Pharmaceutical Statistics最新文献

On Some Modeling Issues in Estimating Vaccine Efficacy 关于估算疫苗疗效的一些模型问题

IF 1.5 4区医学

Pharmaceutical Statistics Pub Date : 2024-09-10 DOI: 10.1002/pst.2440

Mauro Gasparini

引用次数: 0

Propensity Score Analysis With Baseline and Follow-Up Measurements of the Outcome Variable. 对结果变量进行基线和随访测量的倾向得分分析。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-09-05 DOI: 10.1002/pst.2436

Peter C Austin

{"title":"Propensity Score Analysis With Baseline and Follow-Up Measurements of the Outcome Variable.","authors":"Peter C Austin","doi":"10.1002/pst.2436","DOIUrl":"https://doi.org/10.1002/pst.2436","url":null,"abstract":"A common feature in cohort studies is when there is a baseline measurement of the continuous follow-up or outcome variable. Common examples include baseline measurements of physiological characteristics such as blood pressure or heart rate in studies where the outcome is post-baseline measurement of the same variable. Methods incorporating the propensity score are increasingly being used to estimate the effects of treatments using observational studies. We examined six methods for incorporating the baseline value of the follow-up variable when using propensity score matching or weighting. These methods differed according to whether the baseline value of the follow-up variable was included or excluded from the propensity score model, whether subsequent regression adjustment was conducted in the matched or weighted sample to adjust for the baseline value of the follow-up variable, and whether the analysis estimated the effect of treatment on the follow-up variable or on the change from baseline. We used Monte Carlo simulations with 750 scenarios. While no analytic method had uniformly superior performance, we provide the following recommendations: first, when using weighting and the ATE is the target estimand, use an augmented inverse probability weighted estimator or include the baseline value of the follow-up variable in the propensity score model and subsequently adjust for the baseline value of the follow-up variable in a regression model. Second, when the ATT is the target estimand, regardless of whether using weighting or matching, analyze change from baseline using a propensity score that excludes the baseline value of the follow-up variable.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142140774","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

Generalizing Treatment Effect to a Target Population Without Individual Patient Data in a Real-World Setting. 在真实世界环境中，在没有个体患者数据的情况下，将治疗效果推广到目标人群。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-09-03 DOI: 10.1002/pst.2435

Hui Quan, Tong Li, Xun Chen, Gang Li

{"title":"Generalizing Treatment Effect to a Target Population Without Individual Patient Data in a Real-World Setting.","authors":"Hui Quan, Tong Li, Xun Chen, Gang Li","doi":"10.1002/pst.2435","DOIUrl":"https://doi.org/10.1002/pst.2435","url":null,"abstract":"The innovative use of real-world data (RWD) can answer questions that cannot be addressed using data from randomized clinical trials (RCTs). While the sponsors of RCTs have a central database containing all individual patient data (IPD) collected from trials, analysts of RWD face a challenge: regulations on patient privacy make access to IPD from all regions logistically prohibitive. In this research, we propose a double inverse probability weighting (DIPW) approach for the analysis sponsor to estimate the population average treatment effect (PATE) for a target population without the need to access IPD. One probability weighting is for achieving comparable distributions in confounders across treatment groups; another probability weighting is for generalizing the result from a subpopulation of patients who have data on the endpoint to the whole target population. The likelihood expressions for propensity scores and the DIPW estimator of the PATE can be written to only rely on regional summary statistics that do not require IPD. Our approach hinges upon the positivity and conditional independency assumptions, prerequisites to most RWD analysis approaches. Simulations are conducted to compare the performances of the proposed method against a modified meta-analysis and a regular meta-analysis.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142126379","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

Comparative Analyses of Bioequivalence Assessment Methods for In Vitro Permeation Test Data. 体外渗透试验数据的生物等效性评估方法比较分析。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-08-24 DOI: 10.1002/pst.2434

Sami Leon, Elena Rantou, Jessica Kim, Sungwoo Choi, Nam Hee Choi

{"title":"Comparative Analyses of Bioequivalence Assessment Methods for In Vitro Permeation Test Data.","authors":"Sami Leon, Elena Rantou, Jessica Kim, Sungwoo Choi, Nam Hee Choi","doi":"10.1002/pst.2434","DOIUrl":"https://doi.org/10.1002/pst.2434","url":null,"abstract":"For topical, dermatological drug products, an in vitro option to determine bioequivalence (BE) between test and reference products is recommended. In particular, in vitro permeation test (IVPT) data analysis uses a reference-scaled approach for two primary endpoints, cumulative penetration amount (AMT) and maximum flux (Jmax), which takes the within donor variability into consideration. In 2022, the Food and Drug Administration (FDA) published a draft IVPT guidance that includes statistical analysis methods for both balanced and unbalanced cases of IVPT study data. This work presents a comprehensive evaluation of various methodologies used to estimate critical parameters essential in assessing BE. Specifically, we investigate the performance of the FDA draft IVPT guidance approach alongside alternative empirical and model-based methods utilizing mixed-effects models. Our analyses include both simulated scenarios and real-world studies. In simulated scenarios, empirical formulas consistently demonstrate robustness in approximating the true model, particularly in effectively addressing treatment-donor interactions. Conversely, the effectiveness of model-based approaches heavily relies on precise model selection, which significantly influences their results. The research emphasizes the importance of accurate model selection in model-based BE assessment methodologies. It sheds light on the advantages of empirical formulas, highlighting their reliability compared to model-based approaches and offers valuable implications for BE assessments. Our findings underscore the significance of robust methodologies and provide essential insights to advance their understanding and application in the assessment of BE, employed in IVPT data analysis.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142047000","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

Simultaneous Inference Using Multiple Marginal Models. 使用多重边际模型进行同步推理。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-08-21 DOI: 10.1002/pst.2428

Ludwig A Hothorn, Christian Ritz, Frank Schaarschmidt, Signe M Jensen, Robin Ristl

{"title":"Simultaneous Inference Using Multiple Marginal Models.","authors":"Ludwig A Hothorn, Christian Ritz, Frank Schaarschmidt, Signe M Jensen, Robin Ristl","doi":"10.1002/pst.2428","DOIUrl":"https://doi.org/10.1002/pst.2428","url":null,"abstract":"This tutorial describes single-step low-dimensional simultaneous inference with a focus on the availability of adjusted p values and compatible confidence intervals for more than just the usual mean value comparisons. The basic idea is, first, to use the influence of correlation on the quantile of the multivariate t-distribution: the higher the less conservative. In addition, second, the estimability of the correlation matrix using the multiple marginal models approach (mmm) using multiple models in the class of linear up to generalized linear mixed models. The underlying maxT-test using mmm is discussed by means of several real data scenarios using selected R packages. Surprisingly, different features are highlighted, among them: (i) analyzing different-scaled, correlated, multiple endpoints, (ii) analyzing multiple correlated binary endpoints, (iii) modeling dose as qualitative factor and/or quantitative covariate, (iv) joint consideration of several tuning parameters within the poly-k trend test, (v) joint testing of dose and time, (vi) considering several effect sizes, (vii) joint testing of subgroups and overall population in multiarm randomized clinical trials with correlated primary endpoints, (viii) multiple linear mixed effect models, (ix) generalized estimating equations, and (x) nonlinear regression models.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142009206","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

Sample Size Reestimation in Stochastic Curtailment Tests With Time-to-Events Outcome in the Case of Nonproportional Hazards Utilizing Two Weibull Distributions With Unknown Shape Parameters. 在非比例危害的情况下，利用具有未知形状参数的两个 Weibull 分布，对具有时间到事件结果的随机缩尾试验进行样本量重估。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-08-18 DOI: 10.1002/pst.2429

Palash Sharma, Milind A Phadnis

{"title":"Sample Size Reestimation in Stochastic Curtailment Tests With Time-to-Events Outcome in the Case of Nonproportional Hazards Utilizing Two Weibull Distributions With Unknown Shape Parameters.","authors":"Palash Sharma, Milind A Phadnis","doi":"10.1002/pst.2429","DOIUrl":"https://doi.org/10.1002/pst.2429","url":null,"abstract":"Stochastic curtailment tests for Phase II two-arm trials with time-to-event end points are traditionally performed using the log-rank test. Recent advances in designing time-to-event trials have utilized the Weibull distribution with a known shape parameter estimated from historical studies. As sample size calculations depend on the value of this shape parameter, these methods either cannot be used or likely underperform/overperform when the natural variation around the point estimate is ignored. We demonstrate that when the magnitude of the Weibull shape parameters changes, unblinded interim information on the shape of the survival curves can be useful to enrich the final analysis for reestimation of the sample size. For such scenarios, we propose two Bayesian solutions to estimate the natural variations of the Weibull shape parameter. We implement these approaches under the framework of the newly proposed relative time method that allows nonproportional hazards and nonproportional time. We also demonstrate the sample size reestimation for the relative time method using three different approaches (internal pilot study approach, conditional power, and predictive power approach) at the interim stage of the trial. We demonstrate our methods using a hypothetical example and provide insights regarding the practical constraints for the proposed methods.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142000525","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

Principles for Defining Estimands in Clinical Trials-A Proposal. 定义临床试验估算值的原则--一项建议。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-08-13 DOI: 10.1002/pst.2432

Tobias Mütze, James Bell, Stefan Englert, Philip Hougaard, Dan Jackson, Vivian Lanius, Henrik Ravn

{"title":"Principles for Defining Estimands in Clinical Trials-A Proposal.","authors":"Tobias Mütze, James Bell, Stefan Englert, Philip Hougaard, Dan Jackson, Vivian Lanius, Henrik Ravn","doi":"10.1002/pst.2432","DOIUrl":"https://doi.org/10.1002/pst.2432","url":null,"abstract":"The ICH E9(R1) guideline outlines the estimand framework, which aligns planning, design, conduct, analysis, and interpretation of a clinical trial. The benefits and value of using this framework in clinical trials have been outlined in the literature, and guidance has been provided on how to choose the estimand and define the estimand attributes. Although progress has been made in the implementation of estimands in clinical trials, to the best of our knowledge, there is no published discussion on the basic principles that estimands in clinical trials should fulfill to be well defined and consistent with the ideas presented in the ICH E9(R1) guideline. Therefore, in this Viewpoint article, we propose four key principles for defining an estimand. These principles form a basis for well-defined treatment effects that reflect the estimand thinking process. We hope that this Viewpoint will complement ICH E9(R1) and stimulate a discussion on which fundamental properties an estimand in a clinical trial should have and that such discussions will eventually lead to an improved clarity and precision for defining estimands in clinical trials.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141976330","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 Predictive Probability Based on a Bivariate Index Vector for Single-Arm Phase II Study With Binary Efficacy and Safety Endpoints. 基于双变量指数向量的贝叶斯预测概率，用于具有二元有效性和安全性终点的单臂 II 期研究。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-08-13 DOI: 10.1002/pst.2431

Takuya Yoshimoto, Satoru Shinoda, Kouji Yamamoto, Kouji Tahata

{"title":"Bayesian Predictive Probability Based on a Bivariate Index Vector for Single-Arm Phase II Study With Binary Efficacy and Safety Endpoints.","authors":"Takuya Yoshimoto, Satoru Shinoda, Kouji Yamamoto, Kouji Tahata","doi":"10.1002/pst.2431","DOIUrl":"https://doi.org/10.1002/pst.2431","url":null,"abstract":"In oncology, Phase II studies are crucial for clinical development plans as such studies identify potent agents with sufficient activity to continue development in the subsequent Phase III trials. Traditionally, Phase II studies are single-arm studies, with the primary endpoint being short-term treatment efficacy. However, drug safety is also an important consideration. In the context of such multiple-outcome designs, predictive probability-based Bayesian monitoring strategies have been developed to assess whether a clinical trial will provide enough evidence to continue with a Phase III study at the scheduled end of the trial. Therefore, we propose a new simple index vector to summarize the results that cannot be captured by existing strategies. Specifically, we define the worst and most promising situations for the potential effect of a treatment, then use the proposed index vector to measure the deviation between the two situations. Finally, simulation studies are performed to evaluate the operating characteristics of the design. The obtained results demonstrate that the proposed method makes appropriate interim go/no-go decisions.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141976329","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 Methods for Quality Tolerance Limit (QTL) Monitoring. 质量容限 (QTL) 监测的贝叶斯方法。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-08-09 DOI: 10.1002/pst.2427

J C Poythress, Jin Hyung Lee, Kentaro Takeda, Jun Liu

{"title":"Bayesian Methods for Quality Tolerance Limit (QTL) Monitoring.","authors":"J C Poythress, Jin Hyung Lee, Kentaro Takeda, Jun Liu","doi":"10.1002/pst.2427","DOIUrl":"https://doi.org/10.1002/pst.2427","url":null,"abstract":"In alignment with the ICH guideline for Good Clinical Practice [ICH E6(R2)], quality tolerance limit (QTL) monitoring has become a standard component of risk-based monitoring of clinical trials by sponsor companies. Parameters that are candidates for QTL monitoring are critical to participant safety and quality of trial results. Breaching the QTL of a given parameter could indicate systematic issues with the trial that could impact participant safety or compromise the reliability of trial results. Methods for QTL monitoring should detect potential QTL breaches as early as possible while limiting the rate of false alarms. Early detection allows for the implementation of remedial actions that can prevent a QTL breach at the end of the trial. We demonstrate that statistically based methods that account for the expected value and variability of the data generating process outperform simple methods based on fixed thresholds with respect to important operating characteristics. We also propose a Bayesian method for QTL monitoring and an extension that allows for the incorporation of partial information, demonstrating its potential to outperform frequentist methods originating from the statistical process control literature.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141907380","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 : 2024-08-09 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":"https://doi.org/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":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-08-09","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