检索结果-内蒙古大学图书馆

Sequential designs for individualized dosing in phase I cancer clinical trials

CONTEMPORARY CLINICAL TRIALS 2017年 63卷 51-58页

作者： Mao, Xuezhou Cheung, Ying Kuen Sanofi Biostat & Programming 55 Corp Dr Bridgewater MA 08807 USA Columbia Univ Dept Biostat 722 West 168 St New York NY 10032 USA

This paper addresses dose finding in clinical trials where individuals exhibit biologic characteristics that alter the toxicity risks of the individuals. In these situations, instead of determining a dose that works for every patient, the trial aims to identify a dosing algorithm that prescribes dose according to the patient's biomarker or pharmacokinetic expression. Specifically, we aim to dose patients around a pre-specified level of area under the curve of irinotecan concentration using the patients' baseline phenotypes that predict drug clearance. We propose least squares recursion procedures to estimate the dosing algorithm sequentially with an aim to treat patients in the trial around the true unknown dosing algorithm, and introduce a novel application of an eigenvalue theory that guarantees convergence to the true dosing algorithms. Our simulation study demonstrates that using an eigenvalue constraint improves the efficiency of the recursion by as large as 40%, while concentrating in-trial patient allocation around the true dosing algorithm. We also provide practical guidance on design calibration, and design future irinotecan studies based on data from our first trial. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Adaptive design Coherence Eigenvalue constraint Least squares recursion Precision medicine Pharmacokinetic variability

来源：评论

学校读者我要写书评

暂无评论

Methods for Missing Data Handling in Phase III Clinical Trials with Nonnormal Endpoints and Nonnormal Covariates

引用

STATISTICS IN BIOPHARMACEUTICAL RESEARCH 2020年第1期12卷 68-78页

作者： Fan, Chunpeng Wei, Lynn Koch, Gary G. Sanofi US Inc Dept Biostat & Programming 55 Corp Dr Bridgewater NJ 08807 USA Univ N Carolina Dept Biostat Chapel Hill NC 27515 USA

In randomized clinical trials, when the endpoint is the change from baseline at the last scheduled visit, various parametric, semiparametric, and nonparametric methods have been developed to handle the possible missing data due to dropouts. Although the last observation carried forward (LOCF) followed by an analysis of covariance (ANCOVA) model or a rank ANCOVA model and the mixed-effects model for repeated measures (MMRM) have been extensively compared and widely used even with presence of the covariates, they may lead to biased results when the required distributional or missing technique assumptions are not satisfied. Nonparametric missing data handling methods including the mean rank imputation (MRI) method relax the underlying distributional assumption;however, when covariates are present, conditions for it to be valid have been investigated to a very limited extent. This article rigorously derives asymptotic properties of the mean rank imputation method with the presence of a nonnormal covariate. The investigated methods are applied to an illustrative phase III clinical trial. Simulation studies confirmed the better performance of the mean rank imputation method in terms of Type I error rate control and power under certain mild conditions.

关键词： MAR Missing at random Missing not at random MNAR Nonparametric Rank

来源：评论

学校读者我要写书评

暂无评论

Semiparametric varying-coefficient regression analysis of recurrent events with applications to treatment switching

引用

STATISTICS IN MEDICINE 2018年第27期37卷 3959-3974页

作者： Li, Yang Qi, Li Sun, Yanqing Univ North Carolina Charlotte Dept Math & Stat Charlotte NC 28223 USA Sanofi Biostat & Programming Bridgewater NJ USA

This paper investigates the semiparametric statistical methods for recurrent events. The mean number of the recurrent events are modeled with the generalized semiparametric varying-coefficient model that can flexibly model three types of covariate effects: time-constant effects, time-varying effects, and covariate-varying effects. We assume that the time-varying effects are unspecified functions of time and the covariate-varying effects are parametric functions of an exposure variable specified up to a finite number of unknown parameters. Different link functions can be selected to provide a rich family of models for recurrent events data. The profile estimation methods are developed for the parametric and nonparametric components. The asymptotic properties are established. We also develop some hypothesis testing procedures to test validity of the parametric forms of covariate-varying effects. The simulation study shows that both estimation and hypothesis testing procedures perform well. The proposed method is applied to analyze a data set from an acyclovir study and investigate whether acyclovir treatment reduces the mean relapse recurrences.

关键词： link function model checking profile weighted least squares estimation recurrent events semiparametric model treatment switching varying-coefficients

来源：评论

学校读者我要写书评

暂无评论

Planning the duration of a survival group sequential trial with a fixed follow-up time for all subjects

引用

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2017年第15期46卷 7460-7478页

作者： Liu, Yanning Janssen Res & Dev Quantitat Sci China Shanghai Peoples R China Janssen Res & Dev LLC Biostat & Programming Titusville NJ USA

To explore the operation characteristics of survival group sequential trials with a fixed follow-up period, the accrual time and total trial duration to ensure power and type I error rate requirements are explained and investigated for hazard ratios ranging from 1.3 to 3.0, with slow or high accrual rate, and in the presence or absence of censoring. Impacts of hazard rate, accrual rate, and competitive censoring on accrual time and subsequently on total trial duration are carefully illustrated. Real time for interim analyses, needed number of events, and recruited number of subjects at time of interim analyses are also tabulated.

关键词： A fixed follow-up period Group sequential trial Operation characteristics Survival end point

来源：评论

学校读者我要写书评

暂无评论

Design and monitoring of survival trials based on restricted mean survival times

引用

CLINICAL TRIALS 2019年第6期16卷 616-625页

作者： Luo, Xiaodong Huang, Bo Quan, Hui Sanofi US Dept Biostat & Programming Res & Dev Bridgewater NJ 08807 USA Pfizer Inc New London CT USA

Background/Aims: Restricted mean survival time has become a popular treatment effect measurement because of its nice interpretability. However, study design based on restricted mean survival times often requires extensive simulation studies as the variance structure is hard to obtain analytically. This article aims to provide a flexible approach to conduct study design and monitoring based on the restricted mean survival times without resorting to simulation. Methods: We assume that both the event time and censoring time distributions are piecewise exponential, and the accrual distribution is piecewise uniform, with which the restricted mean survival times and their variance-covariance structure can be conveniently computed. Results: Since we allow arbitrary number of pieces in the piecewise exponential and uniform distributions, the resulting model can handle a wide range of scenarios. The usefulness of the approach is demonstrated via an example. Conclusion: The proposed approach is flexible and useful in the design and monitoring of survival trials based on restricted mean survival times.

关键词： Restricted mean survival times survival trials design and monitoring piecewise exponential non-uniform accrual

来源：评论

学校读者我要写书评

暂无评论

Blinded assessment of treatment effects for survival endpoint in an ongoing trial

引用

PHARMACEUTICAL STATISTICS 2012年第3期11卷 204-213页

作者： Xie, Jun Quan, Hui Zhang, Ji Purdue Univ Dept Stat W Lafayette IN 47907 USA Sanofi Aventis Biostat & Programming Dept Bridgewater NJ 08807 USA

Many assumptions, including assumptions regarding treatment effects, are made at the design stage of a clinical trial for power and sample size calculations. It is desirable to check these assumptions during the trial by using blinded data. Methods for sample size re-estimation based on blinded data analyses have been proposed for normal and binary endpoints. However, there is a debate that no reliable estimate of the treatment effect can be obtained in a typical clinical trial situation. In this paper, we consider the case of a survival endpoint and investigate the feasibility of estimating the treatment effect in an ongoing trial without unblinding. We incorporate information of a surrogate endpoint and investigate three estimation procedures, including a classification method and two expectationmaximization (EM) algorithms. Simulations and a clinical trial example are used to assess the performance of the procedures. Our studies show that the expectationmaximization algorithms highly depend on the initial estimates of the model parameters. Despite utilization of a surrogate endpoint, all three methods have large variations in the treatment effect estimates and hence fail to provide a precise conclusion about the treatment effect. Copyright (c) 2012 John Wiley & Sons, Ltd.

关键词： EM algorithm hazard ratio interim analysis surrogate endpoint

来源：评论

学校读者我要写书评

暂无评论

Bias reduction using surrogate endpoints as auxiliary variables

引用

ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS 2019年第4期71卷 837-852页

作者： Takagi, Yoshiharu Kano, Yutaka Sanofi KK Biostat & Programming Tokyo 1631488 Japan Osaka Univ Div Math Sci Grad Sch Engn Sci Toyonaka Osaka 5608531 Japan

Recently, it is becoming more active to apply appropriate statistical methods dealing with missing data in clinical trials. Under not missing at random missingness, MLE based on direct-likelihood, or observed likelihood, possibly has a serious bias. A solution to the bias problem is to add auxiliary variables such as surrogate endpoints to the model for the purpose of reducing the bias. We theoretically studied the impact of an auxiliary variable on MLE and evaluated the bias reduction or inflation in the case of several typical correlation structures.

关键词： Auxiliary variables Surrogate endpoints Direct-likelihood Not missing at random missingness data

来源：评论

学校读者我要写书评

暂无评论

Dose-finding studies, MCP-Mod, model selection, and model averaging: Two applications in the real world

引用

CLINICAL TRIALS 2014年第4期11卷 476-484页

作者： Verrier, Dominique Sivapregassam, Sindou Solente, Anne-Catherine Sanofi R&D Biostat & Programming Dept F-34184 Montpellier France Inserm DGOS CIE 802 Ctr Invest Clin Epidemiol Clin Antilles Guyane Guyane France Sanofi R&D Dept Biostat & Programming Paris France

Background Phase II clinical trials are important milestones to determine whether a dose-effect exists and to decide on future doses to use in confirmatory studies. To take into account the overall shape of the dose response curve, modeling the relationship by linear or non-linear models is preferable to the classical pair-wise comparisons of the effect of each dose versus the placebo or the comparator. The multiple comparisons and modeling approach has been developed within the last 10 years to address this important question in the clinical development of drugs. Despite some recent publications referring to this methodology, few detailed applications have been shown so far and several practical questions remain to be addressed. Methods Starting from a set of candidate models, model selection using classical methods criteria is possible. However, it suffers some limitations, not taking into account the uncertainty of the selection process itself. An attractive solution is to use model averaging, which applies appropriate weights to the parameters (e.g., the minimum effective dose) obtained from each model. Results A discussion of the selection criteria is first presented. Through two real examples, how to proceed with model selection and model averaging is presented and discussed. Limitations The first multiple comparisons and modeling approach papers addressed normal responses. More recently, an extension of this methodology has been proposed to deal with other types of responses, in particular binary, time-to-event and longitudinal data. Questions that remain are concerned with the choice of the candidate models and of their parameters' guesstimates. Conclusions The analysis of clinical dose-finding studies using a modeling of the entire curve offers a promising alternative as compared with the classical multiple comparisons methods, while not compromising the necessary rigor of the analysis.

关键词：

来源：评论

学校读者我要写书评

暂无评论

ZIAQ: a quantile regression method for differential expression analysis of single-cell RNA-seq data

引用

BIOINFORMATICS 2020年第10期36卷 3124-3130页

作者： Zhang, Wenfei Wei, Ying Zhang, Donghui Xu, Ethan Y. Sanofi Dept Biostat & Programming Framingham MA 01701 USA Columbia Univ Dept Biostat New York NY 10032 USA Sanofi Translat Sci Framingham MA 01701 USA

Motivation: Single-cell RNA sequencing (scRNA-seq) has enabled the simultaneous transcriptomic profiling of individual cells under different biological conditions. scRNA-seq data have two unique challenges that can affect the sensitivity and specificity of single-cell differential expression analysis: a large proportion of expressed genes with zero or low read counts ('dropout' events) and multimodal data distributions. Results: We have developed a zero-inflation-adjusted quantile (ZIAQ) algorithm, which is the first method to account for both dropout rates and complex scRNA-seq data distributions in the same model. ZIAQ demonstrates superior performance over several existing methods on simulated scRNA-seq datasets by finding more differentially expressed genes. When ZIAQ was applied to the comparison of neoplastic and non-neoplastic cells from a human glioblastoma dataset, the ranking of biologically relevant genes and pathways showed clear improvement over existing methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Fast FSR Variable Selection with Applications to Clinical Trials

引用

BIOMETRICS 2009年第3期65卷 692-700页

作者： Boos, Dennis D. Stefanski, Leonard A. Wu, Yujun N Carolina State Univ Dept Stat Raleigh NC 27695 USA Sanofis Aventis Inc Dept Biostat & Programming Bridgewater NJ 08807 USA

A new version of the false selection rate variable selection method of Wu, Boos, and Stefanski (2007, Journal of the American Statistical Association 102, 235-243) is developed that requires no simulation. This version allows the tuning parameter in forward selection to be estimated simply by hand calculation from a summary table of output even for situations where the number of explanatory variables is larger than the sample size. Because of the computational simplicity, the method can be used in permutation tests and inside bagging loops for improved prediction. Illustration is provided in clinical trials for linear regression, logistic regression, and Cox proportional hazards regression.

关键词： Bagging False discovery rate False selection rate Forward selection LASSO Model error Model selection Regression

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：