Pharmaceutical Statistics最新文献

{"title":"Drift Parameter Based Sample Size Determination in Multi-Stage Bayesian Randomized Clinical Trials.","authors":"Yueyang Han, Haolun Shi, Jiguo Cao, Ruitao Lin","doi":"10.1002/pst.70037","DOIUrl":"10.1002/pst.70037","url":null,"abstract":"<p><p>Sample size determination in Bayesian randomized phase II trial design often relies on computationally intensive search methods, presenting challenges in terms of feasibility and efficiency. We propose a novel approach that greatly reduces the computing time of sample size calculations for Bayesian trial designs. Our approach innovatively connects group sequential design with Bayesian trial design and leverages the proportional relationship between sample size and the squared drift parameter. This results in a faster algorithm. By employing regression analysis, our method can accurately pinpoint the required sample size with significantly reduced computational burden. Through theoretical justification and extensive numerical evaluations, we validate our approach and illustrate its efficiency across a wide range of common trial scenarios, including binary endpoint with Beta-Binomial model, normal endpoint, binary/ordinal endpoint under Bayesian generalized linear model, and survival endpoints under Bayesian piecewise exponential models. To facilitate the use of our methods, we create an R package named \"BayesSize\" on GitHub.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 5","pages":"e70037"},"PeriodicalIF":1.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144855998","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":"Target Aggregate Data Adjustment Method for Transportability Analysis Utilizing Summary-Level Data From the Target Population.","authors":"Yichen Yan, Quang Vuong, Rebecca K Metcalfe, Tianyu Guan, Haolun Shi, Jay J H Park","doi":"10.1002/pst.70029","DOIUrl":"10.1002/pst.70029","url":null,"abstract":"<p><p>Transportability analysis is a causal inference framework used to evaluate the external validity of studies by transporting treatment effects from a study sample to an external target population by adjusting for differences in the distributions of their effect modifiers. Most existing methods require individual patient-level data (IPD) for both the source and the target population, narrowing its applicability when only target aggregate-level data (AgD) are available. For survival analysis, accounting for censoring may be needed to reduce bias, yet AgD-based transportability methods in the presence of informative-censoring remain underexplored. Here, we propose a two-stage weighting framework named \"Target Aggregate Data Adjustment\" (TADA) that can simultaneously adjust for both censoring bias and distributional imbalances of effect modifiers. In our framework, the final weights are the product of the time-varying inverse probability of censoring weights and participation weights derived using the method of moments. We have conducted an extensive simulation study to evaluate TADA's performance. We have applied our methods to a real case study on the squamous non-small-cell lung cancer trial (NCT00981058). Our results indicate that TADA can effectively control the bias resulting from moderate censoring representative of most practical scenarios, and enhance the application and clinical interpretability of transportability analyses in settings with limited data availability.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 5","pages":"e70029"},"PeriodicalIF":1.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12326296/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144789674","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":"Comparing Estimation Methods for the Area Under the Bi-Weibull ROC Curve.","authors":"Ruhul Ali Khan, Musie Ghebremichael","doi":"10.1002/pst.70038","DOIUrl":"10.1002/pst.70038","url":null,"abstract":"<p><p>In this paper, we carried out extensive simulation studies to compare the performances of partial and maximum likelihood based methods for estimating the area under the bi-Weibull ROC curve. Further, real data sets from HIV/AIDS research were analyzed for illustrative purposes. Simulation results suggest that both methods perform well and yield similar results for Weibull data. However, for non-Weibull data, both methods perform poorly. The bi-Weibull model yields smooth estimates of ROC curves and a closed-form expression for the area under the ROC curve. Moreover, by adjusting its shape parameter, the bi-Weibull model can represent a variety of distributions, such as exponential, Rayleigh, normal, and extreme value distributions. Its compatibility with Cox's proportional hazards model facilitates the derivation of covariate-adjusted ROC curves and supports analyses involving correlated and longitudinal biomarkers. These properties make the model very useful in the ROC curve analyses. Thus, the bi-Weibull model should be considered as an alternative when the restrictive distributional assumptions of the commonly used parametric models (e.g., binormal model) are not met.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 5","pages":"e70038"},"PeriodicalIF":1.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145001214","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 General Approach for Sample Size Calculation With Nonproportional Hazards and Cure Rates.","authors":"Huan Cheng, Xiaoyun Li, Jianghua He","doi":"10.1002/pst.70024","DOIUrl":"10.1002/pst.70024","url":null,"abstract":"<p><p>With the ongoing advancements in cancer drug development, a subset of patients can live quite long, or are even considered cured in certain cancer types. Additionally, nonproportional hazards, such as delayed treatment effects and crossing hazards, are commonly observed in cancer clinical trials with immunotherapy. To address these challenges, various cure models have been proposed to integrate the cure rate into trial designs and accommodate delayed treatment effects. In this article, we introduce a unified approach for calculating sample sizes, taking into account different cure rate models and nonproportional hazards. Our approach supports both the traditional weighted logrank test and the Maxcombo test, which demonstrates robust performance under nonproportional hazards. Furthermore, we assess the accuracy of our sample size estimation through Monte Carlo simulations across various scenarios and compare our method with existing approaches. Several illustrative examples are provided to demonstrate the proposed method.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 4","pages":"e70024"},"PeriodicalIF":1.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144576028","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":"Personalized Treatment Selection for Multivariate Ordinal Scale Outcomes and Multiple Treatments.","authors":"Chathura Siriwardhana, Bakeerathan Gunaratnam, K B Kulasekera","doi":"10.1002/pst.70023","DOIUrl":"10.1002/pst.70023","url":null,"abstract":"<p><p>In this study, we present an innovative approach for tailoring treatment selection on an individualized basis in the presence of correlated multiple responses, particularly those measured on ordinal scales, including binary responses. Our methodology involves the utilization of rank lists for treatments, generated from probabilities of observing responses of higher order than each level of the ordinal outcome, conditional on patient covariate measurements. We introduce a rank aggregation technique designed to amalgamate multiple lists of ranks, allowing for correlations both within these lists and among elements within each list. Our approach is versatile, accommodating any number of treatments and responses, and is applicable across a wide range of models. Our method offers flexibility by allowing the integration of response weights, enabling customization based on patient and clinician preferences on an individual case basis for optimal treatment decisions. To evaluate the performance of our proposed method in finite samples, we conducted a simulation study. Furthermore, we provide two illustrative examples using data from clinical trials on Cystic Fibrosis and Alzheimer's Disease, demonstrating the application of our proposed procedure in real-world scenarios.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 4","pages":"e70023"},"PeriodicalIF":1.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144476282","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":"Estimating the Variance of Covariate-Adjusted Estimators of Average Treatment Effects in Clinical Trials With Binary Endpoints.","authors":"Dominic Magirr, Craig Wang, Alexander Przybylski, Mark Baillie","doi":"10.1002/pst.70021","DOIUrl":"10.1002/pst.70021","url":null,"abstract":"<p><p>Covariate-adjusted estimators of average treatment effects in clinical trials are typically more efficient than unadjusted estimators. Recent guidance from the FDA is highly detailed regarding the appropriate use of covariate adjustment for point estimation. Less direction is provided, however, on how to estimate the variance of such estimators. In this paper, we demonstrate that a precise description of the estimand is necessary to avoid ambiguity when comparing variance estimators for average treatment effects involving binary endpoints. When considering the suitability of a proposed estimand, together with a corresponding variance estimator, it is important to consider that the patients enrolled in clinical trials are typically a convenience sample. Since there is no unique way to map this process into formal statistical assumptions, it follows that a range of estimands, and therefore a range of variance estimators, may be acceptable. We aim to highlight through simulation results how the properties of proposed variance estimators differ, as well as the underlying reasons.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 4","pages":"e70021"},"PeriodicalIF":1.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144485506","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":"Applying Bias Correction Methods to Build Hybrid Controls Using Real-World Patients for a Phase IIb Randomized Controlled Trial of Baricitinib for Rheumatoid Arthritis.","authors":"Heather J Litman, Kristin M Sheffield, Oksana Pugach, Mingyang Shan, Paul Lakin, Zhanglin Lin Cui, Sarah E Curtis, Joseph A Johnston, Leslie R Harrold","doi":"10.1002/pst.70020","DOIUrl":"10.1002/pst.70020","url":null,"abstract":"<p><p>Studies incorporating randomized controlled trial (RCT) controls with real-world (RW) patients face concerns of selection bias and unmeasured confounding. To evaluate two methods (Match, Test-then-Pool [MTP] and Matching and Bias Adjustment [MBA]) to construct a hybrid control arm using concurrent RCT control patients and trial-eligible RW patients from the CorEvitas Rheumatoid Arthritis (RA) Registry to replicate the treatment effect obtained from the original RCT. The measures considered estimate the effect of baricitinib on the proportion achieving 20% improvement in American College of Rheumatology response (ACR20) and change in disease activity (Clinical Disease Activity Index, CDAI) for patients with moderately to severely active rheumatoid arthritis. Patients included RCT treated (n = 102), a 50% random sample of the RCT control (n = 49), and trial-eligible RW (n = 359). MTP used optimal propensity score (PS) matching to replace half of the RCT control group with a comparable RW control group (n = 49) and tested for differences between RCT and RW control outcomes before combining into the hybrid control arm for analysis. MBA leveraged 3 sets of optimal PS matches to identify a RW control group and adjusted for residual differences in outcomes between RCT and RW controls during analysis when creating the hybrid control arm. Optimal matching produced a matched RW cohort with satisfactory balance on the majority of key covariates compared to RCT participants. Using MTP, the odds ratio (OR) for ACR20 from covariate adjusted modeling was 4.71 (95% confidence interval [CI] 2.51, 9.15). Using MBA, ACR20 treatment effect from bias and covariate adjusted modeling was 4.76 (2.10, 10.81). For reference, the OR for the treatment difference of ACR20 for the trial-eligible population was 3.81 (2.13, 6.95) and was 4.74 (2.32, 10.00) for the RCT population with 50% sample of controls. With MTP with covariate adjustment, the treatment effect of mean CDAI change was -8.96 (-12.58, -5.35). For MBA with bias and covariate adjustment, the treatment effect of mean CDAI change was -9.85 (-15.16, -5.02). In the original RCT, the mean CDAI change was -8.88 (-12.58, -5.18) and for the RCT population with 50% sample of controls, the mean CDAI change was -10.10 (-14.46, -5.74). With both hybrid control methods using the combination of well-matched RWD and RCT control patients, we replicated the Phase IIb trial results. Trial Registration: Data were used from Eli Lilly and Company's clinical trial (NCT01185353) and the CorEvitas Rheumatoid Arthritis Registry (NCT01402661), both registered at clinicaltrials.gov.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 4","pages":"e70020"},"PeriodicalIF":1.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144266997","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":"Correction to \"Mixture Experimentation in Pharmaceutical Formulations: A Tutorial\".","authors":"","doi":"10.1002/pst.70017","DOIUrl":"10.1002/pst.70017","url":null,"abstract":"","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 4","pages":"e70017"},"PeriodicalIF":1.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144226184","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":"Further Practical Guidance on Adjusting Time-To-Event Outcomes for Treatment Switching.","authors":"Claire Watkins, Eva Kleine, Miguel Miranda, Emmanuel Bourmaud, Orlando Doehring","doi":"10.1002/pst.70019","DOIUrl":"10.1002/pst.70019","url":null,"abstract":"<p><p>The objective of this article is to bring together the key current information on practical considerations when conducting statistical analyses adjusting long-term outcomes for treatment switching, combining it with learnings from our own experience, thus providing a useful reference tool for analysts. When patients switch from their randomised treatment to another therapy that affects a subsequently observed outcome such as overall survival, there may be interest in estimating the treatment effect under a hypothetical scenario without the intercurrent event of switching. We describe the theory and provide guidance on how and when to conduct analyses using three commonly used complex approaches: rank preserving structural failure time models (RPSFTM), two-stage estimation (TSE), and inverse probability of censoring weighting (IPCW). Extensions and alternatives to the standard approaches are summarised. Important and sometimes misunderstood concepts such as recensoring and sources of variability are explained. An overview of available software and programming guidance is provided, along with an R code repository for a worked example, reporting recommendations, and a review of the current acceptability of these methods to regulatory and health technology assessment agencies. Since the current guidance on this topic is scattered across multiple sources, it is difficult for an analyst to obtain a good overview of all options and potential pitfalls. This paper is intended to save statisticians time and effort by summarizing important information in a single source. By also including recommendations for best practice, it aims to improve the quality of the analyses and reporting when adjusting time-to-event outcomes for treatment switching.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 4","pages":"e70019"},"PeriodicalIF":1.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12181803/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144340348","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":"The Choice Between Pearson's χ² Test and Fisher's Exact Test for 2 × 2 Tables.","authors":"Markus Neuhäuser, Graeme D Ruxton","doi":"10.1002/pst.70012","DOIUrl":"10.1002/pst.70012","url":null,"abstract":"<p><p>Pearson's asymptotic χ² test is often used to compare binary data between two groups. However, when the sample sizes or expected frequencies are small, the test is usually replaced by Fisher's exact test. Several alternative rules of thumb exist for defining \"small\" in this context. Replacing one test with another based on the obtained data is unusual in statistical practice. Moreover, this commonly-used switch is unnecessary because Pearson's χ² test can easily be carried out as an exact test for any sample sizes. Therefore, we recommend routinely using an exact test regardless of the obtained data. This change of approach allows prespecifying a particular test and a much less ambiguous and more reliable analysis.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 3","pages":"e70012"},"PeriodicalIF":1.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143743674","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}