Pub Date : 2023-09-20DOI: 10.1080/19466315.2023.2261671
Oleksandr Sverdlov, Yevgen Ryeznik
AbstractVarious restricted randomization procedures are available to achieve equal (1:1) allocation in a randomized clinical trial. However, for some procedures, there is a nonnegligible probability of imbalance in the final numbers which may result in an underpowered study. It is important to assess such probability at the study planning stage and make adjustments in the design if needed. In this paper, we perform a quantitative assessment of the tradeoff between randomness, balance, and power of restricted randomization designs targeting equal allocation. First, we study the small-sample performance of biased coin designs with known asymptotic properties and identify a design with an excellent balance–randomness tradeoff. Second, we investigate the issue of randomization-induced treatment imbalance and the corresponding risk of an underpowered study. We propose two risk mitigation strategies: increasing the total sample size or fine-tuning the biased coin parameter to obtain the least restrictive randomization procedure that attains the target power with a high, user-defined probability for the given sample size. Additionally, we investigate an approach for finding the most balanced design that satisfies a constraint on the chosen measure of randomness. Our proposed methodology is simple and yet generalizable to more complex settings, such as trials with stratified randomization and multi-arm trials with possibly unequal randomization ratios.Keywords: Biased coin designequal allocationmaximum tolerated imbalancepowerrestricted randomizationvariability in the allocation proportionDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. Supplementary MaterialsThe R Markdown document with Julia and R code for performing simulations and summarizing/visualizing the simulation results is available at the journal website.AcknowledgementsThe authors are grateful to the two anonymous reviewers and the journal editors whose comments helped improve this manuscript.Disclosure StatementThe authors have no conflict of interest with regards to the contents presented in this paper.FundingThe author(s) reported there is no funding associated with the work featured in this article.
{"title":"Balancing the Objectives of Statistical Efficiency and Allocation Randomness in Randomized Controlled Trials","authors":"Oleksandr Sverdlov, Yevgen Ryeznik","doi":"10.1080/19466315.2023.2261671","DOIUrl":"https://doi.org/10.1080/19466315.2023.2261671","url":null,"abstract":"AbstractVarious restricted randomization procedures are available to achieve equal (1:1) allocation in a randomized clinical trial. However, for some procedures, there is a nonnegligible probability of imbalance in the final numbers which may result in an underpowered study. It is important to assess such probability at the study planning stage and make adjustments in the design if needed. In this paper, we perform a quantitative assessment of the tradeoff between randomness, balance, and power of restricted randomization designs targeting equal allocation. First, we study the small-sample performance of biased coin designs with known asymptotic properties and identify a design with an excellent balance–randomness tradeoff. Second, we investigate the issue of randomization-induced treatment imbalance and the corresponding risk of an underpowered study. We propose two risk mitigation strategies: increasing the total sample size or fine-tuning the biased coin parameter to obtain the least restrictive randomization procedure that attains the target power with a high, user-defined probability for the given sample size. Additionally, we investigate an approach for finding the most balanced design that satisfies a constraint on the chosen measure of randomness. Our proposed methodology is simple and yet generalizable to more complex settings, such as trials with stratified randomization and multi-arm trials with possibly unequal randomization ratios.Keywords: Biased coin designequal allocationmaximum tolerated imbalancepowerrestricted randomizationvariability in the allocation proportionDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. Supplementary MaterialsThe R Markdown document with Julia and R code for performing simulations and summarizing/visualizing the simulation results is available at the journal website.AcknowledgementsThe authors are grateful to the two anonymous reviewers and the journal editors whose comments helped improve this manuscript.Disclosure StatementThe authors have no conflict of interest with regards to the contents presented in this paper.FundingThe author(s) reported there is no funding associated with the work featured in this article.","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136307274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-18DOI: 10.1080/19466315.2023.2259829
Jie Chen, Daniel Scharfstein, Hongwei Wang, Binbing Yu, Yang Song, Weili He, John Scott, Xiwu Lin, Hana Lee
AbstractA Real-World Evidence (RWE) Scientific Working Group (SWG) of the American Statistical Association Biopharmaceutical Section (ASA BIOP) has been reviewing statistical considerations for the generation of RWE to support regulatory decision-making. As part of the effort, the working group is addressing estimands in RWE studies. Constructing the right estimand—the target of estimation—which reflects the research question and the study objective, is one of the key components in formulating a clinical study. ICH E9(R1) describes statistical principles for constructing estimands in clinical trials with a focus on five attributes—population, treatment, endpoints, intercurrent events, and population-level summary. However, defining estimands for clinical studies using real-world data (RWD), i.e., RWE studies, requires additional considerations due to, for example, heterogeneity of study population, complexity of treatment regimes, different types and patterns of intercurrent events, and complexities in choosing study endpoints. This paper reviews the essential components of estimands and causal inference framework, discusses considerations in constructing estimands for RWE studies, highlights similarities and differences in traditional clinical trial and RWE study estimands, and provides a roadmap for choosing appropriate estimands for RWE studies.Key words: Real-world evidencereal-world dataestimandestimand frameworkDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. FundingThe author(s) reported there is no funding associated with the work featured in this article.
{"title":"Estimands in Real-World Evidence Studies","authors":"Jie Chen, Daniel Scharfstein, Hongwei Wang, Binbing Yu, Yang Song, Weili He, John Scott, Xiwu Lin, Hana Lee","doi":"10.1080/19466315.2023.2259829","DOIUrl":"https://doi.org/10.1080/19466315.2023.2259829","url":null,"abstract":"AbstractA Real-World Evidence (RWE) Scientific Working Group (SWG) of the American Statistical Association Biopharmaceutical Section (ASA BIOP) has been reviewing statistical considerations for the generation of RWE to support regulatory decision-making. As part of the effort, the working group is addressing estimands in RWE studies. Constructing the right estimand—the target of estimation—which reflects the research question and the study objective, is one of the key components in formulating a clinical study. ICH E9(R1) describes statistical principles for constructing estimands in clinical trials with a focus on five attributes—population, treatment, endpoints, intercurrent events, and population-level summary. However, defining estimands for clinical studies using real-world data (RWD), i.e., RWE studies, requires additional considerations due to, for example, heterogeneity of study population, complexity of treatment regimes, different types and patterns of intercurrent events, and complexities in choosing study endpoints. This paper reviews the essential components of estimands and causal inference framework, discusses considerations in constructing estimands for RWE studies, highlights similarities and differences in traditional clinical trial and RWE study estimands, and provides a roadmap for choosing appropriate estimands for RWE studies.Key words: Real-world evidencereal-world dataestimandestimand frameworkDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. FundingThe author(s) reported there is no funding associated with the work featured in this article.","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135149619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-15DOI: 10.1080/19466315.2023.2241405
Qiang Zhao, Haijun Ma
Incorporating historical information in clinical trials has been of much interest recently because of its potential to reduce the size and cost of clinical trials. Data-conflict is one of the biggest challenges in incorporating historical information. In order to address the conflict between historical data and current data, several methods have been proposed including the robust meta-analytic-predictive (rMAP) prior method. In this paper, we propose to modify the rMAP prior method by using an empirical Bayes approach to estimate the weights for the two components of the rMAP prior. Via numerical calculations, we show that this modification to the rMAP method improves its performance regarding multiple key metrics.
{"title":"Modified Robust Meta-Analytic-Predictive Priors for Incorporating Historical Controls in Clinical Trials","authors":"Qiang Zhao, Haijun Ma","doi":"10.1080/19466315.2023.2241405","DOIUrl":"https://doi.org/10.1080/19466315.2023.2241405","url":null,"abstract":"Incorporating historical information in clinical trials has been of much interest recently because of its potential to reduce the size and cost of clinical trials. Data-conflict is one of the biggest challenges in incorporating historical information. In order to address the conflict between historical data and current data, several methods have been proposed including the robust meta-analytic-predictive (rMAP) prior method. In this paper, we propose to modify the rMAP prior method by using an empirical Bayes approach to estimate the weights for the two components of the rMAP prior. Via numerical calculations, we show that this modification to the rMAP method improves its performance regarding multiple key metrics.","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135353574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-29DOI: 10.1080/19466315.2023.2238645
Ruqayya A. Azher, J. Wason, Michael Grayling
{"title":"A Comparison of Randomization Methods for Multi-Arm Clinical Trials","authors":"Ruqayya A. Azher, J. Wason, Michael Grayling","doi":"10.1080/19466315.2023.2238645","DOIUrl":"https://doi.org/10.1080/19466315.2023.2238645","url":null,"abstract":"","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42219768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-24DOI: 10.1080/19466315.2023.2252150
Leroy Jide Ovbude, Luca Grassano, B. Cheuvart, F. Solmi
{"title":"Statistical inference for vaccine efficacy: a re-randomization procedure to analyse Poisson outcomes under covariate-adaptive randomization.","authors":"Leroy Jide Ovbude, Luca Grassano, B. Cheuvart, F. Solmi","doi":"10.1080/19466315.2023.2252150","DOIUrl":"https://doi.org/10.1080/19466315.2023.2252150","url":null,"abstract":"","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46264250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-15DOI: 10.1080/19466315.2023.2239521
Kerstine Carter, A. Scheffold, Jone Renteria, V. Berger, Yuqun Abigail Luo, J. Chipman, O. Sverdlov
{"title":"Regulatory Guidance on Randomization and the Use of Randomization Tests in Clinical Trials: A Systematic Review","authors":"Kerstine Carter, A. Scheffold, Jone Renteria, V. Berger, Yuqun Abigail Luo, J. Chipman, O. Sverdlov","doi":"10.1080/19466315.2023.2239521","DOIUrl":"https://doi.org/10.1080/19466315.2023.2239521","url":null,"abstract":"","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43510143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-04DOI: 10.1080/19466315.2023.2241399
Gu Mi, Yue-Jeng Yang, Z. Jin, Ji Lin, C. Lorenzato
{"title":"Enhancement of Basket Trial Designs with Incorporation of a Bayesian Three-Outcome Decision-Making Framework","authors":"Gu Mi, Yue-Jeng Yang, Z. Jin, Ji Lin, C. Lorenzato","doi":"10.1080/19466315.2023.2241399","DOIUrl":"https://doi.org/10.1080/19466315.2023.2241399","url":null,"abstract":"","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43259712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-25DOI: 10.1080/19466315.2023.2241398
Qi Zhang, Yuqian Shen, H. Quan, P. Minini, Lin Wang
{"title":"A Case Study of 2-stage Seamless Adaptive Sample Size Re-Estimation Design with Efficacy Interim analysis When Slope is the Primary Endpoint","authors":"Qi Zhang, Yuqian Shen, H. Quan, P. Minini, Lin Wang","doi":"10.1080/19466315.2023.2241398","DOIUrl":"https://doi.org/10.1080/19466315.2023.2241398","url":null,"abstract":"","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44395211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-25DOI: 10.1080/19466315.2023.2238650
R. Sridhara, Olga V. Marchenko, Qi Jiang, Elizabeth Barksdale, T. Alonzo, Anup K Amatya, David F. Arons, Alex Bliu, Qiuyi Choo, M. Coory, Martha Donoghue, L. Ehrlich, Leonardo Fabio Costa Filho, E. Fox, B. Freidlin, Nancy Goodman, D. Hawkins, D. Häring, Dominik Karres, E. Kolb, Helen Mao, Pallavi S. Mishra Kalyani, A. Naranjo, A. Pappo, M. Posch, Karen L. Price, A. Raven, K. Rantell, Lindsay Renfro, D. Rivera, Pourab Roy, Ming-wei Shan, Richard Simon, Sonia Singh, Malcolm Smith, M. Theoret, Marius Thomas, Z. Thomas, A. Thompson, Hong Tian, Y. Tymofyeyev, Jonathon Vallejo, K. Wathen, Jingjing Ye, R. Pazdur, G. Reaman
Abstract This article provides a summary of discussions from the American Statistical Association (ASA) Biopharmaceutical (BIOP) Section Open Forum organized by the ASA BIOP Statistical Methods in Oncology Scientific Working Group in coordination with the US FDA Oncology Center of Excellence and LUNGevity Foundation on June 24, 2021, and January 13, 2022. Diverse stakeholders engaged in a discussion on how best to use various innovative clinical trial designs in designing future pediatric oncology trials. While standard randomized controlled trials are preferred to evaluate treatment effect in an unbiased manner, given the rarity of pediatric cancers, innovative strategies are needed to promote and assure timely cancer drug development in pediatric populations. The discussions highlighted the need to consider innovative designs with less stringent Type I error specification and Bayesian designs borrowing from external control data, or borrowing treatment effect information from adult data, or both. Such designs are available in the literature and some examples are summarized under the FDA Complex Innovative Trials Design Pilot Program (https://www.fda.gov/drugs/development-resources/complex-innovative-trial-design-meeting-program). Early consultation with global regulatory agencies for pediatric clinical trials can provide a better understanding of different features of the clinical trial design options for successful pediatric cancer drug development.
{"title":"Statistical Considerations in Pediatric Cancer Trials: Report of American Statistical Association Biopharmaceutical Section Open Forum Discussions","authors":"R. Sridhara, Olga V. Marchenko, Qi Jiang, Elizabeth Barksdale, T. Alonzo, Anup K Amatya, David F. Arons, Alex Bliu, Qiuyi Choo, M. Coory, Martha Donoghue, L. Ehrlich, Leonardo Fabio Costa Filho, E. Fox, B. Freidlin, Nancy Goodman, D. Hawkins, D. Häring, Dominik Karres, E. Kolb, Helen Mao, Pallavi S. Mishra Kalyani, A. Naranjo, A. Pappo, M. Posch, Karen L. Price, A. Raven, K. Rantell, Lindsay Renfro, D. Rivera, Pourab Roy, Ming-wei Shan, Richard Simon, Sonia Singh, Malcolm Smith, M. Theoret, Marius Thomas, Z. Thomas, A. Thompson, Hong Tian, Y. Tymofyeyev, Jonathon Vallejo, K. Wathen, Jingjing Ye, R. Pazdur, G. Reaman","doi":"10.1080/19466315.2023.2238650","DOIUrl":"https://doi.org/10.1080/19466315.2023.2238650","url":null,"abstract":"Abstract This article provides a summary of discussions from the American Statistical Association (ASA) Biopharmaceutical (BIOP) Section Open Forum organized by the ASA BIOP Statistical Methods in Oncology Scientific Working Group in coordination with the US FDA Oncology Center of Excellence and LUNGevity Foundation on June 24, 2021, and January 13, 2022. Diverse stakeholders engaged in a discussion on how best to use various innovative clinical trial designs in designing future pediatric oncology trials. While standard randomized controlled trials are preferred to evaluate treatment effect in an unbiased manner, given the rarity of pediatric cancers, innovative strategies are needed to promote and assure timely cancer drug development in pediatric populations. The discussions highlighted the need to consider innovative designs with less stringent Type I error specification and Bayesian designs borrowing from external control data, or borrowing treatment effect information from adult data, or both. Such designs are available in the literature and some examples are summarized under the FDA Complex Innovative Trials Design Pilot Program (https://www.fda.gov/drugs/development-resources/complex-innovative-trial-design-meeting-program). Early consultation with global regulatory agencies for pediatric clinical trials can provide a better understanding of different features of the clinical trial design options for successful pediatric cancer drug development.","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41670769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-03DOI: 10.1080/19466315.2022.2096688
Freda Cooner, R. Liao, Junjing Lin, Sophie Barthel, Y. Seifu, Shiling Ruan
Abstract Starting in early 2020, a fast-ravaging viral infection erupted and caused the COVID-19 (coronavirus disease of 2019) pandemic. The disease rapidly spread across the world and has altered people’s lifestyle since its first reporting. Many scientists and medical practitioners have strived to understand the disease and research for treatments and vaccines. As real-world data quickly accumulate, the general public reacts to new findings and government bodies enforce preventive measures accordingly. These actions subsequently alter the real-world data pattern and structure. It creates great challenges in interpreting this maze of data. This article delves into the specificity of COVID-19 real-world data; summarizes some existing COVID-19 databases and the disease modeling strategies; outlines potential trial designs incorporating real-world data to meet evidentiary requirements for treatment effect demonstration; and then presents a few case examples. It provides statistical considerations for real-world data utilization in understanding COVID-19 and finding potential treatments and preventive care.
{"title":"Leveraging Real-World Data in COVID-19 Response","authors":"Freda Cooner, R. Liao, Junjing Lin, Sophie Barthel, Y. Seifu, Shiling Ruan","doi":"10.1080/19466315.2022.2096688","DOIUrl":"https://doi.org/10.1080/19466315.2022.2096688","url":null,"abstract":"Abstract Starting in early 2020, a fast-ravaging viral infection erupted and caused the COVID-19 (coronavirus disease of 2019) pandemic. The disease rapidly spread across the world and has altered people’s lifestyle since its first reporting. Many scientists and medical practitioners have strived to understand the disease and research for treatments and vaccines. As real-world data quickly accumulate, the general public reacts to new findings and government bodies enforce preventive measures accordingly. These actions subsequently alter the real-world data pattern and structure. It creates great challenges in interpreting this maze of data. This article delves into the specificity of COVID-19 real-world data; summarizes some existing COVID-19 databases and the disease modeling strategies; outlines potential trial designs incorporating real-world data to meet evidentiary requirements for treatment effect demonstration; and then presents a few case examples. It provides statistical considerations for real-world data utilization in understanding COVID-19 and finding potential treatments and preventive care.","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42583569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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