract
Most clinical trials evaluating coronavirus disease 2019 (COVID-19) therapeutics include assessments of antiviral activity. In recently completed outpatient trials, changes in nasal severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA levels from baseline were commonly assessed using analysis of covariance (ANCOVA) or mixed models for repeated measures (MMRM) with single imputation for results below assay lower limits of quantification (LLoQ). Analyzing changes in viral RNA levels with singly imputed values can lead to biased estimates of treatment effects. In this article, using an illustrative example from the ACTIV-2 trial, we highlight potential pitfalls of imputation when using ANCOVA or MMRM methods, and illustrate how these methods can be used when considering values <LLoQ as censored measurements. Best practices when analyzing quantitative viral RNA data should include details about the assay and its LLoQ, completeness summaries of viral RNA data, and outcomes among participants with baseline viral RNA ≥ LLoQ, as well as those with viral RNA < LLoQ.
Clinical Trials Registration. NCT04518410.</description><subject>Analysis of covariance</subject><subject>Antiviral activity</subject><subject>Antiviral Agents</subject><subject>Biological Assay</subject><subject>Clinical trials</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>Humans</subject><subject>Ribonucleic acid</subject><subject>RNA</subject><subject>RNA, Viral</subject><subject>SARS-CoV-2 - genetics</subject><subject>Severe acute respiratory syndrome coronavirus 2</subject><issn>0022-1899</issn><issn>1537-6613</issn><issn>1537-6613</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkc1P3DAQxa2qVVmg1x4rS72UQ8AfG3tzDOkHSAuoLNBjNEls1ivH3saOquXO_13DLj30wunN4TdvNO8h9JGSY0oKfmKc7kw4WRno2Cx_gyY05zITgvK3aEIIYxmdFcUe2g9hRQiZciHfoz0uRU4YzyfocREhmhBNCxZXS7BWuXsV8K-lcrh0YDcPxt3jRXm9yCp_lzF8fVniCwVhHFSvXAz4VFn_B8elwmUIsMFz05uIvcY_R3DR6GQdjXfYOFxd3Z1_zWiBK2vc88mbwYANh-idTqI-7PQA3X7_dlOdZfOrH-dVOc9aXsiYpY_zVkpOBIEpbXTDSCuFUK2YTTXRDe-0bKVmlDagph3QTsgcuKYAeSfSdIC-bH3Xg_89qhDr3oRWWQtO-THUKcJCECYkT-jn_9CVH4cUSKg5yelMMFk8Ucdbqh18CIPS9XowPQybmpL6qaB6W1C9KygtfNrZjk2vun_4SyMJONoCfly_ZvYXveebiQ</recordid><startdate>20230831</startdate><enddate>20230831</enddate><creator>Moser, Carlee B</creator><creator>Chew, Kara W</creator><creator>Giganti, Mark J</creator><creator>Li, Jonathan Z</creator><creator>Aga, Evgenia</creator><creator>Ritz, Justin</creator><creator>Greninger, Alexander L</creator><creator>Javan, Arzhang Cyrus</creator><creator>Bender Ignacio, Rachel</creator><creator>Daar, Eric S</creator><creator>Wohl, David A</creator><creator>Currier, Judith S</creator><creator>Eron, Joseph J</creator><creator>Smith, Davey M</creator><creator>Hughes, Michael D</creator><general>Oxford University Press</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>K9.</scope><scope>NAPCQ</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-2255-9756</orcidid><orcidid>https://orcid.org/0000-0002-7764-0212</orcidid><orcidid>https://orcid.org/0000-0001-5601-9112</orcidid></search><sort><creationdate>20230831</creationdate><title>Statistical Challenges When Analyzing SARS-CoV-2 RNA Measurements Below the Assay Limit of Quantification in COVID-19 Clinical Trials</title><author>Moser, Carlee B ; Chew, Kara W ; Giganti, Mark J ; Li, Jonathan Z ; Aga, Evgenia ; Ritz, Justin ; Greninger, Alexander L ; Javan, Arzhang Cyrus ; Bender Ignacio, Rachel ; Daar, Eric S ; Wohl, David A ; Currier, Judith S ; Eron, Joseph J ; Smith, Davey M ; Hughes, Michael D</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c397t-1095c773060a41bfb20c766ec684f0fb3df7c7f211bae4da1d675a3f1aa5d65a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Analysis of covariance</topic><topic>Antiviral activity</topic><topic>Antiviral Agents</topic><topic>Biological Assay</topic><topic>Clinical trials</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>Humans</topic><topic>Ribonucleic acid</topic><topic>RNA</topic><topic>RNA, Viral</topic><topic>SARS-CoV-2 - genetics</topic><topic>Severe acute respiratory syndrome coronavirus 2</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Moser, Carlee B</creatorcontrib><creatorcontrib>Chew, Kara W</creatorcontrib><creatorcontrib>Giganti, Mark J</creatorcontrib><creatorcontrib>Li, Jonathan Z</creatorcontrib><creatorcontrib>Aga, Evgenia</creatorcontrib><creatorcontrib>Ritz, Justin</creatorcontrib><creatorcontrib>Greninger, Alexander L</creatorcontrib><creatorcontrib>Javan, Arzhang Cyrus</creatorcontrib><creatorcontrib>Bender Ignacio, Rachel</creatorcontrib><creatorcontrib>Daar, Eric S</creatorcontrib><creatorcontrib>Wohl, David A</creatorcontrib><creatorcontrib>Currier, Judith S</creatorcontrib><creatorcontrib>Eron, Joseph J</creatorcontrib><creatorcontrib>Smith, Davey M</creatorcontrib><creatorcontrib>Hughes, Michael D</creatorcontrib><creatorcontrib>ACTIV-2/A5401 Study Team</creatorcontrib><creatorcontrib>for the ACTIV-2/A5401 Study Team</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Premium</collection><collection>MEDLINE - Academic</collection><jtitle>The Journal of infectious diseases</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Moser, Carlee B</au><au>Chew, Kara W</au><au>Giganti, Mark J</au><au>Li, Jonathan Z</au><au>Aga, Evgenia</au><au>Ritz, Justin</au><au>Greninger, Alexander L</au><au>Javan, Arzhang Cyrus</au><au>Bender Ignacio, Rachel</au><au>Daar, Eric S</au><au>Wohl, David A</au><au>Currier, Judith S</au><au>Eron, Joseph J</au><au>Smith, Davey M</au><au>Hughes, Michael D</au><aucorp>ACTIV-2/A5401 Study Team</aucorp><aucorp>for the ACTIV-2/A5401 Study Team</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Statistical Challenges When Analyzing SARS-CoV-2 RNA Measurements Below the Assay Limit of Quantification in COVID-19 Clinical Trials</atitle><jtitle>The Journal of infectious diseases</jtitle><addtitle>J Infect Dis</addtitle><date>2023-08-31</date><risdate>2023</risdate><volume>228</volume><issue>Supplement_2</issue><spage>S101</spage><epage>S110</epage><pages>S101-S110</pages><issn>0022-1899</issn><issn>1537-6613</issn><eissn>1537-6613</eissn><abstract>Abstract
Most clinical trials evaluating coronavirus disease 2019 (COVID-19) therapeutics include assessments of antiviral activity. In recently completed outpatient trials, changes in nasal severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA levels from baseline were commonly assessed using analysis of covariance (ANCOVA) or mixed models for repeated measures (MMRM) with single imputation for results below assay lower limits of quantification (LLoQ). Analyzing changes in viral RNA levels with singly imputed values can lead to biased estimates of treatment effects. In this article, using an illustrative example from the ACTIV-2 trial, we highlight potential pitfalls of imputation when using ANCOVA or MMRM methods, and illustrate how these methods can be used when considering values <LLoQ as censored measurements. Best practices when analyzing quantitative viral RNA data should include details about the assay and its LLoQ, completeness summaries of viral RNA data, and outcomes among participants with baseline viral RNA ≥ LLoQ, as well as those with viral RNA < LLoQ.
Clinical Trials Registration. NCT04518410.</abstract><cop>United States</cop><pub>Oxford University Press</pub><pmid>37650235</pmid><doi>10.1093/infdis/jiad285</doi><orcidid>https://orcid.org/0000-0003-2255-9756</orcidid><orcidid>https://orcid.org/0000-0002-7764-0212</orcidid><orcidid>https://orcid.org/0000-0001-5601-9112</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0022-1899 |
| ispartof | The Journal of infectious diseases, 2023-08, Vol.228 (Supplement_2), p.S101-S110 |
| issn | 0022-1899 1537-6613 1537-6613 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_2859602673 |
| source | Oxford University Press Journals All Titles (1996-Current); MEDLINE; Alma/SFX Local Collection |
| subjects | Analysis of covariance Antiviral activity Antiviral Agents Biological Assay Clinical trials Coronaviruses COVID-19 Humans Ribonucleic acid RNA RNA, Viral SARS-CoV-2 - genetics Severe acute respiratory syndrome coronavirus 2 |
| title | Statistical Challenges When Analyzing SARS-CoV-2 RNA Measurements Below the Assay Limit of Quantification in COVID-19 Clinical Trials |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-30T21%3A45%3A19IST&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=Statistical%20Challenges%20When%20Analyzing%20SARS-CoV-2%20RNA%20Measurements%20Below%20the%20Assay%20Limit%20of%20Quantification%20in%20COVID-19%20Clinical%20Trials&rft.jtitle=The%20Journal%20of%20infectious%20diseases&rft.au=Moser,%20Carlee%20B&rft.aucorp=ACTIV-2/A5401%20Study%20Team&rft.date=2023-08-31&rft.volume=228&rft.issue=Supplement_2&rft.spage=S101&rft.epage=S110&rft.pages=S101-S110&rft.issn=0022-1899&rft.eissn=1537-6613&rft_id=info:doi/10.1093/infdis/jiad285&rft_dat=%3Cproquest_cross%3E2859602673%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=3051862793&rft_id=info:pmid/37650235&rft_oup_id=10.1093/infdis/jiad285&rfr_iscdi=true |