Pharmaceutical Statistics最新文献

Robustness of the Pairwise-Fitting Approach Under Missing at Random Dropout: A Case and Simulation Study. 随机缺失情况下成对拟合方法的鲁棒性：一个案例与仿真研究。

IF 1.5 4区医学

Pharmaceutical Statistics Pub Date : 2026-09-01 DOI: 10.1002/pst.70115

Dries De Witte, Geert Verbeke, Thomas Neyens, Ariel Alonso Abad, Geert Molenberghs

{"title":"Robustness of the Pairwise-Fitting Approach Under Missing at Random Dropout: A Case and Simulation Study.","authors":"Dries De Witte, Geert Verbeke, Thomas Neyens, Ariel Alonso Abad, Geert Molenberghs","doi":"10.1002/pst.70115","DOIUrl":"10.1002/pst.70115","url":null,"abstract":"In many studies, multiple longitudinal outcomes are collected, and interest lies in studying the association between these outcomes. Joint modeling is then required, but full likelihood estimation becomes infeasible as the number of outcomes increases. To address this, the pairwise-fitting approach was developed. However, the robustness of this pseudo-likelihood-based approach under missing at random (MAR) remains unclear. We investigate the impact of MAR dropout on the pairwise-fitting approach through a case and simulation study and compare the results to full likelihood estimation. In the simulation study, we simulate three continuous longitudinal outcomes so that full likelihood estimation remains computationally feasible, allowing a comparison with the pairwise fitting approach. Various settings are examined, including random intercept and random intercept-and-slope models, in which we vary the standard deviation of the error terms and the degree of correlation between random effects. Our results show that bias remains limited in random intercept models and in most random intercept-and-slope models. However, when the standard deviation of the error terms becomes large compared to that of the random effects, some bias appears in the covariances between the random effects of the outcomes not driving dropout. This bias is mitigated using multiple imputation. As a case study, we analyzed data from a schizophrenia study using both full likelihood and pseudo-likelihood approaches and compared the results.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 5","pages":"e70115"},"PeriodicalIF":1.5,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13524844/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148851109","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

Accelerated Stability Studies: A Frequentist-Bayesian Hybrid Simulation Approach Implemented in the AccelStab R Package. 加速稳定性研究：在AccelStab R包中实现的频率-贝叶斯混合仿真方法。

IF 1.5 4区医学

Pharmaceutical Statistics Pub Date : 2026-09-01 DOI: 10.1002/pst.70116

Bernard G Francq, Nikolaos Giannelos, Raymundo Sanchez, Marilena Paludi

{"title":"Accelerated Stability Studies: A Frequentist-Bayesian Hybrid Simulation Approach Implemented in the AccelStab R Package.","authors":"Bernard G Francq, Nikolaos Giannelos, Raymundo Sanchez, Marilena Paludi","doi":"10.1002/pst.70116","DOIUrl":"10.1002/pst.70116","url":null,"abstract":"Stability assessment of vaccine drug products typically requires years of data collection. Accelerated stability studies offer a practical alternative by leveraging high-temperature data collected over shorter time periods. We model degradation using arbitrary nth order kinetics and derive a closed-form solution that enables extrapolation across time and temperature. We compare interval estimation methods including the delta method, bootstrap resampling, and Bayesian inference and propose a frequentist-Bayesian hybrid (FBH) posterior approximation framework based on a multivariate Student's t-distribution. FBH is closely related to objective Bayesian inference under noninformative priors in simple Gaussian settings and fully propagates uncertainty in both model parameters and residual variance. Simulation results show that FBH outperforms standard approaches by consistently achieving nominal coverage, particularly in small-sample settings where competing methods exhibit undercoverage. The approach provides well-calibrated confidence and prediction intervals while maintaining computational efficiency comparable to analytical methods and avoiding the cost of full posterior sampling. The methodology is illustrated using two vaccine development case studies and implemented in the AccelStab R package.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 5","pages":"e70116"},"PeriodicalIF":1.5,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13545001/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148892262","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

A Bayesian Optimal Interval Design Considering Efficacy and Toxicity in Early Phase Basket Trials. 早期篮子试验中考虑疗效和毒性的贝叶斯最优区间设计。

IF 1.5 4区医学

Pharmaceutical Statistics Pub Date : 2026-07-01 DOI: 10.1002/pst.70108

Tomoyuki Kakizume, Kentaro Takeda, Masataka Taguri, Satoshi Morita

{"title":"A Bayesian Optimal Interval Design Considering Efficacy and Toxicity in Early Phase Basket Trials.","authors":"Tomoyuki Kakizume, Kentaro Takeda, Masataka Taguri, Satoshi Morita","doi":"10.1002/pst.70108","DOIUrl":"10.1002/pst.70108","url":null,"abstract":"Oncology drug development has increasingly shifted toward determining optimal biological doses rather than maximum tolerated doses (MTDs), particularly for targeted therapies and immunotherapies that exhibit complex dose-efficacy relationships. Concurrently, basket trials have emerged as an efficient approach for evaluating investigational treatments across multiple cancer types sharing common molecular targets. We propose the BOIN-ETB design, a model-assisted dose-finding design that addresses optimal dose (OD) identification in phase I/II basket trials by incorporating both toxicity and efficacy endpoints. The proposed approach employs common toxicity boundaries across cancer types while implementing cancer-specific efficacy boundaries to account for differential efficacy responses between baskets. OD selection utilizes utility functions that quantify efficacy-toxicity trade-offs. Through comprehensive simulation studies across Fourteen realistic scenarios, the BOIN-ETB design demonstrates robust performance in identifying true ODs while maintaining acceptable safety profiles across diverse cancer populations. The design provides superior consistency compared to alternative approaches, particularly in scenarios with heterogeneous dose-efficacy relationships between cancer types, making it well-suited for contemporary oncology dose-finding basket trials.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 4","pages":"e70108"},"PeriodicalIF":1.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148397446","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 Knowledge Base of Designs and Statistical Methods for Adaptive Clinical Dose-Finding Trials. 适应性临床剂量发现试验的设计和统计方法知识库。

IF 1.5 4区医学

Pharmaceutical Statistics Pub Date : 2026-07-01 DOI: 10.1002/pst.70074

Rob Kessels, Yongxi Long, Rene Spijker, Ewoud Schuit, Marieke Hollestelle, Chris van Lieshout, Neeltje Steeghs, Sjoerd G Elias, Peter M van de Ven

{"title":"A Knowledge Base of Designs and Statistical Methods for Adaptive Clinical Dose-Finding Trials.","authors":"Rob Kessels, Yongxi Long, Rene Spijker, Ewoud Schuit, Marieke Hollestelle, Chris van Lieshout, Neeltje Steeghs, Sjoerd G Elias, Peter M van de Ven","doi":"10.1002/pst.70074","DOIUrl":"10.1002/pst.70074","url":null,"abstract":"Adaptive clinical dose-finding trials aim to identify an optimal drug dose for use in subsequent phase II and III trials. In adaptive dose-finding trials, dose levels for newly included patients are informed by outcomes of patients that received the drug earlier in the trial. The body of methodological research on adaptive dose-finding trials is extensive, but a clear overview is lacking. The goal of this paper is to provide a knowledge base of designs and statistical methods for adaptive clinical dose-finding trials by means of a literature review. We identified 315 adaptive dose-finding trial methodology articles of which the majority was inspired by oncology. Recent methods focused on identification of an optimal dose considering both toxicity and efficacy endpoints, and addressing challenges related to subgroup-specific dose-finding, dose-finding for combination therapies, and incorporation of delayed outcomes in dose-finding. These developments are driven by the emergence of newer classes of cancer drugs, such as targeted therapies and immunotherapies, and by initiatives like Project Optimus. Most articles focused on model-based designs, like the Continual Reassessment Method (CRM), but recent years have seen a strong increase in model-assisted or interval-based designs, including Toxicity Probability Interval (TPI) and Bayesian Optimal Interval (BOIN) designs and expansions thereof. Considering the increasing availability and large variety of adaptive dose-finding trial designs, it is challenging for researchers to find relevant designs tailored to their needs. Therefore, we provide an interactive map summarizing the classification of our results to facilitate the identification of relevant designs/methods, which we will update regularly.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 4","pages":"e70074"},"PeriodicalIF":1.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147974280","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

Harmonic Fowlkes-Mallows Index for Medical Diagnostics Tests and Optimal Cut-Off Point Selection of Binary Diseases. 医学诊断试验的谐波fowkes - mallows指数及二元疾病的最优分界点选择。

IF 1.5 4区医学

Pharmaceutical Statistics Pub Date : 2026-07-01 DOI: 10.1002/pst.70104

Parthkumar Rabari, Purbasha Biswas, Jing X Kersey, Hani Samawi

{"title":"Harmonic Fowlkes-Mallows Index for Medical Diagnostics Tests and Optimal Cut-Off Point Selection of Binary Diseases.","authors":"Parthkumar Rabari, Purbasha Biswas, Jing X Kersey, Hani Samawi","doi":"10.1002/pst.70104","DOIUrl":"10.1002/pst.70104","url":null,"abstract":"Accurately distinguishing between healthy and diseased states is fundamental to clinical diagnostics. This paper introduces the Harmonic Fowlkes-Mallows (HFM) index, a novel and robust metric for assessing diagnostic accuracy and identifying optimal cut-off points. The proposed HFM index integrates performance across both positive and negative classes by combining the traditional Fowlkes-Mallows Index (FM) with the proposed Negative Fowlkes-Mallows Index (NFM), using a weighted harmonic mean. Unlike conventional measures such as the F1-score or Youden Index, HFM provides a more comprehensive evaluation of classification performance by simultaneously addressing sensitivity and specificity. Additionally, it incorporates a tunable β parameter to adjust for asymmetries in class importance. Through simulation studies, the HFM index demonstrates strong performance in binary classification tasks and proves effective in selecting optimal decision thresholds. To further demonstrate its practical utility, we apply the HFM index to real-world breast cancer data and compare its performance with other diagnostic accuracy measures.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 4","pages":"e70104"},"PeriodicalIF":1.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148284197","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

The Impact of Model Misspecification on the Individual Causal Association in Surrogate Endpoint Evaluation. 替代终点评估中模型错配对个体因果关联的影响。

IF 1.5 4区医学

Pharmaceutical Statistics Pub Date : 2026-07-01 DOI: 10.1002/pst.70110

Gokce Deliorman, Florian Stijven, Wim Van der Elst, Maria Del Carmen Pardo, Ariel Alonso

{"title":"The Impact of Model Misspecification on the Individual Causal Association in Surrogate Endpoint Evaluation.","authors":"Gokce Deliorman, Florian Stijven, Wim Van der Elst, Maria Del Carmen Pardo, Ariel Alonso","doi":"10.1002/pst.70110","DOIUrl":"10.1002/pst.70110","url":null,"abstract":"Surrogate endpoints are often used in place of expensive, delayed, or rare clinical endpoints in clinical trials. However, regulatory authorities require thorough evaluation to accept these surrogate endpoints as reliable substitutes. One evaluation approach is the information-theoretic causal inference framework, which quantifies surrogacy using the individual causal association (ICA). Like most causal inference methods, this approach relies on models that are only partially identifiable. For continuous outcomes, a normal model is often used. In this study, we explored the effects of model misspecification across various scenarios. We first considered true data-generating mechanisms based on multivariate <math> <semantics><mrow><mi>t</mi></mrow> <annotation>$$ t $$</annotation></semantics> </math> and log-normal distributions. We then used D-vine copulas with Gaussian, Clayton, Gumbel, and Frank families to vary the unidentifiable copulas involving counterfactual pairs while preserving the observable bivariate margins, and considered intuitive restrictions on nonidentified correlations, including positivity and conditional independence. In all settings, the identifiability issue was addressed through sensitivity analysis. Finally, we illustrate the proposed sensitivity analyses using clinical-trial data from schizophrenia studies, evaluating the ICA under several modeling assumptions. The results show that, in most scenarios considered, the impact of model misspecification is small; however, certain departures from the assumed model can materially affect the surrogacy assessment.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 4","pages":"e70110"},"PeriodicalIF":1.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13356973/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148430821","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

Optimizing Randomization Ratios in Clinical Trials With Survival Endpoints. 在有生存终点的临床试验中优化随机化比例。

IF 1.5 4区医学

Pharmaceutical Statistics Pub Date : 2026-07-01 DOI: 10.1002/pst.70103

Luoying Yang, Weijia Mai, Yuanyuan Han

{"title":"Optimizing Randomization Ratios in Clinical Trials With Survival Endpoints.","authors":"Luoying Yang, Weijia Mai, Yuanyuan Han","doi":"10.1002/pst.70103","DOIUrl":"10.1002/pst.70103","url":null,"abstract":"An equal randomization ratio (1:1) is the most commonly used allocation strategy in confirmatory clinical trials. Recently, there has been growing discussion around the use of unequal randomization, which may be favored for several reasons, including encouraging trial recruitment, reducing costs, and improving the robustness of estimates in the treatment arm. However, despite these potential benefits, unequal randomization is still rarely applied in trial design. A key barrier is the lack of a method to determine the optimal randomization ratio that balances the various considerations of a trial. To address this challenge, we developed an optimization framework that identifies the optimal randomization ratio to maximize a trial's probability of success and profit-two major considerations in trial design. The framework incorporates trial parameters such as prior knowledge of treatment efficacy, sample size, budget, and per-subject cost. We evaluate the proposed method through simulations and a hypothetical trial. The results show that the optimal randomization ratio is highly influenced by sample size and cost differences between treatment arms. Our framework demonstrates the ability to reduce costs while maintaining a high probability of success.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 4","pages":"e70103"},"PeriodicalIF":1.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148272399","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

Early Phase Dose-Finding Designs for CAR-T Cell Therapies. CAR-T细胞疗法的早期剂量发现设计。

IF 1.5 4区医学

Pharmaceutical Statistics Pub Date : 2026-07-01 DOI: 10.1002/pst.70102

Weishi Chen, Pavel Mozgunov, Jimmy Mullaert, Xavier Paoletti

{"title":"Early Phase Dose-Finding Designs for CAR-T Cell Therapies.","authors":"Weishi Chen, Pavel Mozgunov, Jimmy Mullaert, Xavier Paoletti","doi":"10.1002/pst.70102","DOIUrl":"10.1002/pst.70102","url":null,"abstract":"Chimeric Antigen Receptor (CAR)-T cell is an immunotherapy which revolutionised the treatment of relapsed/refractory lymphoma and leukaemia. It is shown to have a higher response rate, higher mid-to-long term overall survival, and lower toxicity than standard treatments. However, due to a lack of dose-limiting toxicity (DLT) and unclear dose-effect relationship, traditional phase I designs of clinical trials cannot lead to accurate selections of the optimal dose (OD). Beside clinical outcomes, the CAR-T cell expansion from serial blood samples is measured at various time points. We propose a novel early phase dose-finding design for CAR-T cells, using both toxicity and activity endpoints to locate the OD. The number of CAR-T cells measured in the peripheral blood is used to indicate activity, which is more sensitive than the short-term clinical responses traditionally used. A Bi-Exponential model is used for the repeated measures of the number of cells for each patient, and is estimated under a Bayesian framework. The model is motivated by biological concerns and is flexible enough to accommodate different shapes of the cell-expansion curve. Three criteria for activity are considered: (1) the number of cells at specific time points, (2) the duration before all cells are eliminated, (3) the area under the cell-expansion curve. Simulation studies show that the OD can be selected with high accuracy even under small sample sizes.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 4","pages":"e70102"},"PeriodicalIF":1.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13277398/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148272436","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

Impact of Information Leakage in Platform Trials With Survival Endpoints on Type I Error Control. 有生存终点的平台试验中信息泄漏对I型错误控制的影响

IF 1.5 4区医学

Pharmaceutical Statistics Pub Date : 2026-07-01 DOI: 10.1002/pst.70106

Quynh Nguyen, Martin Posch, Benjamin Hofner, Franz König

{"title":"Impact of Information Leakage in Platform Trials With Survival Endpoints on Type I Error Control.","authors":"Quynh Nguyen, Martin Posch, Benjamin Hofner, Franz König","doi":"10.1002/pst.70106","DOIUrl":"10.1002/pst.70106","url":null,"abstract":"Platform trials evaluate multiple treatments within a single trial infrastructure. Such designs have gained a lot of attraction in clinical research. If information gained from platform trials should provide confirmatory evidence for regulatory decisions, control of the Type I error rate is key. One critical issue is information leakage, for example, if any information of the ongoing trial is available, especially if it may impact the further conduct of treatments still in the platform trial and bias their results. This paper evaluates the potential impact of information leakage on the control of the Type I error rate in platform trials with time-to-event endpoints such as overall survival. We explore different strategies how information on the treatment effect of an still ongoing treatment could be obtained if a pre-planned analysis for another arm is conducted. This (leaked) information might be used to decide whether to continue the other arm as planned or conduct its final analysis immediately. By means of clinical trial simulations we evaluate the impact of different levels of information leakage on the Type I error rate. We show how the conditional error principle can be applied to estimate worst case Type I error rate inflation for the different forms of information leakage. We do not aim to quantify the exact maximum Type I error rate inflation but rather to raise awareness of the potential risk for estimation of comparative results. Finally, we discuss the regulatory implications of information leakage and propose strategies to mitigate these risks.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 4","pages":"e70106"},"PeriodicalIF":1.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13305155/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148331771","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

Will the Pharmaceutical Industry Need Statisticians in an AI World? 制药行业在人工智能世界中需要统计学家吗？

IF 1.5 4区医学

Pharmaceutical Statistics Pub Date : 2026-07-01 DOI: 10.1002/pst.70100

Chris Harbron

引用次数: 0