Pharmaceutical Statistics最新文献

{"title":"Designing and Evaluating Bayesian Advanced Adaptive Randomised Clinical Trials: A Practical Guide.","authors":"Anders Granholm, Aksel Karl Georg Jensen, Theis Lange, Anders Perner, Morten Hylander Møller, Benjamin Skov Kaas-Hansen","doi":"10.1002/pst.70042","DOIUrl":"10.1002/pst.70042","url":null,"abstract":"<p><p>Advanced adaptive randomised clinical trials are increasingly used. Compared to their conventional counterparts, their flexibility may make them more efficient, increase the probability of obtaining conclusive results without larger samples than necessary, and increase the probability that individual participants are allocated to more promising interventions. However, limited guidance is available on designing and evaluating the performance of advanced adaptive trials. Here, we summarise the methodological considerations and provide practical guidance on the entire workflow of planning and evaluating advanced adaptive trials using adaptive stopping, adaptive arm dropping, and response-adaptive randomisation within a Bayesian statistical framework. This comprehensive practical guide covers the key methodological decisions for Bayesian advanced adaptive trials and their specification and evaluation using statistical simulation. These considerations include interventions and common control use; outcome type and generation; analysis timing and outcome-data lag; allocation rules; analysis model; adaptation rules for stopping and arm dropping; clinical scenarios assessed; performance metrics; calibration; sensitivity analyses; and reporting. The considerations are covered in the context of realistic examples, along with simulation code using the adaptr R package. In conclusion, this practical guide will help clinical trialists, methodologists, and biostatisticians design and evaluate Bayesian advanced adaptive trials.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 6","pages":"e70042"},"PeriodicalIF":1.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12509790/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145252272","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}

{"title":"Great Wall: A Generalized Dose Optimization Design for Drug Combination Trials Maximizing Survival Benefit.","authors":"Yan Han, Yingjie Qiu, Yi Zhao, Isabella Wan, Lang Li, Suyu Liu, Yong Zang","doi":"10.1002/pst.70049","DOIUrl":"10.1002/pst.70049","url":null,"abstract":"<p><p>Most phase I-II drug-combination trial designs assume that selecting the optimal dose combination based on early outcomes will also lead to maximum long-term survival benefits. However, this assumption is often violated in many clinical studies, generally due to high rates of relapse following the initial response. To address this problem, we propose the Great Wall design, a general dose optimization design for drug-combination trials. The Great Wall design employs a \"divide-and-conquer\" algorithm to address the issue of partial order of toxicity and uses early outcomes to eliminate dose combinations that are excessively toxic or less efficacious. It utilizes a dose randomization approach to construct a candidate set of the promising dose combinations balancing the toxicity and early efficacy outcomes. The patients assigned to the candidate set are followed to collect the survival outcomes and the final optimal dose combination is then selected to maximize the survival benefit. The simulation studies confirm the desirable operating characteristics of the Great Wall design under various clinical settings. R codes are also provided to facilitate the application. The Great Wall design is modular and practically useful in settings where investigators plan to follow patients long enough to assess survival outcomes.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 6","pages":"e70049"},"PeriodicalIF":1.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12606548/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145496378","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}

{"title":"Long-Term Safety Evaluations in the Presence of Switching: Evaluation of Two Approaches.","authors":"Sandra Schmeller, Rima Izem, Pedro Lopez Romero, Valentine Jehl","doi":"10.1002/pst.70039","DOIUrl":"10.1002/pst.70039","url":null,"abstract":"<p><p>Evaluating the long-term safety of approved drugs for chronic indications is essential. This assessment ensures that the benefits continue to outweigh the risks beyond the follow-up observed in the clinical trials supporting market authorization. Consequently, these evaluations are mandated or recommended by multiple regulatory agencies and necessitate collecting and analyzing data from longitudinal cohorts in real-world settings post-market authorization. One challenge in analyzing and interpreting results using these sources is the complexity of long-term real-world drug utilization patterns in a competitive landscape, including switching between multiple drugs. Several methods have been developed to evaluate comparative long-term safety under real-world conditions. These methods include the experimental hierarchical approach, which extends the analytical follow-up period for the test drug to include the time after switching away, and the overlapping approach, which extends the follow-up period for both the test and comparator drugs to include the time after switching away. This paper uses multistate model methodology to consider initial and subsequent exposures in evaluating the estimators in these two methods. Our mathematical evaluations and simulations demonstrate that the estimators inflate the type-1-error across different switching and outcome incidence rate scenarios. Therefore, we propose a minor modification of the estimators to preserve the type-1-error. Currently used methods are simple but biased and lack clearly defined estimands. Methods based on multistate models may help identify and refine new estimands for evaluating long-term safety.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 6","pages":"e70039"},"PeriodicalIF":1.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12475889/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145177386","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}

{"title":"Extending Multiple Testing With Unknown Test Dependency via the CoCo Test: With Applications to Cancer Studies.","authors":"Jiangtao Gou, Kai Wu, Oliver Y Chén","doi":"10.1002/pst.70040","DOIUrl":"10.1002/pst.70040","url":null,"abstract":"<p><p>Multiple testing issues are common in clinical and scientific research, particularly in clinical trials involving multple endpoints. The central challenge lies in controlling the type I error rate ( <math> <semantics><mrow><mi>α</mi></mrow> <annotation>$$ alpha $$</annotation></semantics> </math> -control). The behavior of multiple testing procedures for <math> <semantics><mrow><mi>α</mi></mrow> <annotation>$$ alpha $$</annotation></semantics> </math> -control when the tests are independent or dependent but with a known joint distribution is relatively well known. When the joint distribution of test statistics is unknown, one can still guarantee the <math> <semantics><mrow><mi>α</mi></mrow> <annotation>$$ alpha $$</annotation></semantics> </math> -control, if the positive dependency through stochastic ordering (PDS) condition is satisfied. Despite the frequent occurrence of unknown test dependency in multiple testing and the importance of the PDS condition in endorsing its validity, little do we know about how to verify this condition. Here, we develop a new nonparametric statistical test, called the CoCo test, based on ranked correlation coefficients and a simple, yet effective, algebraic arrangement of the Spearman's <math> <semantics><mrow><mi>ρ</mi></mrow> <annotation>$$ rho $$</annotation></semantics> </math> and Kendall's <math> <semantics><mrow><mi>τ</mi></mrow> <annotation>$$ tau $$</annotation></semantics> </math> , that can validate the PDS condition, through which one can control for <math> <semantics><mrow><mi>α</mi></mrow> <annotation>$$ alpha $$</annotation></semantics> </math> regardless of the prior knowledge of the dependency between test statistics. Simulation studies show that the CoCo test can faithfully detect the violation of the PDS condition or lack thereof. To further evaluate the efficacy of the CoCo test, we apply it to investigate three meta-analyses. Our simulation studies and data analyses encourage one to evaluate the PDS condition during multiple testing, especially when one is uncertain about the relationship between tests, and the proposed CoCo test provides both methodological insights into and a technical device for doing so. An R package cocotest to implement the proposed methodology is available at CRAN.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 6","pages":"e70040"},"PeriodicalIF":1.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145200723","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 Generalized Bayesian Hierarchical Model in Basket Trials.","authors":"Ruoxuan Xiang, John Scott","doi":"10.1002/pst.70041","DOIUrl":"10.1002/pst.70041","url":null,"abstract":"<p><p>Basket trials in oncology assess one treatment simultaneously on multiple cancer histologies that share a common genomic aberration. A Bayesian hierarchical model (BHM) first proposed by Thall et al. is widely used to borrow information across multiple cancer types in a basket trial. However, this BHM fails to adaptively determine the strength of borrowing and can lead to substantial inflation of Type I error when treatment effects vary by subgroups. Variations of the BHM accounting for heterogeneity have been proposed, some of which are through modifications of the exchangeability assumption of BHM or calibrations of the variance parameter in the BHM. We propose a generalized Bayesian hierarchical model (GBHM), which relaxes the assumption for the variance parameter in the BHM. We perform extensive comparisons to existing BHM variants under four simulation studies, which are based on the same setup as in the respective articles where the BHM variants were proposed. We investigate GBHM employing Inverse-Gamma (IG) and Cauchy priors with various hyperparameters. Simulation studies show that GBHM with certain choices of priors is robust to treatment effect heterogeneity. We recommend GBHM with IG(0.01,0.01) prior for slightly liberal information borrowing if Type I error control is not the main concern and GBHM with Cauchy(25) prior for conservative borrowing otherwise. A tremendous advantage of the GBHM is that it is very simple to implement and complicated prior specification is not needed, whereas current existing approaches may require careful tuning of the prior hyperparameters.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 6","pages":"e70041"},"PeriodicalIF":1.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145372960","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 Unified Approach to Covariate Adjustment for Survival Endpoints in Randomized Clinical Trials.","authors":"Zhiwei Zhang, Ya Wang, Dong Xi","doi":"10.1002/pst.70054","DOIUrl":"10.1002/pst.70054","url":null,"abstract":"<p><p>Covariate adjustment aims to improve the statistical efficiency of randomized trials by incorporating information from baseline covariates. Popular methods for covariate adjustment include analysis of covariance for continuous endpoints and standardized logistic regression for binary endpoints. For survival endpoints, while some covariate adjustment methods have been developed for specific effect measures, they are not commonly used in practice for various reasons, including high demands for theoretical and methodological sophistication as well as computational skills. This article describes an augmentation approach to covariate adjustment for survival endpoints that is relatively easy to understand and widely applicable to different effect measures. This approach involves augmenting a given treatment effect estimator in a way that preserves consistency and asymptotic normality under minimal assumptions (i.e., randomization). It does not attempt to exploit other possible constraints (e.g., independent censoring, proportional hazards) on the observed data distribution. The optimal augmentation term, which minimizes the asymptotic variance of an augmented estimator, can be estimated using various statistical and machine learning methods. Simulation results demonstrate that the augmentation approach can bring substantial gains in statistical efficiency. This approach has been implemented in an R package named sleete, which is described in detail and illustrated with real data.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 6","pages":"e70054"},"PeriodicalIF":1.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145534122","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":"Quantile Effect on Duration of Response: A Zero-Inflated Censored Regression Approach.","authors":"Nan Sun, Jixian Wang, Ram Tiwari","doi":"10.1002/pst.70053","DOIUrl":"10.1002/pst.70053","url":null,"abstract":"<p><p>Duration of response (DOR) has been increasingly used as a useful measure of response to treatments in randomized clinical trials (RCT). Some estimands for DOR, such as the restricted mean DOR, although simple to use, may be sensitive to outliers and may not correctly measure treatment effects on the quantiles of DOR, such as the proportion of patients with DOR of at least 3 months. Quantile regression for survival data has been well developed. However, it is not directly applicable to DOR data in RCTs, due to the presence of non-responders for whom DOR is not defined. Although they can be treated as having zero DOR in a standard quantile regression, such an approach may not be flexible to model these subset of patients. To mitigate this issue, we propose an approach similar to the two-parts zero-inflated models, for example, for count data, so that the nonresponders are modeled as a part of the model, while DOR is modeled using quantile regression. A simulation study is conducted to examine the performance of the proposed approach. For illustration, we apply our approach to a simulated dataset of an acute myeloid leukemia trial, since the true dataset cannot be used due to confidentiality. The asymptotic properties of the proposed approach are also derived.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 6","pages":"e70053"},"PeriodicalIF":1.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145541755","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":"Utility-Based Dose Optimization Approaches for Multiple-Dose Randomized Trial Designs Accounting for Multiple Endpoints.","authors":"Gina D'Angelo, Guannan Chen, Di Ran","doi":"10.1002/pst.70044","DOIUrl":"10.1002/pst.70044","url":null,"abstract":"<p><p>The initiation of dose optimization has driven a paradigm shift in oncology clinical trials to determine the optimal biological dose (OBD). Early-phase trials with randomized doses can facilitate additional investigation of the identified OBD in targeted populations by incorporating safety, efficacy, and biomarker data. To support dose comparison in such settings, we propose to extend the utility score-based approach (U-MET) to account for multiple endpoints and doses. The utility-based dose optimization approach for multiple-dose randomized trial designs accounting for multiple (≤ 3) endpoints and doses (U-MET-m) extends the U-MET, using a utility score to account for multiple endpoints jointly (e.g., toxicity-efficacy trade-off). When there are > 3 endpoints, assigning weights jointly is quite complicated; therefore, we suggest an alternative approach with CUI-MET (clinical utility index dose optimization approach for multiple-dose randomized trial designs), which uses a utility index to account for multiple endpoints marginally. We demonstrate the relationship between U-MET-m and CUI-MET to offer a guide in weight selection for U-MET-m when there are up to three endpoints. U-MET-m and (extended) CUI-MET use Bayesian inference within a hypothesis framework to compare utility metrics across doses to identify the OBD. Here we describe simulation studies and present examples to compare both the U-MET-m and CUI-MET designs with the empirical design. The U-MET-m design and CUI-MET were shown to have satisfactory operating characteristics for selecting the OBD. Based on these findings, we recommend the U-MET-m with ≤ 3 endpoints and CUI-MET with > 3 endpoints as the primary dose comparison approach to select the OBD.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 6","pages":"e70044"},"PeriodicalIF":1.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145329777","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":"Statistical Consideration for Event-Free Survival With Cure Rate in Acute Myeloid Leukemia Studies.","authors":"Yuichiro Kaneko, Kentaro Takeda, Shufang Liu, Lu Tian","doi":"10.1002/pst.70052","DOIUrl":"10.1002/pst.70052","url":null,"abstract":"<p><p>In many acute myeloid leukemia (AML) studies, event-free survival (EFS) has been accepted as a primary efficacy endpoint. In those studies, the patients who do not achieve complete remission (CR) in the induction period are regarded as induction treatment failure (ITF). The recent FDA guidance on AML (2022) has clearly specified ITF as the event at Day 1 of randomization, considering the variability of length of individual induction treatment periods among studies. Xu et al. (2021) suggested decomposing the log-rank test statistic into the ITF portion, and the Non-ITF portion that is defined as the patients who achieved CR, and assumed proportional hazards for the Non-ITF portion. However, especially in the newly diagnosed AML study, there is some indication of the cured patients who achieve CR during the induction period. As a result, Non-zero ITF rates and cured patients invalidate the proportional hazards assumption and therefore, the conventional power calculation based on the number of events may be problematic in this setting. Our research follows the same decomposition of the log-rank test statistic as Xu et al. (2021) and suggests a new sample size calculation method accounting for the presence of both ITF and cured patients. The result shows that the analytically calculated power of log-rank test based on our proposal was very similar to the empirical power based on simulations in various finite sample settings and was also useful to protect from overestimation and underestimation of the required sample size in the presence of cure fraction.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 6","pages":"e70052"},"PeriodicalIF":1.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145534178","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":"The Wilcoxon-Mann-Whitney Estimand Versus Differences in Medians or Means.","authors":"Linda J Harrison, Ronald J Bosch","doi":"10.1002/pst.70036","DOIUrl":"10.1002/pst.70036","url":null,"abstract":"<p><p>There is a renewed interest in defining the target of estimation when designing randomized trials. Motivated by design work in trials of HIV-1 curative interventions, we compare the Wilcoxon-Mann-Whitney (WMW) estimand to a difference in medians or means in a two-arm study. First, we define each estimand along with an appropriate estimator. Then, we highlight relevant asymptotic relative efficiency (ARE) results for the estimators under normal distributions (ARE: WMW/mean = <math> <semantics><mrow><mn>3</mn> <mo>/</mo> <mi>π</mi></mrow> <annotation>$$ 3/pi $$</annotation></semantics> </math> , median/mean = <math> <semantics><mrow><mn>2</mn> <mo>/</mo> <mi>π</mi></mrow> <annotation>$$ 2/pi $$</annotation></semantics> </math> , median/WMW = <math> <semantics><mrow><mn>2</mn> <mo>/</mo> <mn>3</mn></mrow> <annotation>$$ 2/3 $$</annotation></semantics> </math> ), as well as normal mixtures. Measurement of outcomes related to HIV-1 cure involve laboratory assays with lower limits of quantification giving rise to left-censored data. In our simulation study, we compare the estimators in the presence of left-censored observations and at small sample sizes, illustrating that under a censored normal mixture distribution the WMW approach is unbiased, powerful, and has confidence intervals with nominal coverage. We apply our findings to a randomized trial designed to reduce HIV-1 reservoirs. We further expose several extensions of the WMW approach that allows for assessment of interactions between subgroups in a trial, adjustment for covariates, and general ranking methods for clinical outcomes in other disease areas. We end with a discussion summarizing the merits of a WMW based intervention effect estimate versus an estimate summarized on the scale the intervention was originally measured such as the difference in medians or means.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 5","pages":"e70036"},"PeriodicalIF":1.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12379204/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144822258","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}