Utility of Covariate-Adjusted Response-Adaptive Randomization in Survival Trials

Covariate-adjusted response-adaptive (CARA) randomization is used in clinical trials to balance the competing goals of assigning a greater number of study subjects to the better treatment and achieving high statistical efficiency in estimating treatment effects in the presence of covariates, while maintaining randomness in treatment assignments. In this article, we propose CARA randomization procedures for survival intervention trials with two treatment arms when the primary outcomes follow an exponential regression model. We demonstrate, through extensive simulation, that our CARA procedures can achieve some reduction in the number of events in the study without compromising power and Type I error, compared with balanced randomization designs, including in situations when the exponential model is misspecified. We conclude that the proposed CARA procedures can be suitable alternatives to the traditional balanced randomization designs in survival trials, provided response data are available during the recruitment phase to enable adaptations in the design. We illustrate the proposed methodology by redesigning two survival trials from the literature.     

A Graphical Comparison of Response-Adaptive Randomization Procedures

Response-adaptive randomization procedures have a dual goal of estimating the treatment effect and randomizing patients with a higher probability of receiving the superior treatment. These are competing objectives, and no procedure in the literature is “perfect” with respect to both objectives. For clinical trials of two treatments, we discuss metrics for comparing response-adaptive randomization procedures that can be represented graphically to compare designs. These metrics involve the simulated distribution of the set of jointly sufficient statistics for estimating functions of the unknown parameters. We explore the binary response and normal cases, and compare numerous procedures found in the literature. We distinguish between metrics of efficiency and metrics that measure ethical cost. Each of these is a function of the joint sufficient statistics. When graphed against each other, we can gauge competing designs in obtaining these competing objectives. We find that, contrary to asymptotic results, tuning parameters that affect the variability of the procedure do not have much impact in the finite case. In the binary response case, we find that procedures that target an optimal allocation based on ethical and efficiency considerations generally provide a better compromise design than procedures that do not. In the normal response case, a randomly reinforced urn tends to provide a good compromise procedure.     

A class of Covariate-Adjusted Response-Adaptive Allocation Designs for Multitreatment Binary Response Trials

A class of covariate-adjusted response-adaptive randomization procedures is developed for binary treatment outcomes in a phase III clinical trial set up involving multiple treatments. The target allocation is developed by combining the ethical aspects with statistical precision under the existence of treatment covariate interaction. Relevant measures of the performance for the proposed allocation designs are studied and compared.     

An Urn Model for Odds-Ratio-Based Response-Adaptive Phase III Clinical Trials for Two or More Treatments

Adaptive data-dependent allocation designs are used in phase III clinical trials having two or more competing treatments with sequential entrance of patients, in order to allocate a larger number of patients to the better treatment. The odds ratio is a popular concept for biomedical practitioners; hence, odds-ratio-based adaptive designs could be very useful in practice. Rosenberger et al. (2001 Rosenberger , W. F. , Vidyashankar , A. N. , Agarwal , D. K. ( 2001 ). Covariate-adjusted response-adaptive designs for binary response . J. Biopharm. Stat. 11 : 227 – 236 .[Taylor &; Francis Online] , [Google Scholar]) introduced an odds-ratio-based two-treatment response-adaptive design; however, they did not study the properties in details. In this article, we describe these designs by means of urn models and provide limiting results for them. Some properties of the design are also studied numerically. We compare the performance of the proposed design with some possible competitors with respect to a few criteria. A real dataset is used to illustrate the applicability of the proposed design. Thus, we provide a base for using odds-ratio-based response-adaptive designs in practice. We extend our design for covariates and also for more than two treatments. In particular, we study the three-treatment design in this article.     

Optimal Adaptive Designs for Binary Response Trials With Three Treatments

A fundamental question in response–adaptive randomization is: What allocation proportion should we target to achieve required power while resulting in fewer treatment failures? For comparing two treatments, such optimal allocations are well studied in the literature. However, few authors address the question for multiple treatments and the generalization of optimal allocations is necessary in practice. We are interested in finding the optimal allocation proportion, which achieves a required power of a multivariate test of homogeneity in binary response experiments while minimizing expected treatment failures at the same time. We propose such an optimal allocation for three treatments by giving an analytical solution for the optimization problem. Numerical studies show that a response–adaptive randomization procedure that targets proposed optimal allocation is superior to complete randomization. We also discuss some future research topics and additional issues on optimal adaptive designs.     

Covariate-Adjusted Adaptive Designs for Continuous Responses in a Phase III Clinical Trial: Recommendation for Practice

In this paper we propose a Bayesian method to combine safety data collected from two separate drug development programs using the same active drug substance but for different indications, formulations, or patient populations. The objective of combining the data across the programs is to better define the level of safety risk associated with the new indication or target population. There may be adverse events (AEs) observed in the new program that represent new safety signals. Our method is to explore the AEs using data from both development programs. Our approach utilizes data collected previously to assist in analyzing safety data from the new program. It is assumed that the frequency of a certain AE follows a distribution with a parameter that characterizes the safety risk level. The parameter is assumed to follow a distribution function. In the Bayesian framework, this distribution function is called a prior distribution in the absence of data and posterior distribution when updated by real data. The key concept behind our method is to use data from the previous program to construct a posterior distribution that will in turn serve as a prior distribution for the new program. The construction of this updated prior down weights data from the previous program to emphasize the new program and thus avoids simple pooling of the data across programs. Such “soft use” of previous information minimizes the potential for undue influence of previous data on the analysis. Data from the new program are used to update the prior distribution and compute the posterior distribution for the new program. Key statistics are then calculated from the posterior distribution to quantify the risk level for the new program. We have tested the proposed approach using data from a real Phase 2 study that was conducted as part of a clinical development program for a new indication of an approved drug. The results indicate that the estimated risk level was affected both by the observed event rates and the extents of exposure across the two development programs. This approach appropriately characterizes the safety profile across the two development programs and properly contextualizes new safety signals from the new program.     

Self-Designing Trial Combined with Classical Group Sequential Monitoring

ABSTRACT

At the interim analyses of a clinical trial, it is appealing to modify the originally planned sample size in order to achieve an adequate power to detect a meaningful treatment effect. We propose a flexible sequential monitoring scheme through combining the self-designing and classical group sequential methods. The maximum sample size does not have to be specified in advance and one efficacy interim analysis is conducted for the purpose of possible early termination after the first block of data is observed. At the interim analysis for efficacy, the usual sufficient test statistic is used and the type I error rate is adjusted to maintain the overall nominal level. At the final analysis, the test is constructed from a weighted average of the blockwise test statistics based on the sequentially collected data. The weight function at each stage is determined by the observed data prior to that stage. The futility stopping rule allows the trial to be terminated when there is no beneficial treatment effect. We conduct simulation studies to evaluate the performance of the proposed design.     

A Free Gift: An Adaptive Strategy in a Single-Arm Trial Using an Exact Test Through the Binomial Distribution

For medical product development within the same generation, single-arm trial designs are commonly implemented to test the performance of the new product against an objective performance criterion. When the primary endpoint is binary and the sample size is moderate, an exact test through the binomial distribution is usually used. This article shows that it is a free gift to add an adaptive component to a fixed-sample-size design so that when the interim result is marginal, the adaptive feature can be activated without any penalty. A hypothetical example is used to illustrate the application of this method.     

我院临床试验用药品的管理体会

目的:探讨临床试验用药品的管理模式。方法:比较目前临床试验用药品的两种管理模式(专业管理药品与设立中心试验药房管理药品)的优缺点,并介绍我院建立中心试验药房管理试验用药品的做法和体会。结果与结论:两种管理模式各有利弊,但在我院的实践中,认为采用中心试验药房管理模式在对试验用药品的管理上更具有显著的优势,其可集中统一管理试验用药品,通过制定并不断完善相关管理制度,促进药物临床试验的规范开展和保障结果的可靠性。     

Stopping Boundaries of Flexible Sample Size Design With Flexible Trial Monitoring

In the group sequential (GS) approach with a fixed sample size design, the Type I error is controlled by the additivity of exit spending values. However, in a flexible sample size design where the sample size will be recalculated using the interim data, the overall Type I error rate can be inflated. Therefore, the predefined GS stopping boundaries have to be adjusted to maintain the Type I error level at each interim analysis and the at the overall level. The modified α spending function adjusted for sample size reestimation (SSR) is proposed to maintain the Type I error level. We use a unified approach and mathematically quantify the Type I error with and without sample size adjustment constraints. As a result, stopping boundaries can be obtained by inversely solving the exact Type I error functions. This unified approach, using Brownian motion theory, can be applied to normal, survival, and binary endpoints. Extensive simulations show the adjusted stopping boundaries can control the Type I error at each analysis and at the overall level.     

药物临床试验方案的常见问题分析

目的：结合临床试验实践探讨药物临床试验方案中存在的问题。方法：结合本院药物临床试验开展实际案例,就受试者入选／排除标准、试验程序与临床常规诊疗习惯差异、评价指标、试验对照设计及受试者的知情同意等方面,分析临床试验方案缺陷及注意事项。结果：药物临床试验方案本身会对临床试验质量造成显著影响。结论：科学、伦理、可操作是临床试验方案关注的重点内容。     

药物临床试验管理系统中质量控制体系的设计与应用

目的:为我国药物临床试验信息化规范管理提供参考。方法:对药物临床试验管理系统中质量控制体系功能模块设计要点与其应用进行探讨。结果与结论:该体系基于模块进行设计,具有电子签名、用户权限管理、审核管理、检查监督、稽查留痕等功能。该体系应用有利于保证药物临床试验质量,是良好的药物临床试验管理系统应具备的一部分。     

临床试验药物信息化管理系统的流程设计与应用

试验药物的规范化管理是药物临床试验管理的重点。本研究在医院现有系统基础上设计了一套试验药物管理系统,通过信息化手段减少因手工记录而导致的出错率,实现了试验药物管理的电子化溯源。该系统兼容性良好,避免了自购系统需对接医院HIS进行二次开发的风险和弊端,便于临床使用,有效提升了试验药物管理的质量水平和工作效率,使临床试验的开展更加规范、科学和有序,值得今后进一步推广应用。     

药物临床试验电子化数据采集管理系统的设计与应用

目的:实现药物临床试验电子化数据采集及管理,为我国药物临床试验信息化奠定基础。方法:自主研发药物临床试验电子化数据采集管理系统,并将其应用到实际的药物临床试验管理中。结果与结论:该系统采用B/S三层结构设计,具有用户管理、数据采集器设计和填写、质疑管理、统计分析及稽查留痕等功能模块。其应用提高了临床研究管理效率,节省了时间和管理成本,更好地保证了数据的真实性、准确性和完整性。     

我院药物临床试验实施质量回顾性分析

目的:评价我院药物临床试验的实施质量,探讨临床试验实施中的不足,并提出有针对性的改进措施。方法:根据2007-2013年我院药物临床试验项目的质控记录,对照项目实施质量评分标准,分析临床试验质量得分以及各要素的得分情况。并分别对实行和未实行临床研究协调员(CRC)制的A科室和B科室,以及国际多中心和同期国内多中心项目的实施质量进行比较。结果:我院药物临床试验质量得分逐年提高,2013年达90.4分,比2007年增加了1倍;但2013年有3个要素的得分率低于90%,分别为试验实施、试验药物管理和试验记录。A科室项目完成质量得分均高于B科室;2011-2013年A科室的优势要素(得分率差异>15%)显著优于B科室,主要为试验实施(98.8%vs.75.0%)、试验药物管理(100%vs.83.0%)、试验记录(89.9%vs.63.0%)和试验资料管理(100%vs.81.2%),均P<0.01。国际多中心项目实施质量得分高于国内(94.7分vs.83.0分),P<0.01。结论:我院临床试验的薄弱环节主要为试验实施、试验药物管理和试验记录。而CRC制的推广以及国际多中心临床试验的承接是解决以上薄弱环节、提高临床试验质量的有效措施。     

临床试验药物管理中记录表的应用

目的:促进临床试验药物管理中记录表的规范应用。方法:按照我院临床试验药物的管理流程阐述各环节记录表的设计和应用体会。结果与结论:一套设计规范的记录表能有效减少信息遗漏,能够提高临床试验药物管理中包括储藏、温/湿度、接收、发放/回收、退还等环节的记录质量。     

Estimating Covariate-Adjusted Log Hazard Ratios in Randomized Clinical Trials Using Cox Proportional Hazards Models and Nonparametric Randomization Based Analysis of Covariance

In the context of randomized clinical trials with time-to-event outcomes, estimates of covariate-adjusted log hazard ratios for comparing two treatments are obtained via nonparametric analysis of covariance by forcing the difference in means for covariables to zero. The method avoids the assumption of proportional hazards for each of the covariates, and it provides an adjusted analysis for the same population average treatment effect that the unadjusted analysis addresses. It is primarily useful in regulatory clinical trials that require analyses to be specified a priori. To illustrate, the method is applied to a study of lung disease with multivariate time-to-event outcomes.     

剖宫产瘢痕妊娠的临床特点及不同临床类型治疗结果的回顾性分析

目的探讨剖宫产瘢痕妊娠(CSP)的临床特点及对不同临床类型的治疗效果进行回顾性分析。方法选取来我院接受剖宫产瘢痕妊娠治疗的患者79名,其中39名患者为外生型CSP,40名患者为内生型CSP,对所有患者的临床特点进行比较分析,并对相同手术方式对不同类型CSP的治疗效果进行分析研究,并将分析结果使用SPSS统计学软件进行统计学处理。结果①接受宫腔镜治疗的患者中,外生型CSP患者的术中出血量、手术时间及住院天数均明显多于内生型患者,P<0.05;②接受清宫术治疗的患者中,外生型CSP患者的术中出血量及手术时间明显多于内生型患者,P<0.05。结论通过对剖宫产瘢痕妊娠的临床特点进行分析研究,对于该病的诊断及最佳治疗方式的选择具有着重要的临床治疗意义。     

抗肿瘤创新药临床试验设计的一般路径

通过对抗肿瘤创新药Ⅰ～Ⅲ期临床试验各个环节抉择点的分析,结合当前的指导原则、法规和以往的案例,从各阶段的试验目的 、样本量、对照组的设置和对照药的选择及主要疗效观察指标的设立等,进行分析讨论,试图为抗肿瘤新药临床试验开发路径的设计提供参考建议.     

加替沙星片随机双盲对照治疗细菌性感染临床研究

目的:评价加替沙星片治疗细菌性感染的临床疗效和安全性。方法:39例病人,随机分为治疗组20例和对照组19例,分别应用加替沙星片和左氧氟沙星片,剂量均为0.2 g,bid,饭前口服给药,疗程7～14 d。结果:加替沙星片和左氧氟沙星片的临床有效率分别为90.0%和84.2%,细菌清除率分别为88.2%和87.5%,两组不良反应发生率分别为10%和15%,试验中未见光敏反应及其他严重不良反应。结论:加替沙星片可作为治疗细菌性感染有效、安全的抗菌药。