Selecting appropriate endpoints for assessing treatment effects in comparative clinical studies for COVID-19.
Identifieur interne : 000281 ( Main/Exploration ); précédent : 000280; suivant : 000282Selecting appropriate endpoints for assessing treatment effects in comparative clinical studies for COVID-19.
Auteurs : Zachary R. Mccaw [États-Unis] ; Lu Tian [États-Unis] ; Kevin N. Sheth [États-Unis] ; Wan-Ting Hsu [États-Unis] ; W Taylor Kimberly [États-Unis] ; Lee-Jen Wei [États-Unis]Source :
- Contemporary clinical trials [ 1559-2030 ] ; 2020.
Descripteurs français
- KwdFr :
- Analyse de survie (MeSH), Biais (épidémiologie) (MeSH), Détermination du point final (MeSH), Essais cliniques comme sujet (méthodes), Essais cliniques comme sujet (normes), Gestion des soins aux patients (méthodes), Gestion des soins aux patients (statistiques et données numériques), Hospitalisation (MeSH), Humains (MeSH), Oxygénothérapie (statistiques et données numériques), Unités de soins intensifs (statistiques et données numériques).
- MESH :
- méthodes : Essais cliniques comme sujet, Gestion des soins aux patients.
- normes : Essais cliniques comme sujet.
- statistiques et données numériques : Gestion des soins aux patients, Oxygénothérapie, Unités de soins intensifs.
- Analyse de survie, Biais (épidémiologie), Détermination du point final, Hospitalisation, Humains.
English descriptors
- KwdEn :
- Bias (MeSH), COVID-19 (epidemiology), COVID-19 (therapy), Clinical Trials as Topic (methods), Clinical Trials as Topic (standards), Duration of Therapy (MeSH), Endpoint Determination (MeSH), Hospitalization (MeSH), Humans (MeSH), Intensive Care Units (statistics & numerical data), Oxygen Inhalation Therapy (statistics & numerical data), Patient Care Management (methods), Patient Care Management (statistics & numerical data), SARS-CoV-2 (MeSH), Survival Analysis (MeSH).
- MESH :
- epidemiology : COVID-19.
- methods : Clinical Trials as Topic, Patient Care Management.
- standards : Clinical Trials as Topic.
- statistics & numerical data : Intensive Care Units, Oxygen Inhalation Therapy, Patient Care Management.
- therapy : COVID-19.
- Bias, Duration of Therapy, Endpoint Determination, Hospitalization, Humans, SARS-CoV-2, Survival Analysis.
Abstract
To evaluate the efficacy and safety of a new treatment for COVID-19 vs. standard care, certain key endpoints are related to the duration of a specific event, such as hospitalization, ICU stay, or receipt of supplemental oxygen. However, since patients may die in the hospital during study follow-up, using, for example, the duration of hospitalization to assess treatment efficacy can be misleading. If the treatment tends to prolong patients' survival compared with standard care, patients in the new treatment group may spend more time in hospital. This can lead to a "survival bias" issue, where a treatment that is effective for preventing death appears to prolong an undesirable outcome. On the other hand, by using hospital-free survival time as the endpoint, we can circumvent the survival bias issue. In this article, we use reconstructed data from a recent, large clinical trial for COVID-19 to illustrate the advantages of this approach. For the analysis of ICU stay or oxygen usage, where the initiating event is potentially an outcome of treatment, standard survival analysis techniques may not be appropriate. We also discuss issues with analyzing the durations of such events.
DOI: 10.1016/j.cct.2020.106145
PubMed: 32927092
PubMed Central: PMC7486285
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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Chan School of Public Health, Boston, MA</wicri:regionArea><placeName><region type="state">Massachusetts</region></placeName></affiliation></author></analytic><series><title level="j">Contemporary clinical trials</title><idno type="eISSN">1559-2030</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Bias (MeSH)</term><term>COVID-19 (epidemiology)</term><term>COVID-19 (therapy)</term><term>Clinical Trials as Topic (methods)</term><term>Clinical Trials as Topic (standards)</term><term>Duration of Therapy (MeSH)</term><term>Endpoint Determination (MeSH)</term><term>Hospitalization (MeSH)</term><term>Humans (MeSH)</term><term>Intensive Care Units (statistics & numerical data)</term><term>Oxygen Inhalation Therapy (statistics & numerical data)</term><term>Patient Care Management (methods)</term><term>Patient Care Management (statistics & numerical data)</term><term>SARS-CoV-2 (MeSH)</term><term>Survival Analysis (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Analyse de survie (MeSH)</term><term>Biais (épidémiologie) (MeSH)</term><term>Détermination du point final (MeSH)</term><term>Essais cliniques comme sujet (méthodes)</term><term>Essais cliniques comme sujet (normes)</term><term>Gestion des soins aux patients (méthodes)</term><term>Gestion des soins aux patients (statistiques et données numériques)</term><term>Hospitalisation (MeSH)</term><term>Humains (MeSH)</term><term>Oxygénothérapie (statistiques et données numériques)</term><term>Unités de soins intensifs (statistiques et données numériques)</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>COVID-19</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Clinical Trials as Topic</term><term>Patient Care Management</term></keywords><keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Essais cliniques comme sujet</term><term>Gestion des soins aux patients</term></keywords><keywords scheme="MESH" qualifier="normes" xml:lang="fr"><term>Essais cliniques comme sujet</term></keywords><keywords scheme="MESH" qualifier="standards" xml:lang="en"><term>Clinical Trials as Topic</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Intensive Care Units</term><term>Oxygen Inhalation Therapy</term><term>Patient Care Management</term></keywords><keywords scheme="MESH" qualifier="statistiques et données numériques" xml:lang="fr"><term>Gestion des soins aux patients</term><term>Oxygénothérapie</term><term>Unités de soins intensifs</term></keywords><keywords scheme="MESH" qualifier="therapy" xml:lang="en"><term>COVID-19</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Bias</term><term>Duration of Therapy</term><term>Endpoint Determination</term><term>Hospitalization</term><term>Humans</term><term>SARS-CoV-2</term><term>Survival Analysis</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Analyse de survie</term><term>Biais (épidémiologie)</term><term>Détermination du point final</term><term>Hospitalisation</term><term>Humains</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">To evaluate the efficacy and safety of a new treatment for COVID-19 vs. standard care, certain key endpoints are related to the duration of a specific event, such as hospitalization, ICU stay, or receipt of supplemental oxygen. However, since patients may die in the hospital during study follow-up, using, for example, the duration of hospitalization to assess treatment efficacy can be misleading. If the treatment tends to prolong patients' survival compared with standard care, patients in the new treatment group may spend more time in hospital. This can lead to a "survival bias" issue, where a treatment that is effective for preventing death appears to prolong an undesirable outcome. On the other hand, by using hospital-free survival time as the endpoint, we can circumvent the survival bias issue. In this article, we use reconstructed data from a recent, large clinical trial for COVID-19 to illustrate the advantages of this approach. For the analysis of ICU stay or oxygen usage, where the initiating event is potentially an outcome of treatment, standard survival analysis techniques may not be appropriate. We also discuss issues with analyzing the durations of such events.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">32927092</PMID><DateCompleted><Year>2020</Year><Month>12</Month><Day>10</Day></DateCompleted><DateRevised><Year>2021</Year><Month>01</Month><Day>10</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1559-2030</ISSN><JournalIssue CitedMedium="Internet"><Volume>97</Volume><PubDate><Year>2020</Year><Month>10</Month></PubDate></JournalIssue><Title>Contemporary clinical trials</Title><ISOAbbreviation>Contemp Clin Trials</ISOAbbreviation></Journal><ArticleTitle>Selecting appropriate endpoints for assessing treatment effects in comparative clinical studies for COVID-19.</ArticleTitle><Pagination><MedlinePgn>106145</MedlinePgn></Pagination><ELocationID EIdType="pii" ValidYN="Y">S1551-7144(20)30223-8</ELocationID><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.cct.2020.106145</ELocationID><Abstract><AbstractText>To evaluate the efficacy and safety of a new treatment for COVID-19 vs. standard care, certain key endpoints are related to the duration of a specific event, such as hospitalization, ICU stay, or receipt of supplemental oxygen. However, since patients may die in the hospital during study follow-up, using, for example, the duration of hospitalization to assess treatment efficacy can be misleading. If the treatment tends to prolong patients' survival compared with standard care, patients in the new treatment group may spend more time in hospital. This can lead to a "survival bias" issue, where a treatment that is effective for preventing death appears to prolong an undesirable outcome. On the other hand, by using hospital-free survival time as the endpoint, we can circumvent the survival bias issue. In this article, we use reconstructed data from a recent, large clinical trial for COVID-19 to illustrate the advantages of this approach. For the analysis of ICU stay or oxygen usage, where the initiating event is potentially an outcome of treatment, standard survival analysis techniques may not be appropriate. We also discuss issues with analyzing the durations of such events.</AbstractText><CopyrightInformation>Copyright © 2020 Elsevier Inc. 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Chan School of Public Health, Boston, MA, United States of America. Electronic address: wei@hsph.harvard.edu.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>09</Month><Day>12</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Contemp Clin Trials</MedlineTA><NlmUniqueID>101242342</NlmUniqueID><ISSNLinking>1551-7144</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D015982" MajorTopicYN="N">Bias</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000086382" MajorTopicYN="Y">COVID-19</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName><QualifierName UI="Q000628" MajorTopicYN="N">therapy</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D002986" MajorTopicYN="Y">Clinical Trials as 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IdType="pubmed">29541747</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>États-Unis</li></country><region><li>Californie</li><li>Connecticut</li><li>Massachusetts</li></region><settlement><li>Stanford (Californie)</li></settlement><orgName><li>Université Stanford</li></orgName></list><tree><country name="États-Unis"><region name="Californie"><name sortKey="Mccaw, Zachary R" sort="Mccaw, Zachary R" uniqKey="Mccaw Z" first="Zachary R" last="Mccaw">Zachary R. Mccaw</name></region><name sortKey="Hsu, Wan Ting" sort="Hsu, Wan Ting" uniqKey="Hsu W" first="Wan-Ting" last="Hsu">Wan-Ting Hsu</name><name sortKey="Kimberly, W Taylor" sort="Kimberly, W Taylor" uniqKey="Kimberly W" first="W Taylor" last="Kimberly">W Taylor Kimberly</name><name sortKey="Sheth, Kevin N" sort="Sheth, Kevin N" uniqKey="Sheth K" first="Kevin N" last="Sheth">Kevin N. Sheth</name><name sortKey="Tian, Lu" sort="Tian, Lu" uniqKey="Tian L" first="Lu" last="Tian">Lu Tian</name><name sortKey="Wei, Lee Jen" sort="Wei, Lee Jen" uniqKey="Wei L" first="Lee-Jen" last="Wei">Lee-Jen Wei</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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