CovidStanfordV1, Main, Exploration, bibRecord, 000529

How to Quantify and Interpret Treatment Effects in Comparative Clinical Studies of COVID-19.

Identifieur interne : 000529 ( Main/Exploration ); précédent : 000528; suivant : 000530

How to Quantify and Interpret Treatment Effects in Comparative Clinical Studies of COVID-19.

Auteurs : Zachary R. Mccaw ; Lu Tian [États-Unis] ; Jason L. Vassy ; Christine Seel Ritchie ; Chien-Chang Lee ; Dae Hyun Kim ; Lee-Jen Wei

Source :

Annals of internal medicine [ 1539-3704 ] ; 2020.

RBID : pubmed:32634024

Descripteurs français

KwdFr :
- Betacoronavirus (MeSH), Essais contrôlés randomisés comme sujet (méthodes), Humains (MeSH), Immunisation passive (méthodes), Infections à coronavirus (thérapie), Infections à coronavirus (épidémiologie), Pandémies (MeSH), Pneumopathie virale (thérapie), Pneumopathie virale (épidémiologie), Résultat thérapeutique (MeSH).
MESH :
- méthodes : Essais contrôlés randomisés comme sujet, Immunisation passive.
- thérapie : Infections à coronavirus, Pneumopathie virale.
- épidémiologie : Infections à coronavirus, Pneumopathie virale.
- Betacoronavirus, Humains, Pandémies, Résultat thérapeutique.

English descriptors

KwdEn :
- Betacoronavirus (MeSH), COVID-19 (MeSH), Coronavirus Infections (epidemiology), Coronavirus Infections (therapy), Humans (MeSH), Immunization, Passive (methods), Pandemics (MeSH), Pneumonia, Viral (epidemiology), Pneumonia, Viral (therapy), Randomized Controlled Trials as Topic (methods), SARS-CoV-2 (MeSH), Treatment Outcome (MeSH).
MESH :
- epidemiology : Coronavirus Infections, Pneumonia, Viral.
- methods : Immunization, Passive, Randomized Controlled Trials as Topic.
- therapy : Coronavirus Infections, Pneumonia, Viral.
- Betacoronavirus, COVID-19, Humans, Pandemics, SARS-CoV-2, Treatment Outcome.

Abstract

Clinical trials of treatments for coronavirus disease 2019 (COVID-19) draw intense public attention. More than ever, valid, transparent, and intuitive summaries of the treatment effects, including efficacy and harm, are needed. In recently published and ongoing randomized comparative trials evaluating treatments for COVID-19, time to a positive outcome, such as recovery or improvement, has repeatedly been used as either the primary or key secondary end point. Because patients may die before recovery or improvement, data analysis of this end point faces a competing risk problem. Commonly used survival analysis techniques, such as the Kaplan-Meier method, often are not appropriate for such situations. Moreover, almost all trials have quantified treatment effects by using the hazard ratio, which is difficult to interpret for a positive event, especially in the presence of competing risks. Using 2 recent trials evaluating treatments (remdesivir and convalescent plasma) for COVID-19 as examples, a valid, well-established yet underused procedure is presented for estimating the cumulative recovery or improvement rate curve across the study period. Furthermore, an intuitive and clinically interpretable summary of treatment efficacy based on this curve is also proposed. Clinical investigators are encouraged to consider applying these methods for quantifying treatment effects in future studies of COVID-19.

DOI: 10.7326/M20-4044
PubMed: 32634024
PubMed Central: PMC7350552

Affiliations:

Links toward previous steps (curation, corpus...)

to stream Main, to step Corpus: 000532
to stream Main, to step Curation: 000532

Le document en format XML

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<term>Humans (MeSH)</term>
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<term>Infections à coronavirus (épidémiologie)</term>
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<front><div type="abstract" xml:lang="en">Clinical trials of treatments for coronavirus disease 2019 (COVID-19) draw intense public attention. More than ever, valid, transparent, and intuitive summaries of the treatment effects, including efficacy and harm, are needed. In recently published and ongoing randomized comparative trials evaluating treatments for COVID-19, time to a positive outcome, such as recovery or improvement, has repeatedly been used as either the primary or key secondary end point. Because patients may die before recovery or improvement, data analysis of this end point faces a competing risk problem. Commonly used survival analysis techniques, such as the Kaplan-Meier method, often are not appropriate for such situations. Moreover, almost all trials have quantified treatment effects by using the hazard ratio, which is difficult to interpret for a positive event, especially in the presence of competing risks. Using 2 recent trials evaluating treatments (remdesivir and convalescent plasma) for COVID-19 as examples, a valid, well-established yet underused procedure is presented for estimating the cumulative recovery or improvement rate curve across the study period. Furthermore, an intuitive and clinically interpretable summary of treatment efficacy based on this curve is also proposed. Clinical investigators are encouraged to consider applying these methods for quantifying treatment effects in future studies of COVID-19.</div>
</front>
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<name sortKey="Mccaw, Zachary R" sort="Mccaw, Zachary R" uniqKey="Mccaw Z" first="Zachary R" last="Mccaw">Zachary R. Mccaw</name>
<name sortKey="Ritchie, Christine Seel" sort="Ritchie, Christine Seel" uniqKey="Ritchie C" first="Christine Seel" last="Ritchie">Christine Seel Ritchie</name>
<name sortKey="Vassy, Jason L" sort="Vassy, Jason L" uniqKey="Vassy J" first="Jason L" last="Vassy">Jason L. Vassy</name>
<name sortKey="Wei, Lee Jen" sort="Wei, Lee Jen" uniqKey="Wei L" first="Lee-Jen" last="Wei">Lee-Jen Wei</name>
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<country name="États-Unis"><region name="Californie"><name sortKey="Tian, Lu" sort="Tian, Lu" uniqKey="Tian L" first="Lu" last="Tian">Lu Tian</name>
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Pour manipuler ce document sous Unix (Dilib)

EXPLOR_STEP=$WICRI_ROOT/Sante/explor/CovidStanfordV1/Data/Main/Exploration

HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000529 | SxmlIndent | more

HfdSelect -h $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd -nk 000529 | SxmlIndent | more

Pour mettre un lien sur cette page dans le réseau Wicri

{{Explor lien
   |wiki=    Sante
   |area=    CovidStanfordV1
   |flux=    Main
   |étape=   Exploration
   |type=    RBID
   |clé=     pubmed:32634024
   |texte=   How to Quantify and Interpret Treatment Effects in Comparative Clinical Studies of COVID-19.
}}

Pour générer des pages wiki

HfdIndexSelect -h $EXPLOR_AREA/Data/Main/Exploration/RBID.i   -Sk "pubmed:32634024" \
       | HfdSelect -Kh $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd   \
       | NlmPubMed2Wicri -a CovidStanfordV1

This area was generated with Dilib version V0.6.38.
Data generation: Tue Feb 2 21:24:25 2021. Site generation: Tue Feb 2 21:26:08 2021

	Serveur d'exploration sur le Covid à Stanford
	Attention, ce site est en cours de développement ! Attention, site généré par des moyens informatiques à partir de corpus bruts. Les informations ne sont donc pas validées.

Serveur d'exploration sur le Covid à Stanford

How to Quantify and Interpret Treatment Effects in Comparative Clinical Studies of COVID-19.

How to Quantify and Interpret Treatment Effects in Comparative Clinical Studies of COVID-19.

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri

Pour générer des pages wiki