Pharmaceutical Statistics最新文献

{"title":"Comparing various Bayesian random‐effects models for pooling randomized controlled trials with rare events","authors":"Minghong Yao, Yulong Jia, Fan Mei, Yuning Wang, Kang Zou, Ling Li, Xin Sun","doi":"10.1002/pst.2392","DOIUrl":"https://doi.org/10.1002/pst.2392","url":null,"abstract":"The meta‐analysis of rare events presents unique methodological challenges owing to the small number of events. Bayesian methods are often used to combine rare events data to inform decision‐making, as they can incorporate prior information and handle studies with zero events without the need for continuity corrections. However, the comparative performances of different Bayesian models in pooling rare events data are not well understood. We conducted a simulation to compare the statistical properties of four parameterizations based on the binomial‐normal hierarchical model, using two different priors for the treatment effect: weakly informative prior (WIP) and non‐informative prior (NIP), pooling randomized controlled trials with rare events using the odds ratio metric. We also considered the beta‐binomial model proposed by Kuss and the random intercept and slope generalized linear mixed models. The simulation scenarios varied based on the treatment effect, sample size ratio between the treatment and control arms, and level of heterogeneity. Performance was evaluated using median bias, root mean square error, median width of 95% credible or confidence intervals, coverage, Type I error, and empirical power. Two reviews are used to illustrate these methods. The results demonstrate that the WIP outperforms the NIP within the same model structure. Among the compared models, the model that included the treatment effect parameter in the risk model for the control arm did not perform well. Our findings confirm that rare events meta‐analysis faces the challenge of being underpowered, highlighting the importance of reporting the power of results in empirical studies.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"100 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140609621","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":"Comments on ‘standard and reference‐based conditional mean imputation’: Regulators and trial statisticians be aware!","authors":"Suzie Cro, Tim P. Morris, James H. Roger, James R. Carpenter","doi":"10.1002/pst.2373","DOIUrl":"https://doi.org/10.1002/pst.2373","url":null,"abstract":"Accurate frequentist performance of a method is desirable in confirmatory clinical trials, but is not sufficient on its own to justify the use of a missing data method. Reference‐based <jats:italic>conditional mean</jats:italic> imputation, with variance estimation justified solely by its frequentist performance, has the surprising and undesirable property that the estimated variance becomes smaller the greater the number of missing observations; as explained under jump‐to‐reference it effectively forces the true treatment effect to be <jats:italic>exactly</jats:italic> zero for patients with missing data.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"107 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140609764","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":"Tree-temporal scan statistics for safety signal detection in vaccine clinical trials","authors":"François Haguinet, Fabian Tibaldi, Christophe Dessart, Andrew Bate","doi":"10.1002/pst.2391","DOIUrl":"https://doi.org/10.1002/pst.2391","url":null,"abstract":"The evaluation of safety is critical in all clinical trials. However, the quantitative analysis of safety data in clinical trials poses statistical difficulties because of multiple potentially overlapping endpoints. Tree-temporal scan statistic approaches address this issue and have been widely employed in other data sources, but not to date in clinical trials. We evaluated the performance of three complementary scan statistical methods for routine quantitative safety signal detection: the self-controlled tree-temporal scan (SCTTS), a tree-temporal scan based on group comparison (BGTTS), and a log-rank based tree-temporal scan (LgRTTS). Each method was evaluated using data from two phase III clinical trials, and simulated data (simulation study). In the case study, the reference set was adverse events (AEs) in the Reference Safety Information of the evaluated vaccine. The SCTTS method had higher sensitivity than other methods, and after dose 1 detected 80 true positives (TP) with a positive predictive value (PPV) of 60%. The LgRTTS detected 49 TPs with 69% PPV. The BGTTS had 90% of PPV with 38 TPs. In the simulation study, with simulated reference sets of AEs, the SCTTS method had good sensitivity to detect transient effects. The LgRTTS method showed the best performance for the detection of persistent effects, with high sensitivity and expected probability of type I error. These three methods provide complementary approaches to safety signal detection in clinical trials or across clinical development programmes. All three methods formally adjust for multiple testing of large numbers of overlapping endpoints without being excessively conservative.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140563123","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":"Mathematical programming tools for randomization purposes in small two‐arm clinical trials: A case study with real data","authors":"Alan R. Vazquez, Weng‐Kee Wong","doi":"10.1002/pst.2388","DOIUrl":"https://doi.org/10.1002/pst.2388","url":null,"abstract":"Modern randomization methods in clinical trials are invariably adaptive, meaning that the assignment of the next subject to a treatment group uses the accumulated information in the trial. Some of the recent adaptive randomization methods use mathematical programming to construct attractive clinical trials that balance the group features, such as their sizes and covariate distributions of their subjects. We review some of these methods and compare their performance with common covariate‐adaptive randomization methods for small clinical trials. We introduce an energy distance measure that compares the discrepancy between the two groups using the joint distribution of the subjects' covariates. This metric is more appealing than evaluating the discrepancy between the groups using their marginal covariate distributions. Using numerical experiments, we demonstrate the advantages of the mathematical programming methods under the new measure. In the supplementary material, we provide R codes to reproduce our study results and facilitate comparisons of different randomization procedures.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"50 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140563093","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":"Predictive Ppk calculations for biologics and vaccines using a Bayesian approach – a tutorial","authors":"Jos Weusten, Jianfang Hu","doi":"10.1002/pst.2380","DOIUrl":"https://doi.org/10.1002/pst.2380","url":null,"abstract":"In pharmaceutical manufacturing, especially biologics and vaccines manufacturing, emphasis on speedy process development can lead to inadequate process development, which often results in less robust commercial manufacturing process after launch. Process performance index (Ppk) is a statistical measurement of the ability of a <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:href=\"https://en.wikipedia.org/wiki/Process_(engineering)\">process</jats:ext-link> to produce output within <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:href=\"https://en.wikipedia.org/wiki/Specification_(technical_standard)\">specification</jats:ext-link> limits over a period of time. In biopharmaceutical manufacturing, progression in process development is based on Critical Quality Attributes meeting their specification limits, lacking insight into the process robustness. Ppk is typically estimated after 15–30 commercial batches at which point it may be too late/too complex to make process adjustments to enhance robustness. The use of Bayesian statistics, prior knowledge, and input from Subject matter experts (SMEs) offers an opportunity to make predictions on process capability during the development cycle. Developing a standard methodology to assess long term process capability at various stages of development provides several benefits: provides opportunity for early insight into process vulnerabilities thereby enabling resolution pre‐licensure; identifies area of the process to prioritize and focus on during process development/process characterization (PC) using a data‐driven approach; and ultimately results in higher process robustness/process knowledge at launch. We propose a Bayesian‐based method to predict the performance of a manufacturing process at full manufacturing scale during the development and commercialization phase, before commercial data exists. Under Bayesian framework, limited development data for the process of interest at hand, data from similar products, general SME knowledge, and literature can be carefully formulated into informative priors. The implementation of the proposed approach is presented through two examples. To allow for continuous improvement during process development, we recommend to embed this approach of using predictive Ppk at pre‐defined commercialization stage‐gates, for example, at completion of process development, prior to and completion of PC, prior to technology transfer runs (Engineering/Process Performance Qualification, PPQ), and prior to commercial specification setting.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"29 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140562934","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":"Dynamic borrowing of historical controls adjusting for covariates in vaccine efficacy clinical trials","authors":"Andrea Callegaro, Yongyi Luo, Naveen Karkada, Toufik Zahaf","doi":"10.1002/pst.2384","DOIUrl":"https://doi.org/10.1002/pst.2384","url":null,"abstract":"Traditional vaccine efficacy trials usually use fixed designs and often require large sample sizes. Recruiting a large number of subjects can make the trial expensive, long, and difficult to conduct. A possible approach to reduce the sample size and speed up the development is to use historical controls. In this paper, we extend the robust mixture prior (RMP) approach (a well established approach for Bayesian dynamic borrowing of historical controls) to adjust for covariates. The adjustment is done using classical methods from causal inference: inverse probability of treatment weighting, G‐computation and double‐robust estimation. We evaluate these covariate‐adjusted RMP approaches using a comprehensive simulation study and demonstrate their use by performing a retrospective analysis of a prophylactic human papillomavirus vaccine efficacy trial. Adjusting for covariates reduces the drift between current and historical controls, with a beneficial effect on bias, control of type I error and power.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"23 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140562907","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":"Information‐based group sequential design for post‐market safety monitoring of medical products using real world data","authors":"Zhiwei Zhang, Carrie Nielson, Ching‐Yi Chuo, Zhishen Ye","doi":"10.1002/pst.2385","DOIUrl":"https://doi.org/10.1002/pst.2385","url":null,"abstract":"Real world healthcare data are commonly used in post‐market safety monitoring studies to address potential safety issues related to newly approved medical products. Such studies typically involve repeated evaluations of accumulating safety data with respect to pre‐defined hypotheses, for which the group sequential design provides a rigorous and flexible statistical framework. A major challenge in designing a group sequential safety monitoring study is the uncertainty associated with product uptake, which makes it difficult to specify the final sample size or maximum duration of the study. To deal with this challenge, we propose an information‐based group sequential design which specifies a target amount of information that would produce adequate power for detecting a clinically significant effect size. At each interim analysis, the variance estimate for the treatment effect of interest is used to compute the current information time, and a pre‐specified alpha spending function is used to determine the stopping boundary. The proposed design can be applied to regression models that adjust for potential confounders and/or heterogeneous treatment exposure. Simulation results demonstrate that the proposed design performs reasonably well in realistic settings","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"13 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140563107","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":"Balance diagnostics in propensity score analysis following multiple imputation: A new method","authors":"Sevinc Puren Yucel Karakaya, Ilker Unal","doi":"10.1002/pst.2389","DOIUrl":"https://doi.org/10.1002/pst.2389","url":null,"abstract":"The combination of propensity score analysis and multiple imputation has been prominent in epidemiological research in recent years. However, studies on the evaluation of balance in this combination are limited. In this paper, we propose a new method for assessing balance in propensity score analysis following multiple imputation. A simulation study was conducted to evaluate the performance of balance assessment methods (Leyrat's, Leite's, and new method). Simulated scenarios varied regarding the presence of missing data in the control or treatment and control group, and the imputation model with/without outcome. Leyrat's method was more biased in all the studied scenarios. Leite's method and the combine method yielded balanced results with lower mean absolute difference, regardless of whether the outcome was included in the imputation model or not. Leyrat's method had a higher false positive ratio and Leite's and combine method had higher specificity and accuracy, especially when the outcome was not included in the imputation model. According to simulation results, most of time, Leyrat's method and Leite's method contradict with each other on appraising the balance. This discrepancy can be solved using new combine method.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"241 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140563007","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 practical guide to the appropriate analysis of eGFR data over time: A simulation study","authors":"Todd DeVries, Kevin J. Carroll, Sandra A. Lewis","doi":"10.1002/pst.2381","DOIUrl":"https://doi.org/10.1002/pst.2381","url":null,"abstract":"In several therapeutic areas, including chronic kidney disease (CKD) and immunoglobulin A nephropathy (IgAN), there is a growing interest in how best to analyze estimated glomerular filtration rate (eGFR) data over time in randomized clinical trials including how to best accommodate situations where the rate of change is not anticipated to be linear over time, often due to possible short term hemodynamic effects of certain classes of interventions. In such situations, concerns have been expressed by regulatory authorities that the common application of single slope analysis models may induce Type I error inflation. This article aims to offer practical advice and guidance, including SAS codes, on the statistical methodology to be employed in an eGFR rate of change analysis and offers guidance on trial design considerations for eGFR endpoints. A two‐slope statistical model for eGFR data over time is proposed allowing for an analysis to simultaneously evaluate short term acute effects and long term chronic effects. A simulation study was conducted under a range of credible null and alternative hypotheses to evaluate the performance of the two‐slope model in comparison to commonly used single slope random coefficients models as well as to non‐slope based analyses of change from baseline or time normalized area under the curve (TAUC). Importantly, and contrary to preexisting concerns, these simulations demonstrate the absence of alpha inflation associated with the use of single or two‐slope random coefficient models, even when such models are misspecified, and highlight that any concern regarding model misspecification relates to power and not to lack of Type I error control.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"2013 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140563098","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":"Synergy detection: A practical guide to statistical assessment of potential drug combinations.","authors":"Elli Makariadou, Xuechen Wang, Nicholas Hein, Negera W Deresa, Kathy Mutambanengwe, Bie Verbist, Olivier Thas","doi":"10.1002/pst.2383","DOIUrl":"https://doi.org/10.1002/pst.2383","url":null,"abstract":"<p><p>Combination treatments have been of increasing importance in drug development across therapeutic areas to improve treatment response, minimize the development of resistance, and/or minimize adverse events. Pre-clinical in-vitro combination experiments aim to explore the potential of such drug combinations during drug discovery by comparing the observed effect of the combination with the expected treatment effect under the assumption of no interaction (i.e., null model). This tutorial will address important design aspects of such experiments to allow proper statistical evaluation. Additionally, it will highlight the Biochemically Intuitive Generalized Loewe methodology (BIGL R package available on CRAN) to statistically detect deviations from the expectation under different null models. A clear advantage of the methodology is the quantification of the effect sizes, together with confidence interval while controlling the directional false coverage rate. Finally, a case study will showcase the workflow in analyzing combination experiments.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140336499","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}