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...
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Veröffentlicht in: | Statistical methods in medical research 2022-02, Vol.31 (2), p.348-360 |
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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 |
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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 & Abstracts (ASSIA)</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & 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> |
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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 |
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