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Reparametrized Firth's Logistic Regressions for Dose-Finding Study With the Biased-Coin Design. 采用偏币设计的剂量寻找研究中的重拟合 Firth Logistic 回归。
IF 1.3 4区 医学
Pharmaceutical Statistics Pub Date : 2024-11-01 Epub Date: 2024-07-16 DOI: 10.1002/pst.2423
Hyungwoo Kim, Seungpil Jung, Yudi Pawitan, Woojoo Lee
{"title":"Reparametrized Firth's Logistic Regressions for Dose-Finding Study With the Biased-Coin Design.","authors":"Hyungwoo Kim, Seungpil Jung, Yudi Pawitan, Woojoo Lee","doi":"10.1002/pst.2423","DOIUrl":"10.1002/pst.2423","url":null,"abstract":"<p><p>Finding an adequate dose of the drug by revealing the dose-response relationship is very crucial and a challenging problem in the clinical development. The main concerns in dose-finding study are to identify a minimum effective dose (MED) in anesthesia studies and maximum tolerated dose (MTD) in oncology clinical trials. For the estimation of MED and MTD, we propose two modifications of Firth's logistic regression using reparametrization, called reparametrized Firth's logistic regression (rFLR) and ridge-penalized reparametrized Firth's logistic regression (RrFLR). The proposed methods are designed by directly reducing the small-sample bias of the maximum likelihood estimate for the parameter of interest. In addition, we develop a method on how to construct confidence intervals for rFLR and RrFLR using profile penalized likelihood. In the up-and-down biased-coin design, numerical studies confirm the superior performance of the proposed methods in terms of the mean squared error, bias, and coverage accuracy of confidence intervals.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"1117-1127"},"PeriodicalIF":1.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141627326","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}
引用次数: 0
Optimal Cut-Point Selection Methods Under Binary Classification When Subclasses Are Involved. 二元分类下涉及子类时的最佳切点选择方法
IF 1.3 4区 医学
Pharmaceutical Statistics Pub Date : 2024-11-01 Epub Date: 2024-07-07 DOI: 10.1002/pst.2413
Jia Wang, Lili Tian
{"title":"Optimal Cut-Point Selection Methods Under Binary Classification When Subclasses Are Involved.","authors":"Jia Wang, Lili Tian","doi":"10.1002/pst.2413","DOIUrl":"10.1002/pst.2413","url":null,"abstract":"<p><p>In practice, we often encounter binary classification problems where both main classes consist of multiple subclasses. For example, in an ovarian cancer study where biomarkers were evaluated for their accuracy of distinguishing noncancer cases from cancer cases, the noncancer class consists of healthy subjects and benign cases, while the cancer class consists of subjects at both early and late stages. This article aims to provide a large number of optimal cut-point selection methods for such setting. Furthermore, we also study confidence interval estimation of the optimal cut-points. Simulation studies are carried out to explore the performance of the proposed cut-point selection methods as well as confidence interval estimation methods. A real ovarian cancer data set is analyzed using the proposed methods.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"984-1030"},"PeriodicalIF":1.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141555330","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}
引用次数: 0
Visualizing hypothesis tests in survival analysis under anticipated delayed effects. 预期延迟效应下生存分析中的可视化假设检验
IF 1.3 4区 医学
Pharmaceutical Statistics Pub Date : 2024-11-01 Epub Date: 2024-05-06 DOI: 10.1002/pst.2393
José L Jiménez, Isobel Barrott, Francesca Gasperoni, Dominic Magirr
{"title":"Visualizing hypothesis tests in survival analysis under anticipated delayed effects.","authors":"José L Jiménez, Isobel Barrott, Francesca Gasperoni, Dominic Magirr","doi":"10.1002/pst.2393","DOIUrl":"10.1002/pst.2393","url":null,"abstract":"<p><p>What can be considered an appropriate statistical method for the primary analysis of a randomized clinical trial (RCT) with a time-to-event endpoint when we anticipate non-proportional hazards owing to a delayed effect? This question has been the subject of much recent debate. The standard approach is a log-rank test and/or a Cox proportional hazards model. Alternative methods have been explored in the statistical literature, such as weighted log-rank tests and tests based on the Restricted Mean Survival Time (RMST). While weighted log-rank tests can achieve high power compared to the standard log-rank test, some choices of weights may lead to type-I error inflation under particular conditions. In addition, they are not linked to a mathematically unambiguous summary measure. Test statistics based on the RMST, on the other hand, allow one to investigate the average difference between two survival curves up to a pre-specified time point <math><mrow><mi>τ</mi></mrow> </math> -a mathematically unambiguous summary measure. However, by emphasizing differences prior to <math><mrow><mi>τ</mi></mrow> </math> , such test statistics may not fully capture the benefit of a new treatment in terms of long-term survival. In this article, we introduce a graphical approach for direct comparison of weighted log-rank tests and tests based on the RMST. This new perspective allows a more informed choice of the analysis method, going beyond power and type I error comparison.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"870-883"},"PeriodicalIF":1.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140859909","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}
引用次数: 0
Using an early outcome as the sole source of information of interim decisions regarding treatment effect on a long-term endpoint: The non-Gaussian case. 将早期结果作为临时决定对长期终点治疗效果的唯一信息来源：非高斯情况
IF 1.3 4区 医学
Pharmaceutical Statistics Pub Date : 2024-11-01 Epub Date: 2024-06-05 DOI: 10.1002/pst.2398
Leandro Garcia Barrado, Tomasz Burzykowski
{"title":"Using an early outcome as the sole source of information of interim decisions regarding treatment effect on a long-term endpoint: The non-Gaussian case.","authors":"Leandro Garcia Barrado, Tomasz Burzykowski","doi":"10.1002/pst.2398","DOIUrl":"10.1002/pst.2398","url":null,"abstract":"<p><p>In randomized clinical trials that use a long-term efficacy endpoint, the follow-up time necessary to observe the endpoint may be substantial. In such trials, an attractive option is to consider an interim analysis based solely on an early outcome that could be used to expedite the evaluation of treatment's efficacy. Garcia Barrado et al. (Pharm Stat. 2022; 21: 209-219) developed a methodology that allows introducing such an early interim analysis for the case when both the early outcome and the long-term endpoint are normally-distributed, continuous variables. We extend the methodology to any combination of the early-outcome and long-term-endpoint types. As an example, we consider the case of a binary outcome and a time-to-event endpoint. We further evaluate the potential gain in operating characteristics (power, expected trial duration, and expected sample size) of a trial with such an interim analysis in function of the properties of the early outcome as a surrogate for the long-term endpoint.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"928-938"},"PeriodicalIF":1.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141261163","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}
引用次数: 0
Variable Duration Trial as an Alternative Design for Continuous Endpoints. 可变持续时间试验作为连续终点的替代设计
IF 1.3 4区 医学
Pharmaceutical Statistics Pub Date : 2024-11-01 Epub Date: 2024-07-11 DOI: 10.1002/pst.2418
Jitendra Ganju, Julie Guoguang Ma
{"title":"Variable Duration Trial as an Alternative Design for Continuous Endpoints.","authors":"Jitendra Ganju, Julie Guoguang Ma","doi":"10.1002/pst.2418","DOIUrl":"10.1002/pst.2418","url":null,"abstract":"<p><p>Clinical trials with continuous primary endpoints typically measure outcomes at baseline, at a fixed timepoint (denoted T <sub>min</sub>), and at intermediate timepoints. The analysis is commonly performed using the mixed model repeated measures method. It is sometimes expected that the effect size will be larger with follow-up longer than T <sub>min</sub>. But extending the follow-up for all patients delays trial completion. We propose an alternative trial design and analysis method that potentially increases statistical power without extending the trial duration or increasing the sample size. We propose following the last enrolled patient until T <sub>min</sub>, with earlier enrollees having variable follow-up durations up to a maximum of T <sub>max</sub>. The sample size at T <sub>max</sub> will be smaller than at T <sub>min</sub>, and due to staggered enrollment, data missing at T <sub>max</sub> will be missing completely at random. For analysis, we propose an alpha-adjusted procedure based on the smaller of the p values at T <sub>min</sub> and T <sub>max</sub>, termed <math> <semantics><mrow><mtext>minP</mtext></mrow> </semantics> </math> . This approach can provide the highest power when the powers at T <sub>min</sub> and T <sub>max</sub> are similar. If the power at T <sub>min</sub> and T <sub>max</sub> differ significantly, the power of <math> <semantics><mrow><mtext>minP</mtext></mrow> </semantics> </math> is modestly reduced compared with the larger of the two powers. Rare disease trials, due to the limited size of the patient population, may benefit the most with this design.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"1059-1064"},"PeriodicalIF":1.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141591009","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}
引用次数: 0
Sample Size Estimation Using a Partially Clustered Frailty Model for Biomarker-Strategy Designs With Multiple Treatments. 使用部分聚类虚弱模型估算具有多种治疗方法的生物标记物策略设计的样本量。
IF 1.3 4区 医学
Pharmaceutical Statistics Pub Date : 2024-11-01 Epub Date: 2024-07-16 DOI: 10.1002/pst.2407
Derek Dinart, Virginie Rondeau, Carine Bellera
{"title":"Sample Size Estimation Using a Partially Clustered Frailty Model for Biomarker-Strategy Designs With Multiple Treatments.","authors":"Derek Dinart, Virginie Rondeau, Carine Bellera","doi":"10.1002/pst.2407","DOIUrl":"10.1002/pst.2407","url":null,"abstract":"<p><p>Biomarker-guided therapy is a growing area of research in medicine. To optimize the use of biomarkers, several study designs including the biomarker-strategy design (BSD) have been proposed. Unlike traditional designs, the emphasis here is on comparing treatment strategies and not on treatment molecules as such. Patients are assigned to either a biomarker-based strategy (BBS) arm, in which biomarker-positive patients receive an experimental treatment that targets the identified biomarker, or a non-biomarker-based strategy (NBBS) arm, in which patients receive treatment regardless of their biomarker status. We proposed a simulation method based on a partially clustered frailty model (PCFM) as well as an extension of Freidlin formula to estimate the sample size required for BSD with multiple targeted treatments. The sample size was mainly influenced by the heterogeneity of treatment effect, the proportion of biomarker-negative patients, and the randomization ratio. The PCFM is well suited for the data structure and offers an alternative to traditional methodologies.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"1084-1094"},"PeriodicalIF":1.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141627249","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}
引用次数: 0
A model-assisted design for partially or completely ordered groups. 部分或完全有序群体的模型辅助设计。
IF 1.3 4区 医学
Pharmaceutical Statistics Pub Date : 2024-11-01 Epub Date: 2024-05-20 DOI: 10.1002/pst.2396
Connor Celum, Mark Conaway
{"title":"A model-assisted design for partially or completely ordered groups.","authors":"Connor Celum, Mark Conaway","doi":"10.1002/pst.2396","DOIUrl":"10.1002/pst.2396","url":null,"abstract":"<p><p>This paper proposes a trial design for locating group-specific doses when groups are partially or completely ordered by dose sensitivity. Previous trial designs for partially ordered groups are model-based, whereas the proposed method is model-assisted, providing clinicians with a design that is simpler. The proposed method performs similarly to model-based methods, providing simplicity without losing accuracy. Additionally, to the best of our knowledge, the proposed method is the first paper on dose-finding for partially ordered groups with convergence results. To generalize the proposed method, a framework is introduced that allows partial orders to be transferred to a grid format with a known ordering across rows but an unknown ordering within rows.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"906-927"},"PeriodicalIF":1.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11602961/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141071600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Do You Want to Stay Single? Considerations on Single-Arm Trials in Drug Development and the Postregulatory Space. 您想继续单臂试验吗？药物开发中的单臂试验和后监管空间的考虑。
IF 1.3 4区 医学
Pharmaceutical Statistics Pub Date : 2024-11-01 Epub Date: 2024-06-25 DOI: 10.1002/pst.2412
Yulia Dyachkova, Cornelia Dunger-Baldauf, Nathalie Barbier, Jenny Devenport, Stefan Franzén, Gbenga Kazeem, Thomas Künzel, Pierre Mancini, Giacomo Mordenti, Knut Richert, Antonia Ridolfi, Daniel Saure
{"title":"Do You Want to Stay Single? Considerations on Single-Arm Trials in Drug Development and the Postregulatory Space.","authors":"Yulia Dyachkova, Cornelia Dunger-Baldauf, Nathalie Barbier, Jenny Devenport, Stefan Franzén, Gbenga Kazeem, Thomas Künzel, Pierre Mancini, Giacomo Mordenti, Knut Richert, Antonia Ridolfi, Daniel Saure","doi":"10.1002/pst.2412","DOIUrl":"10.1002/pst.2412","url":null,"abstract":"<p><p>Single-arm trials (SATs), while not preferred, remain in use throughout the drug development cycle. They may be accepted by regulators in particular contexts (e.g., in oncology or rare diseases) when the potential effects of new treatments are very large and placebo treatment is unethical. However, in the postregulatory space, SATs are common, and perhaps even more poorly suited to address the questions of interest. In this manuscript, we review regulatory and HTA positions on SATs; challenges posed by SATs to address research questions beyond regulators, evolving statistical methods to provide context for SATs, case studies where SATs could and could not address questions of interest, and communication strategies to influence decision making and optimize study design to address evidence needs.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"1206-1217"},"PeriodicalIF":1.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141458675","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}
引用次数: 0
Handling Partially Observed Trial Data After Treatment Withdrawal: Introducing Retrieved Dropout Reference-Base Centred Multiple Imputation. 处理治疗退出后的部分观察试验数据：引入以检索到的辍学参考基数为中心的多重估算。
IF 1.3 4区 医学
Pharmaceutical Statistics Pub Date : 2024-11-01 Epub Date: 2024-07-16 DOI: 10.1002/pst.2416
Suzie Cro, James H Roger, James R Carpenter
{"title":"Handling Partially Observed Trial Data After Treatment Withdrawal: Introducing Retrieved Dropout Reference-Base Centred Multiple Imputation.","authors":"Suzie Cro, James H Roger, James R Carpenter","doi":"10.1002/pst.2416","DOIUrl":"10.1002/pst.2416","url":null,"abstract":"<p><p>The ICH E9(R1) Addendum (International Council for Harmonization 2019) suggests treatment-policy as one of several strategies for addressing intercurrent events such as treatment withdrawal when defining an estimand. This strategy requires the monitoring of patients and collection of primary outcome data following termination of randomised treatment. However, when patients withdraw from a study early before completion this creates true missing data complicating the analysis. One possible way forward uses multiple imputation to replace the missing data based on a model for outcome on- and off-treatment prior to study withdrawal, often referred to as retrieved dropout multiple imputation. This article introduces a novel approach to parameterising this imputation model so that those parameters which may be difficult to estimate have mildly informative Bayesian priors applied during the imputation stage. A core reference-based model is combined with a retrieved dropout compliance model, using both on- and off-treatment data, to form an extended model for the purposes of imputation. This alleviates the problem of specifying a complex set of analysis rules to accommodate situations where parameters which influence the estimated value are not estimable, or are poorly estimated leading to unrealistically large standard errors in the resulting analysis. We refer to this new approach as retrieved dropout reference-base centred multiple imputation.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"1095-1116"},"PeriodicalIF":1.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11602953/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141627325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Bayesian Hierarchical Models for Subgroup Analysis. 用于分组分析的贝叶斯层次模型。
IF 1.3 4区 医学
Pharmaceutical Statistics Pub Date : 2024-11-01 Epub Date: 2024-07-15 DOI: 10.1002/pst.2424
Yun Wang, Wenda Tu, William Koh, James Travis, Robert Abugov, Kiya Hamilton, Mengjie Zheng, Roberto Crackel, Pablo Bonangelino, Mark Rothmann
{"title":"Bayesian Hierarchical Models for Subgroup Analysis.","authors":"Yun Wang, Wenda Tu, William Koh, James Travis, Robert Abugov, Kiya Hamilton, Mengjie Zheng, Roberto Crackel, Pablo Bonangelino, Mark Rothmann","doi":"10.1002/pst.2424","DOIUrl":"10.1002/pst.2424","url":null,"abstract":"<p><p>In conventional subgroup analyses, subgroup treatment effects are estimated using data from each subgroup separately without considering data from other subgroups in the same study. The subgroup treatment effects estimated this way may be heterogenous with high variability due to small sample sizes in some subgroups and much different from the treatment effect in the overall population. A Bayesian hierarchical model (BHM) can be used to derive more precise, and less heterogenous estimates of subgroup treatment effects that are closer to the treatment effect in the overall population. BHM assumes exchangeability in treatment effect across subgroups after adjusting for effect modifiers and other relevant covariates. In this article, we will discuss the technical details for applying one-way and multi-way BHM using summary-level statistics, and patient-level data for subgroup analysis. Four case studies based on four new drug applications are used to illustrate the application of these models in subgroup analyses for continuous, dichotomous, time-to-event, and count endpoints.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"1065-1083"},"PeriodicalIF":1.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141620632","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}
引用次数: 0
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