A sequential test to compare the real-time fatality rates of a disease among multiple groups with an application to COVID-19 data

Infectious diseases, such as the ongoing COVID-19 pandemic, pose a significant threat to public health globally. Fatality rate serves as a key indicator for the effectiveness of potential treatments or interventions. With limited time and understanding of novel emerging epidemics, comparisons of the...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Statistical methods in medical research 2022-02, Vol.31 (2), p.348-360
Hauptverfasser: Qu, Yuanke, Yin Lee, Chun, Lam, KF
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 360
container_issue 2
container_start_page 348
container_title Statistical methods in medical research
container_volume 31
creator Qu, Yuanke
Yin Lee, Chun
Lam, KF
description Infectious diseases, such as the ongoing COVID-19 pandemic, pose a significant threat to public health globally. Fatality rate serves as a key indicator for the effectiveness of potential treatments or interventions. With limited time and understanding of novel emerging epidemics, comparisons of the fatality rates in real-time among different groups, say, divided by treatment, age, or area, have an important role to play in informing public health strategies. We propose a statistical test for the null hypothesis of equal real-time fatality rates across multiple groups during an ongoing epidemic. An elegant property of the proposed test statistic is that it converges to a Brownian motion under the null hypothesis, which allows one to develop a sequential testing approach for rejecting the null hypothesis at the earliest possible time when statistical evidence accumulates. This property is particularly important as scientists and clinicians are competing with time to identify possible treatments or effective interventions to combat the emerging epidemic. The method is widely applicable as it only requires the cumulative number of confirmed cases, deaths, and recoveries. A large-scale simulation study shows that the finite-sample performance of the proposed test is highly satisfactory. The proposed test is applied to compare the difference in disease severity among Wuhan, Hubei province (exclude Wuhan) and mainland China (exclude Hubei) from February to March 2020. The result suggests that the disease severity is potentially associated with the health care resource availability during the early phase of the COVID-19 pandemic in mainland China.
doi_str_mv 10.1177/09622802211061927
format Article
fullrecord <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8832113</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sage_id>10.1177_09622802211061927</sage_id><sourcerecordid>2608108454</sourcerecordid><originalsourceid>FETCH-LOGICAL-c466t-223ae447f1ef24d3c68f7de25179b1d78cf7ef9a6a1b390f932a0543081cc9b33</originalsourceid><addsrcrecordid>eNp1kUtvFDEQhC0EIsvCD-CCLHHhMsGvjD0XpGh5RYqUC3Ad9Xrau44848H2BOXIP8erDeElTj7011WuLkKec3bKudavWdcKYZgQnLOWd0I_ICuutG6YlOohWR3mzQE4IU9yvmaMaaa6x-REKqONbMWKfD-nGb8uOBUPgRbMhZZIbRxnSEjLHmlCCE3xI1IHBYIvtzRBBWl0FOjgM0JGCmOcdnRcQvFzQLpLcZkz_ebLnsJEYZ6Dt1B8nA7ym6svF28b3tGhKj4ljxyEjM_u3jX5_P7dp83H5vLqw8Xm_LKxqm1LI4QEVEo7jk6oQdrWOD2gOOO62_JBG-s0ug5a4FvZMddJAexMSWa4td1WyjV5c9Sdl-2Ig62RE4R-Tn6EdNtH8P2fk8nv-1286Y2R9cAHgVd3AinWi-XSjz5bDAEmjEvuRVu9mFHVdE1e_oVexyVNNV6lhGZGCGYqxY-UTTHnhO7-M5z1h4L7fwquOy9-T3G_8bPRCpwegQw7_GX7f8UfyCSuVg</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2627082208</pqid></control><display><type>article</type><title>A sequential test to compare the real-time fatality rates of a disease among multiple groups with an application to COVID-19 data</title><source>Applied Social Sciences Index &amp; Abstracts (ASSIA)</source><source>Access via SAGE</source><source>MEDLINE</source><creator>Qu, Yuanke ; Yin Lee, Chun ; Lam, KF</creator><creatorcontrib>Qu, Yuanke ; Yin Lee, Chun ; Lam, KF</creatorcontrib><description>Infectious diseases, such as the ongoing COVID-19 pandemic, pose a significant threat to public health globally. Fatality rate serves as a key indicator for the effectiveness of potential treatments or interventions. With limited time and understanding of novel emerging epidemics, comparisons of the fatality rates in real-time among different groups, say, divided by treatment, age, or area, have an important role to play in informing public health strategies. We propose a statistical test for the null hypothesis of equal real-time fatality rates across multiple groups during an ongoing epidemic. An elegant property of the proposed test statistic is that it converges to a Brownian motion under the null hypothesis, which allows one to develop a sequential testing approach for rejecting the null hypothesis at the earliest possible time when statistical evidence accumulates. This property is particularly important as scientists and clinicians are competing with time to identify possible treatments or effective interventions to combat the emerging epidemic. The method is widely applicable as it only requires the cumulative number of confirmed cases, deaths, and recoveries. A large-scale simulation study shows that the finite-sample performance of the proposed test is highly satisfactory. The proposed test is applied to compare the difference in disease severity among Wuhan, Hubei province (exclude Wuhan) and mainland China (exclude Hubei) from February to March 2020. The result suggests that the disease severity is potentially associated with the health care resource availability during the early phase of the COVID-19 pandemic in mainland China.</description><identifier>ISSN: 0962-2802</identifier><identifier>ISSN: 1477-0334</identifier><identifier>EISSN: 1477-0334</identifier><identifier>DOI: 10.1177/09622802211061927</identifier><identifier>PMID: 34878362</identifier><language>eng</language><publisher>London, England: SAGE Publications</publisher><subject>Brownian motion ; China - epidemiology ; Coronaviruses ; COVID-19 ; Disease ; Epidemics ; Fatalities ; Health problems ; Health status ; Humans ; Hypotheses ; Infectious diseases ; Intervention ; Null hypothesis ; Original s ; Pandemics ; Property ; Public health ; Real time ; SARS-CoV-2 ; Simulation ; Statistical analysis ; Statistical tests ; Viral diseases</subject><ispartof>Statistical methods in medical research, 2022-02, Vol.31 (2), p.348-360</ispartof><rights>The Author(s) 2021</rights><rights>The Author(s) 2021 2021 SAGE Publications</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c466t-223ae447f1ef24d3c68f7de25179b1d78cf7ef9a6a1b390f932a0543081cc9b33</citedby><cites>FETCH-LOGICAL-c466t-223ae447f1ef24d3c68f7de25179b1d78cf7ef9a6a1b390f932a0543081cc9b33</cites><orcidid>0000-0001-5453-994X ; 0000-0002-7207-2519</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://journals.sagepub.com/doi/pdf/10.1177/09622802211061927$$EPDF$$P50$$Gsage$$H</linktopdf><linktohtml>$$Uhttps://journals.sagepub.com/doi/10.1177/09622802211061927$$EHTML$$P50$$Gsage$$H</linktohtml><link.rule.ids>230,315,782,786,887,21828,27933,27934,31008,43630,43631</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34878362$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Qu, Yuanke</creatorcontrib><creatorcontrib>Yin Lee, Chun</creatorcontrib><creatorcontrib>Lam, KF</creatorcontrib><title>A sequential test to compare the real-time fatality rates of a disease among multiple groups with an application to COVID-19 data</title><title>Statistical methods in medical research</title><addtitle>Stat Methods Med Res</addtitle><description>Infectious diseases, such as the ongoing COVID-19 pandemic, pose a significant threat to public health globally. Fatality rate serves as a key indicator for the effectiveness of potential treatments or interventions. With limited time and understanding of novel emerging epidemics, comparisons of the fatality rates in real-time among different groups, say, divided by treatment, age, or area, have an important role to play in informing public health strategies. We propose a statistical test for the null hypothesis of equal real-time fatality rates across multiple groups during an ongoing epidemic. An elegant property of the proposed test statistic is that it converges to a Brownian motion under the null hypothesis, which allows one to develop a sequential testing approach for rejecting the null hypothesis at the earliest possible time when statistical evidence accumulates. This property is particularly important as scientists and clinicians are competing with time to identify possible treatments or effective interventions to combat the emerging epidemic. The method is widely applicable as it only requires the cumulative number of confirmed cases, deaths, and recoveries. A large-scale simulation study shows that the finite-sample performance of the proposed test is highly satisfactory. The proposed test is applied to compare the difference in disease severity among Wuhan, Hubei province (exclude Wuhan) and mainland China (exclude Hubei) from February to March 2020. The result suggests that the disease severity is potentially associated with the health care resource availability during the early phase of the COVID-19 pandemic in mainland China.</description><subject>Brownian motion</subject><subject>China - epidemiology</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>Disease</subject><subject>Epidemics</subject><subject>Fatalities</subject><subject>Health problems</subject><subject>Health status</subject><subject>Humans</subject><subject>Hypotheses</subject><subject>Infectious diseases</subject><subject>Intervention</subject><subject>Null hypothesis</subject><subject>Original s</subject><subject>Pandemics</subject><subject>Property</subject><subject>Public health</subject><subject>Real time</subject><subject>SARS-CoV-2</subject><subject>Simulation</subject><subject>Statistical analysis</subject><subject>Statistical tests</subject><subject>Viral diseases</subject><issn>0962-2802</issn><issn>1477-0334</issn><issn>1477-0334</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>7QJ</sourceid><recordid>eNp1kUtvFDEQhC0EIsvCD-CCLHHhMsGvjD0XpGh5RYqUC3Ad9Xrau44848H2BOXIP8erDeElTj7011WuLkKec3bKudavWdcKYZgQnLOWd0I_ICuutG6YlOohWR3mzQE4IU9yvmaMaaa6x-REKqONbMWKfD-nGb8uOBUPgRbMhZZIbRxnSEjLHmlCCE3xI1IHBYIvtzRBBWl0FOjgM0JGCmOcdnRcQvFzQLpLcZkz_ebLnsJEYZ6Dt1B8nA7ym6svF28b3tGhKj4ljxyEjM_u3jX5_P7dp83H5vLqw8Xm_LKxqm1LI4QEVEo7jk6oQdrWOD2gOOO62_JBG-s0ug5a4FvZMddJAexMSWa4td1WyjV5c9Sdl-2Ig62RE4R-Tn6EdNtH8P2fk8nv-1286Y2R9cAHgVd3AinWi-XSjz5bDAEmjEvuRVu9mFHVdE1e_oVexyVNNV6lhGZGCGYqxY-UTTHnhO7-M5z1h4L7fwquOy9-T3G_8bPRCpwegQw7_GX7f8UfyCSuVg</recordid><startdate>202202</startdate><enddate>202202</enddate><creator>Qu, Yuanke</creator><creator>Yin Lee, Chun</creator><creator>Lam, KF</creator><general>SAGE Publications</general><general>Sage Publications Ltd</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>7QJ</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>K9.</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-5453-994X</orcidid><orcidid>https://orcid.org/0000-0002-7207-2519</orcidid></search><sort><creationdate>202202</creationdate><title>A sequential test to compare the real-time fatality rates of a disease among multiple groups with an application to COVID-19 data</title><author>Qu, Yuanke ; Yin Lee, Chun ; Lam, KF</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c466t-223ae447f1ef24d3c68f7de25179b1d78cf7ef9a6a1b390f932a0543081cc9b33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Brownian motion</topic><topic>China - epidemiology</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>Disease</topic><topic>Epidemics</topic><topic>Fatalities</topic><topic>Health problems</topic><topic>Health status</topic><topic>Humans</topic><topic>Hypotheses</topic><topic>Infectious diseases</topic><topic>Intervention</topic><topic>Null hypothesis</topic><topic>Original s</topic><topic>Pandemics</topic><topic>Property</topic><topic>Public health</topic><topic>Real time</topic><topic>SARS-CoV-2</topic><topic>Simulation</topic><topic>Statistical analysis</topic><topic>Statistical tests</topic><topic>Viral diseases</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Qu, Yuanke</creatorcontrib><creatorcontrib>Yin Lee, Chun</creatorcontrib><creatorcontrib>Lam, KF</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Applied Social Sciences Index &amp; Abstracts (ASSIA)</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Statistical methods in medical research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Qu, Yuanke</au><au>Yin Lee, Chun</au><au>Lam, KF</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A sequential test to compare the real-time fatality rates of a disease among multiple groups with an application to COVID-19 data</atitle><jtitle>Statistical methods in medical research</jtitle><addtitle>Stat Methods Med Res</addtitle><date>2022-02</date><risdate>2022</risdate><volume>31</volume><issue>2</issue><spage>348</spage><epage>360</epage><pages>348-360</pages><issn>0962-2802</issn><issn>1477-0334</issn><eissn>1477-0334</eissn><abstract>Infectious diseases, such as the ongoing COVID-19 pandemic, pose a significant threat to public health globally. Fatality rate serves as a key indicator for the effectiveness of potential treatments or interventions. With limited time and understanding of novel emerging epidemics, comparisons of the fatality rates in real-time among different groups, say, divided by treatment, age, or area, have an important role to play in informing public health strategies. We propose a statistical test for the null hypothesis of equal real-time fatality rates across multiple groups during an ongoing epidemic. An elegant property of the proposed test statistic is that it converges to a Brownian motion under the null hypothesis, which allows one to develop a sequential testing approach for rejecting the null hypothesis at the earliest possible time when statistical evidence accumulates. This property is particularly important as scientists and clinicians are competing with time to identify possible treatments or effective interventions to combat the emerging epidemic. The method is widely applicable as it only requires the cumulative number of confirmed cases, deaths, and recoveries. A large-scale simulation study shows that the finite-sample performance of the proposed test is highly satisfactory. The proposed test is applied to compare the difference in disease severity among Wuhan, Hubei province (exclude Wuhan) and mainland China (exclude Hubei) from February to March 2020. The result suggests that the disease severity is potentially associated with the health care resource availability during the early phase of the COVID-19 pandemic in mainland China.</abstract><cop>London, England</cop><pub>SAGE Publications</pub><pmid>34878362</pmid><doi>10.1177/09622802211061927</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0001-5453-994X</orcidid><orcidid>https://orcid.org/0000-0002-7207-2519</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0962-2802
ispartof Statistical methods in medical research, 2022-02, Vol.31 (2), p.348-360
issn 0962-2802
1477-0334
1477-0334
language eng
recordid cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8832113
source Applied Social Sciences Index & Abstracts (ASSIA); Access via SAGE; MEDLINE
subjects Brownian motion
China - epidemiology
Coronaviruses
COVID-19
Disease
Epidemics
Fatalities
Health problems
Health status
Humans
Hypotheses
Infectious diseases
Intervention
Null hypothesis
Original s
Pandemics
Property
Public health
Real time
SARS-CoV-2
Simulation
Statistical analysis
Statistical tests
Viral diseases
title A sequential test to compare the real-time fatality rates of a disease among multiple groups with an application to COVID-19 data
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-11-30T12%3A36%3A51IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20sequential%20test%20to%20compare%20the%20real-time%20fatality%20rates%20of%20a%20disease%20among%20multiple%20groups%20with%20an%20application%20to%20COVID-19%20data&rft.jtitle=Statistical%20methods%20in%20medical%20research&rft.au=Qu,%20Yuanke&rft.date=2022-02&rft.volume=31&rft.issue=2&rft.spage=348&rft.epage=360&rft.pages=348-360&rft.issn=0962-2802&rft.eissn=1477-0334&rft_id=info:doi/10.1177/09622802211061927&rft_dat=%3Cproquest_pubme%3E2608108454%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2627082208&rft_id=info:pmid/34878362&rft_sage_id=10.1177_09622802211061927&rfr_iscdi=true