Statistics in Biopharmaceutical Research最新文献

{"title":"Predicting Probability of Success for Phase III Trials via Propensity-Score-Based External Data Borrowing","authors":"Jennifer L. Proper, Veronica Bunn, Bradley Hupf, Jianchang Lin","doi":"10.1080/19466315.2023.2292815","DOIUrl":"https://doi.org/10.1080/19466315.2023.2292815","url":null,"abstract":"Given the rising costs and time length of confirmatory phase III trials, drug developers have become increasingly reliant on quantitative methods to support critical decisions such as whether drug ...","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":"18 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138631875","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}

{"title":"Treatment Comparisons in Adaptive Platform Trials Adjusting for Temporal Drift","authors":"Beibei Guo, Li Wang, Ying Yuan","doi":"10.1080/19466315.2023.2292238","DOIUrl":"https://doi.org/10.1080/19466315.2023.2292238","url":null,"abstract":"An adaptive platform trial (APT) is a multi-arm trial in the context of a single disease where treatment arms are allowed to enter or leave the trial based on some decision rule. If a treatment ent...","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":"21 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138567738","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}

{"title":"DODII: Bayesian Dose Optimization Design for Randomized Phase II Trials","authors":"Ziji Yu, Yanzhao Wang, Jianchang Lin","doi":"10.1080/19466315.2023.2292816","DOIUrl":"https://doi.org/10.1080/19466315.2023.2292816","url":null,"abstract":"The traditional MTD-based dose selection paradigm commonly used for cytotoxic chemotherapies might not be optimal for targeted therapies because a higher dose does not necessarily result in improve...","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":"10 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138575947","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}

{"title":"Joint Analysis of Longitudinal Ordinal Categorical Item Response Data and Survival Times with Cure Fraction","authors":"Ming Chi, Xiaogang Wang, Hui Song, Yingwei Peng, Dongsheng Tu","doi":"10.1080/19466315.2023.2290642","DOIUrl":"https://doi.org/10.1080/19466315.2023.2290642","url":null,"abstract":"For longitudinal ordinal categorical item response data which may not be observable after a subject develops a terminal event, some statistical models have been proposed for the joint analysis of t...","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":"243 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138516362","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}

{"title":"Adjusting for time-varying treatment switches in randomized clinical trials: the danger of extrapolation and how to address it","authors":"Hege Michiels, An Vandebosch, Stijn Vansteelandt","doi":"10.1080/19466315.2023.2289523","DOIUrl":"https://doi.org/10.1080/19466315.2023.2289523","url":null,"abstract":"When choosing estimands and estimators in randomized clinical trials, caution is warranted, as intercurrent events, such as, due to patients who switch treatment after disease progression, are ofte...","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":"17 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138516352","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}

{"title":"Isotonic Phase I cancer clinical trial design utilizing patient-reported outcomes","authors":"Nolan A. Wages, Ruitao Lin","doi":"10.1080/19466315.2023.2288013","DOIUrl":"https://doi.org/10.1080/19466315.2023.2288013","url":null,"abstract":"This article considers the concept of designing Phase I clinical trials using both clinician- and patient-reported outcomes to adaptively allocate study participants to tolerable doses and determin...","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":"13 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138516325","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}

{"title":"A method for ensuring a consistent dose-response relationship between an entire population and one region in multiregional dose-response studies using MCP-Mod","authors":"Shuhei Kaneko","doi":"10.1080/19466315.2023.2277175","DOIUrl":"https://doi.org/10.1080/19466315.2023.2277175","url":null,"abstract":"","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135870271","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}

{"title":"Using Randomization Tests to Address Disruptions in Clinical Trials: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions","authors":"Diane Uschner, Oleksandr Sverdlov, Kerstine Carter, Jonathan Chipman, Olga Kuznetsova, Jone Renteria, Adam Lane, Chris Barker, Nancy Geller, Michael Proschan, Martin Posch, Sergey Tarima, Frank Bretz, William F. Rosenberger","doi":"10.1080/19466315.2023.2257894","DOIUrl":"https://doi.org/10.1080/19466315.2023.2257894","url":null,"abstract":"1. AbstractRecent examples for unplanned external events are the global COVID-19 pandemic, the war in Ukraine, or most recently Hurricane Ian in Puerto Rico. Disruptions due to unplanned external events can lead to violation of assumptions in clinical trials. In certain situations, randomization tests can provide non-parametric inference that is robust to violation of the assumptions usually made in clinical trials. The ICH E9 (R1) Addendum on estimands and sensitivity analyses provides a guideline for aligning the trial objectives with strategies to address disruptions in clinical trials. In this paper, we embed randomization tests within the estimand framework to allow for inference following disruptions in clinical trials in a way that reflects recent literature. A stylized clinical trial is presented to illustrate the method, and a simulation study highlights situations when a randomization test that is conducted under the intention-to-treat principle can provide unbiased results.DisclaimerAs 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":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135823840","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}

{"title":"A randomization-based theory for preliminary testing of covariate balance in controlled trials","authors":"Anqi Zhao, Peng Ding","doi":"10.1080/19466315.2023.2267774","DOIUrl":"https://doi.org/10.1080/19466315.2023.2267774","url":null,"abstract":"AbstractRandomized trials balance all covariates on average and are the gold standard for estimating treatment effects. Chance imbalances nevertheless exist more or less in realized treatment allocations and intrigue an important question: what should we do if the treatment groups differ with respect to some important baseline characteristics? A common strategy is to conduct a preliminary test of the balance of baseline covariates after randomization, and invoke covariate adjustment for subsequent inference if and only if the realized allocation fails some prespecified criterion. Although such practice is intuitive and popular among practitioners, the existing literature has so far only evaluated its properties under strong parametric model assumptions in theory and simulation, yielding results of limited generality. To fill this gap, we examine two strategies for conducting preliminary test-based covariate adjustment by regression, and evaluate the validity and efficiency of the resulting inferences from the randomization-based perspective. The main result is twofold. First, the preliminary-test estimator based on the analysis of covariance can be even less efficient than the unadjusted difference in means, and risks anticonservative confidence intervals based on normal approximation even with the robust standard error. Second, the preliminary-test estimator based on the fully interacted specification is less efficient than its counterpart under the always-adjust strategy, and yields overconservative confidence intervals based on normal approximation. In addition, although the Fisher randomization test is still finite-sample exact for testing the sharp null hypothesis of no treatment effect on any individual, it is no longer valid for testing the weak null hypothesis of zero average treatment effect in large samples even with properly studentized test statistics. These undesirable properties are due to the asymptotic non-normality of the preliminary-test estimators. Based on theory and simulation, we echo the existing literature and do not recommend the preliminary-test procedure for covariate adjustment in randomized trials.Keywords: Causal inferencedesign-based inferenceefficiencyFisher randomization testregression adjustmentrerandomizationDisclaimerAs 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":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135854492","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}

{"title":"Covariate-adaptive biased coin randomization for master protocols with multiple interventions and biomarker-stratified allocation","authors":"Tianhao Song, Lisa M. LaVange, Anastasia Ivanova","doi":"10.1080/19466315.2023.2268313","DOIUrl":"https://doi.org/10.1080/19466315.2023.2268313","url":null,"abstract":"AbstractIn a multi-arm trial with predefined subgroups for each intervention to target, it is often desirable to enrich assignment to an intervention by enrolling more biomarker-positive participants to the intervention. We describe how to implement a biased coin design to achieve desired allocation ratios among interventions and between the number of biomarker-positive and biomarker-negative participants assigned to each intervention. We illustrate the proposed method with the randomization algorithm implemented in the Precision Interventions for Severe and/or Exacerbation-prone Asthma (PrecISE) trial.Key Words: Covariate-adaptive randomizationenrichmentbiomarker-positive subgroupbiased coin designDisclaimerAs 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":"287 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135093690","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}