Pharmaceutical Statistics最新文献_第3页

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":"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.","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":"","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":"","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

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

引用次数: 0

Efficient Study Design and Analysis of Longitudinal Dose-Response Data Using Fractional Polynomials. 利用分数多项式进行高效研究设计和纵向剂量反应数据分析

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-11-01 Epub Date: 2024-07-28 DOI: 10.1002/pst.2425

Benjamin F Hartley, Dave Lunn, Adrian P Mander

{"title":"Efficient Study Design and Analysis of Longitudinal Dose-Response Data Using Fractional Polynomials.","authors":"Benjamin F Hartley, Dave Lunn, Adrian P Mander","doi":"10.1002/pst.2425","DOIUrl":"10.1002/pst.2425","url":null,"abstract":"Correctly characterising the dose-response relationship and taking the correct dose forward for further study is a critical part of the drug development process. We use optimal design theory to compare different designs and show that using longitudinal data from all available timepoints in a continuous-time dose-response model can substantially increase the efficiency of estimation of the dose-response compared to a single timepoint model. We give theoretical results to calculate the efficiency gains for a large class of these models. For example, a linearly growing Emax dose-response in a population with a between/within-patient variance ratio ranging from 0.1 to 1 measured at six visits can be estimated with between 1.43 and 2.22 times relative efficiency gain, or equivalently, with 30% to a 55% reduced sample size, compared to a single model of the final timepoint. Fractional polynomials are a flexible way to incorporate data from repeated measurements, increasing precision without imposing strong constraints. Longitudinal dose-response models using two fractional polynomial terms are robust to mis-specification of the true longitudinal process while maintaining, often large, efficiency gains. These models have applications for characterising the dose-response at interim or final analyses.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"1128-1143"},"PeriodicalIF":1.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141788779","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

Exploring Stratification Strategies for Population- Versus Randomization-Based Inference. 探索基于人群与随机推断的分层策略。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-11-01 Epub Date: 2024-07-10 DOI: 10.1002/pst.2419

Marco Novelli, William F Rosenberger

{"title":"Exploring Stratification Strategies for Population- Versus Randomization-Based Inference.","authors":"Marco Novelli, William F Rosenberger","doi":"10.1002/pst.2419","DOIUrl":"10.1002/pst.2419","url":null,"abstract":"Stratification on important variables is a common practice in clinical trials, since ensuring cosmetic balance on known baseline covariates is often deemed to be a crucial requirement for the credibility of the experimental results. However, the actual benefits of stratification are still debated in the literature. Other authors have shown that it does not improve efficiency in large samples and improves it only negligibly in smaller samples. This paper investigates different subgroup analysis strategies, with a particular focus on the potential benefits in terms of inferential precision of prestratification versus both poststratification and post hoc regression adjustment. For each of these approaches, the pros and cons of population-based versus randomization-based inference are discussed. The effects of the presence of a treatment-by-covariate interaction and the variability in the patient responses are also taken into account. Our results show that, in general, prestratifying does not provide substantial benefit. On the contrary, it may be deleterious, in particular for randomization-based procedures in the presence of a chronological bias. Even when there is treatment-by-covariate interaction, prestratification may backfire by considerably reducing the inferential precision.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"1045-1058"},"PeriodicalIF":1.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141580473","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

T3 + 3: 3 + 3 Design With Delayed Outcomes. T3 + 3：延迟结果的 3 + 3 设计。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-11-01 Epub Date: 2024-06-25 DOI: 10.1002/pst.2414

Jiaying Guo, Mengyi Lu, Isabella Wan, Yumin Wang, Leng Han, Yong Zang

{"title":"T3 + 3: 3 + 3 Design With Delayed Outcomes.","authors":"Jiaying Guo, Mengyi Lu, Isabella Wan, Yumin Wang, Leng Han, Yong Zang","doi":"10.1002/pst.2414","DOIUrl":"10.1002/pst.2414","url":null,"abstract":"Delayed outcome is common in phase I oncology clinical trials. It causes logistic difficulty, wastes resources, and prolongs the trial duration. This article investigates this issue and proposes the time-to-event 3 + 3 (T3 + 3) design, which utilizes the actual follow-up time for at-risk patients with pending toxicity outcomes. The T3 + 3 design allows continuous accrual without unnecessary trial suspension and is costless and implementable with pretabulated dose decision rules. Besides, the T3 + 3 design uses the isotonic regression to estimate the toxicity rates across dose levels and therefore can accommodate for any targeted toxicity rate for maximum tolerated dose (MTD). It dramatically facilitates the trial preparation and conduct without intensive computation and statistical consultation. The extension to other algorithm-based phase I dose-finding designs (e.g., i3 + 3 design) is also studied. Comprehensive computer simulation studies are conducted to investigate the performance of the T3 + 3 design under various dose-toxicity scenarios. The results confirm that the T3 + 3 design substantially shortens the trial duration compared with the conventional 3 + 3 design and yields much higher accuracy in MTD identification than the rolling six design. In summary, the T3 + 3 design addresses the delayed outcome issue while keeping the desirable features of the 3 + 3 design, such as simplicity, transparency, and costless implementation. It has great potential to accelerate early-phase drug development.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"959-970"},"PeriodicalIF":1.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141458677","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

To Dilute or Not to Dilute: Nominal Titer Dosing for Genetic Medicines. 稀释还是不稀释：基因药物的名义滴度剂量。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-11-01 Epub Date: 2024-06-25 DOI: 10.1002/pst.2406

Paul Faya, Tianhui Zhang

{"title":"To Dilute or Not to Dilute: Nominal Titer Dosing for Genetic Medicines.","authors":"Paul Faya, Tianhui Zhang","doi":"10.1002/pst.2406","DOIUrl":"10.1002/pst.2406","url":null,"abstract":"Recombinant adeno-associated virus (AAV) has become a popular platform for many gene therapy applications. The strength of AAV-based products is a critical quality attribute that affects the efficacy of the drug and is measured as the concentration of vector genomes, or physical titer. Because the dosing of patients is based on the titer measurement, it is critical for manufacturers to ensure that the measured titer of the drug product is close to the actual concentration of the batch. Historically, dosing calculations have been performed using the measured titer, which is reported on the drug product label. However, due to recent regulatory guidance, sponsors are now expected to label the drug product with nominal or \"target\" titer. This new expectation for gene therapy products can pose a challenge in the presence of process and analytical variability. In particular, the manufacturer must decide if a dilution of the drug substance is warranted at the drug product stage to bring the strength in line with the nominal value. In this paper, we present two straightforward statistical methods to aid the manufacturer in the dilution decision. These approaches use the understanding of process and analytical variability to compute probabilities of achieving the desired drug product titer. We also provide an approach for determining an optimal assay replication strategy for achieving the desired probability of meeting drug product release specifications.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"939-944"},"PeriodicalIF":1.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141458678","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

Applying the Estimand Framework to Non‐Inferiority Trials. 将 Estimand 框架应用于非劣效性试验。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-11-01 Epub Date: 2024-08-08 DOI: 10.1002/pst.2433

Helle Lynggaard, Oliver N Keene, Tobias Mütze, Sunita Rehal

{"title":"Applying the Estimand Framework to Non‐Inferiority Trials.","authors":"Helle Lynggaard, Oliver N Keene, Tobias Mütze, Sunita Rehal","doi":"10.1002/pst.2433","DOIUrl":"10.1002/pst.2433","url":null,"abstract":"Most published applications of the estimand framework have focused on superiority trials. However, non-inferiority trials present specific challenges compared to superiority trials. The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use notes in their addendum on estimands and sensitivity analysis in clinical trials that there may be special considerations to the implementation of estimands in clinical trials with a non-inferiority objective yet provides little guidance. This paper discusses considerations that trial teams should make when defining estimands for a clinical trial with a non-inferiority objective. We discuss how the pre-addendum way of establishing non-inferiority can be embraced by the estimand framework including a discussion of the role of the Per Protocol analysis set. We examine what clinical questions of interest can be formulated in the context of non-inferiority trials and outline why we do not think it is sensible to describe an estimand as 'conservative'. The impact of the estimand framework on key considerations in non-inferiority trials such as whether trials should have more than one primary estimand, the choice of non-inferiority margin, assay sensitivity, switching from non-inferiority to superiority and estimation are discussed. We conclude by providing a list of recommendations, and important considerations for defining estimands for trials with a non-inferiority objective.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"1156-1165"},"PeriodicalIF":1.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141902593","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 Personalized Dose-Finding Algorithm Based on Adaptive Gaussian Process Regression. 基于自适应高斯过程回归的个性化剂量确定算法

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-11-01 Epub Date: 2024-08-09 DOI: 10.1002/pst.2417

Yeonhee Park, Won Chang

{"title":"A Personalized Dose-Finding Algorithm Based on Adaptive Gaussian Process Regression.","authors":"Yeonhee Park, Won Chang","doi":"10.1002/pst.2417","DOIUrl":"10.1002/pst.2417","url":null,"abstract":"Dose-finding studies play a crucial role in drug development by identifying the optimal dose(s) for later studies while considering tolerability. This not only saves time and effort in proceeding with Phase III trials but also improves efficacy. In an era of precision medicine, it is not ideal to assume patient homogeneity in dose-finding studies as patients may respond differently to the drug. To address this, we propose a personalized dose-finding algorithm that assigns patients to individualized optimal biological doses. Our design follows a two-stage approach. Initially, patients are enrolled under broad eligibility criteria. Based on the Stage 1 data, we fit a regression model of toxicity and efficacy outcomes on dose and biomarkers to characterize treatment-sensitive patients. In the second stage, we restrict the trial population to sensitive patients, apply a personalized dose allocation algorithm, and choose the recommended dose at the end of the trial. Simulation study shows that the proposed design reliably enriches the trial population, minimizes the number of failures, and yields superior operating characteristics compared to several existing dose-finding designs in terms of both the percentage of correct selection and the number of patients treated at target dose(s).","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"1181-1205"},"PeriodicalIF":1.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141907379","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

Optimizing Sample Size Determinations for Phase 3 Clinical Trials in Type 2 Diabetes. 优化 2 型糖尿病 3 期临床试验的样本量确定。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-10-30 DOI: 10.1002/pst.2446

Alexander C Cambon, James Travis, Liping Sun, Jada Idokogi, Anna Kettermann

{"title":"Optimizing Sample Size Determinations for Phase 3 Clinical Trials in Type 2 Diabetes.","authors":"Alexander C Cambon, James Travis, Liping Sun, Jada Idokogi, Anna Kettermann","doi":"10.1002/pst.2446","DOIUrl":"https://doi.org/10.1002/pst.2446","url":null,"abstract":"An informed estimate of subject-level variance is a key determinate for accurate estimation of the required sample size for clinical trials. Evaluating completed adult Type 2 diabetes studies submitted to the FDA for accuracy of the variance estimate at the planning stage provides insights to inform the sample size requirements for future studies. From the U.S. Food and Drug Administration (FDA) database of new drug applications containing 14,106 subjects from 26 phase 3 randomized studies submitted to the FDA in support of drug approvals in adult type 2 diabetes studies reviewed between 2013 and 2017, we obtained estimates of subject-level variance for the primary endpoint-change in glycated hemoglobin (HbA1c) from baseline to 6 months. In addition, we used nine additional studies to examine the impact of clinically meaningful covariates on residual standard deviation and sample size re-estimation. Our analyses show that reduced sample sizes can be used without interfering with the validity of efficacy results for adult type 2 diabetes drug trials. This finding has implications for future research involving the adult type 2 diabetes population, including the potential to reduce recruitment period length and improve the timeliness of results. Furthermore, our findings could be utilized in the design of future endocrinology clinical trials.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142546679","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