Detection of significant antiviral drug effects on COVID-19 with reasonable sample sizes in randomized controlled trials: A modeling study
Development of an effective antiviral drug for Coronavirus Disease 2019 (COVID-19) is a global health priority. Although several candidate drugs have been identified through in vitro and in vivo models, consistent and compelling evidence from clinical studies is limited. The lack of evidence from cl...
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| Veröffentlicht in: | PLoS medicine 2021-07, Vol.18 (7), p.e1003660-e1003660 |
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| creator | Iwanami, Shoya Ejima, Keisuke Kim, Kwang Su Noshita, Koji Fujita, Yasuhisa Miyazaki, Taiga Kohno, Shigeru Miyazaki, Yoshitsugu Morimoto, Shimpei Nakaoka, Shinji Koizumi, Yoshiki Asai, Yusuke Aihara, Kazuyuki Watashi, Koichi Thompson, Robin N Shibuya, Kenji Fujiu, Katsuhito Perelson, Alan S Iwami, Shingo Wakita, Takaji |
| description | Development of an effective antiviral drug for Coronavirus Disease 2019 (COVID-19) is a global health priority. Although several candidate drugs have been identified through in vitro and in vivo models, consistent and compelling evidence from clinical studies is limited. The lack of evidence from clinical trials may stem in part from the imperfect design of the trials. We investigated how clinical trials for antivirals need to be designed, especially focusing on the sample size in randomized controlled trials. A modeling study was conducted to help understand the reasons behind inconsistent clinical trial findings and to design better clinical trials. We first analyzed longitudinal viral load data for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) without antiviral treatment by use of a within-host virus dynamics model. The fitted viral load was categorized into 3 different groups by a clustering approach. Comparison of the estimated parameters showed that the 3 distinct groups were characterized by different virus decay rates (p-value < 0.001). The mean decay rates were 1.17 d.sup.-1 (95% CI: 1.06 to 1.27 d.sup.-1 ), 0.777 d.sup.-1 (0.716 to 0.838 d.sup.-1 ), and 0.450 d.sup.-1 (0.378 to 0.522 d.sup.-1) for the 3 groups, respectively. Such heterogeneity in virus dynamics could be a confounding variable if it is associated with treatment allocation in compassionate use programs (i.e., observational studies). In this study, we found that estimated association in observational studies can be biased due to large heterogeneity in viral dynamics among infected individuals, and statistically significant effect in randomized controlled trials may be difficult to be detected due to small sample size. The sample size can be dramatically reduced by recruiting patients immediately after developing symptoms. We believe this is the first study investigated the study design of clinical trials for antiviral treatment using the viral dynamics model. |
| doi_str_mv | 10.1371/journal.pmed.1003660 |
| format | Article |
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We investigated how clinical trials for antivirals need to be designed, especially focusing on the sample size in randomized controlled trials. A modeling study was conducted to help understand the reasons behind inconsistent clinical trial findings and to design better clinical trials. We first analyzed longitudinal viral load data for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) without antiviral treatment by use of a within-host virus dynamics model. The fitted viral load was categorized into 3 different groups by a clustering approach. Comparison of the estimated parameters showed that the 3 distinct groups were characterized by different virus decay rates (p-value < 0.001). The mean decay rates were 1.17 d.sup.-1 (95% CI: 1.06 to 1.27 d.sup.-1 ), 0.777 d.sup.-1 (0.716 to 0.838 d.sup.-1 ), and 0.450 d.sup.-1 (0.378 to 0.522 d.sup.-1) for the 3 groups, respectively. Such heterogeneity in virus dynamics could be a confounding variable if it is associated with treatment allocation in compassionate use programs (i.e., observational studies). In this study, we found that estimated association in observational studies can be biased due to large heterogeneity in viral dynamics among infected individuals, and statistically significant effect in randomized controlled trials may be difficult to be detected due to small sample size. The sample size can be dramatically reduced by recruiting patients immediately after developing symptoms. 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The mean decay rates were 1.17 d.sup.-1 (95% CI: 1.06 to 1.27 d.sup.-1 ), 0.777 d.sup.-1 (0.716 to 0.838 d.sup.-1 ), and 0.450 d.sup.-1 (0.378 to 0.522 d.sup.-1) for the 3 groups, respectively. Such heterogeneity in virus dynamics could be a confounding variable if it is associated with treatment allocation in compassionate use programs (i.e., observational studies). In this study, we found that estimated association in observational studies can be biased due to large heterogeneity in viral dynamics among infected individuals, and statistically significant effect in randomized controlled trials may be difficult to be detected due to small sample size. The sample size can be dramatically reduced by recruiting patients immediately after developing symptoms. 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Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Iwanami, Shoya</au><au>Ejima, Keisuke</au><au>Kim, Kwang Su</au><au>Noshita, Koji</au><au>Fujita, Yasuhisa</au><au>Miyazaki, Taiga</au><au>Kohno, Shigeru</au><au>Miyazaki, Yoshitsugu</au><au>Morimoto, Shimpei</au><au>Nakaoka, Shinji</au><au>Koizumi, Yoshiki</au><au>Asai, Yusuke</au><au>Aihara, Kazuyuki</au><au>Watashi, Koichi</au><au>Thompson, Robin N</au><au>Shibuya, Kenji</au><au>Fujiu, Katsuhito</au><au>Perelson, Alan S</au><au>Iwami, Shingo</au><au>Wakita, Takaji</au><au>Kretzschmar, Mirjam E. E.</au><aucorp>Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Detection of significant antiviral drug effects on COVID-19 with reasonable sample sizes in randomized controlled trials: A modeling study</atitle><jtitle>PLoS medicine</jtitle><date>2021-07-06</date><risdate>2021</risdate><volume>18</volume><issue>7</issue><spage>e1003660</spage><epage>e1003660</epage><pages>e1003660-e1003660</pages><issn>1549-1676</issn><issn>1549-1277</issn><eissn>1549-1676</eissn><abstract>Development of an effective antiviral drug for Coronavirus Disease 2019 (COVID-19) is a global health priority. Although several candidate drugs have been identified through in vitro and in vivo models, consistent and compelling evidence from clinical studies is limited. The lack of evidence from clinical trials may stem in part from the imperfect design of the trials. We investigated how clinical trials for antivirals need to be designed, especially focusing on the sample size in randomized controlled trials. A modeling study was conducted to help understand the reasons behind inconsistent clinical trial findings and to design better clinical trials. We first analyzed longitudinal viral load data for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) without antiviral treatment by use of a within-host virus dynamics model. The fitted viral load was categorized into 3 different groups by a clustering approach. Comparison of the estimated parameters showed that the 3 distinct groups were characterized by different virus decay rates (p-value < 0.001). The mean decay rates were 1.17 d.sup.-1 (95% CI: 1.06 to 1.27 d.sup.-1 ), 0.777 d.sup.-1 (0.716 to 0.838 d.sup.-1 ), and 0.450 d.sup.-1 (0.378 to 0.522 d.sup.-1) for the 3 groups, respectively. Such heterogeneity in virus dynamics could be a confounding variable if it is associated with treatment allocation in compassionate use programs (i.e., observational studies). In this study, we found that estimated association in observational studies can be biased due to large heterogeneity in viral dynamics among infected individuals, and statistically significant effect in randomized controlled trials may be difficult to be detected due to small sample size. The sample size can be dramatically reduced by recruiting patients immediately after developing symptoms. We believe this is the first study investigated the study design of clinical trials for antiviral treatment using the viral dynamics model.</abstract><cop>San Francisco</cop><pub>Public Library of Science</pub><pmid>34228712</pmid><doi>10.1371/journal.pmed.1003660</doi><orcidid>https://orcid.org/0000-0001-6626-316X</orcidid><orcidid>https://orcid.org/0000-0002-1780-350X</orcidid><orcidid>https://orcid.org/0000-0002-2455-0002</orcidid><orcidid>https://orcid.org/0000-0003-1474-5348</orcidid><orcidid>https://orcid.org/0000-0001-5124-1419</orcidid><orcidid>https://orcid.org/0000-0002-1720-3942</orcidid><orcidid>https://orcid.org/0000-0002-0962-5758</orcidid><orcidid>https://orcid.org/0000-0001-8942-0869</orcidid><orcidid>https://orcid.org/0000-0002-1185-3987</orcidid><orcidid>https://orcid.org/0000-0002-9510-5897</orcidid><orcidid>https://orcid.org/0000-0002-5340-6851</orcidid><orcidid>https://orcid.org/0000-0002-4602-9816</orcidid><orcidid>https://orcid.org/0000-0002-3277-9552</orcidid><orcidid>https://orcid.org/0000-0001-8545-5212</orcidid><orcidid>https://orcid.org/000000016626316X</orcidid><orcidid>https://orcid.org/0000000232779552</orcidid><orcidid>https://orcid.org/0000000209625758</orcidid><orcidid>https://orcid.org/000000021780350X</orcidid><orcidid>https://orcid.org/0000000295105897</orcidid><orcidid>https://orcid.org/0000000151241419</orcidid><orcidid>https://orcid.org/0000000217203942</orcidid><orcidid>https://orcid.org/0000000253406851</orcidid><orcidid>https://orcid.org/0000000246029816</orcidid><orcidid>https://orcid.org/0000000185455212</orcidid><orcidid>https://orcid.org/0000000189420869</orcidid><orcidid>https://orcid.org/0000000211853987</orcidid><orcidid>https://orcid.org/0000000314745348</orcidid><orcidid>https://orcid.org/0000000224550002</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1549-1676 |
| ispartof | PLoS medicine, 2021-07, Vol.18 (7), p.e1003660-e1003660 |
| issn | 1549-1676 1549-1277 1549-1676 |
| language | eng |
| recordid | cdi_plos_journals_2561939228 |
| source | DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Public Library of Science (PLoS); PubMed Central |
| subjects | 60 APPLIED LIFE SCIENCES Analysis Antiviral agents Antiviral drugs Antiviral therapy Antivirals Biological Science Biology and Life Sciences Clinical outcomes Clinical trials Coronaviruses COVID-19 Drug development Drugs Experimental design Mathematical models Medicine and Health Sciences Methods Parameter estimation Patients Placebos Public health Randomized controlled trials Research and Analysis Methods SARS CoV 2 Severe acute respiratory syndrome coronavirus 2 Statistical analysis Testing Viral load Viral replication Viruses |
| title | Detection of significant antiviral drug effects on COVID-19 with reasonable sample sizes in randomized controlled trials: A modeling study |
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