临床试验配对二项数据基于率比的非劣效性/等效性评价

在新药及医疗器械的临床试验中，经常会遇到采用阳性对照、以率作为主要指标，需要对两组率之间非劣效性／等效性进行评价的情形，关于两个独立组率之间非劣效性／等效性评价的统计学方法已有较多介绍，但是对于配对二项数据两组率之间的非劣效性／等效性评价统计学方法的研究和应用还不是很多。文献报道中，配对二项数据两组率间的非劣效性／等效性评价的界值可基于三种测量：率差、率比和比数比。     

临床试验评价的ROC分析方法

目的探讨新药临床试验效果的评价问题,给出一种新的统计分析方法。方法基于ROC分析给出多变量ROC模型,采用有序logit联系函数,利用SAS软件进行参数估计,得到有协变量及交互作用情况下的ROC曲线方程及曲线下面积。结果采用文中给出的方法,可以有效地扣除协变量的影响,用ROC曲线直观地评价药物之间的差别和作用。结论本文提供的方法能够更有效地对临床试验做出客观和准确的评价。     

非参数模型在以等级资料为终点的非劣效性临床试验中的应用及SAS实现

近年来,随着临床试验研究的不断深入,越来越多的临床试验被设计成非劣效性试验,用来评价新药非劣效于标准有效药物.在非劣效性试验中,试验目的不再是为了证明新药优效于标准有效药,而是只要证明新药非劣效于标准有效药疗效即可.     

三臂非劣效性设计生存时间数据的半参数统计推断方法

目的 推荐并改进一种用于新药临床试验中生存分析三臂非劣效性设计的半参数统计推断方法.方法 采用Cox比例风险模型建立用于生存时间数据的三臂非劣效检验方法,通过模拟试验验证该方法的有效性,并给出样本含量估计的R语言程序.结果 模拟试验结果证实,在样本删失风险率维持在一定水平时,三臂Cox模型能有效地控制Ⅰ类错误发生概率,并保证较高的检验效能,实际应用简单有效.结论 本文推荐的方法,可以有效地应用于三臂生存时间数据的非劣效性研究.     

非随机化医学研究中风险比的一种估计方法

目的提出一种适用于非随机化医学研究的,结合倾向指数与非参数生存分析估计风险比的方法.方法首先对倾向指数进行估计,然后对倾向指数分布分层以消除比较两组间协变量分布的不均衡.其次对分层样本用非参数生存分析的方法估计两组间发病或死亡的风险比.最后比较本法与常用的Cox模型方法并探讨其适用性.结果将本法应用于一项评价某降血脂新药效果的4期临床试验数据后显示:(1)对倾向指数分布分层后基本上消除了由于随机分组方案失败导致的新药组与传统药物组之间协变量分布的不均衡性,使得非参数生存分析方法得以应用;(2)由本法得到的新药效果的估计-风险比与由Cox模型得到的结果基本一致.结论对于非随机化医学研究,结合倾向指数进行非参数生存分析是一种新的可选择的统计方法.     

率指标非劣效性界值设定范围的探讨

目的 探讨非劣效试验研究中率指标非劣效性界值的设定问题.方法 从一个实例入手,通过理论推导与软件模拟相结合,分析不同样本量及样本率条件下非劣效性界值的变化.结果 非劣效性界值△主要由两部分组成:△(～)△E+△0,即非劣效性界值的最低要求限△0和期望检验的最大率差△E.当样本量相同时,样本率越接近0.5,则△值越大.当样本率相同时,随着样本量的增大,△值逐渐减小.结论 非劣效性试验中率指标非劣效性界值的设定应同时考虑最低要求限△0和期望检验的最大率差△E,这将为实际工作提供指导和参考.     

率的非劣效试验中样本含量的估计——SAS和PASS实现

目的 提供二分类定性资料平行设计非劣效临床试验样本含量最常用的计算公式及其 SAS和PASS过程,并为相关参数的设置提供参考。方法 基于二项分布的正态近似理论推导样本含量的估计公式,通过SAS程序和PASS过程探讨各重要参数（样本率、非劣效界值）变化时样本含量及检验效能的变化情况。结果 对率的非劣效试验样本含量的计算,公式、SAS程序和PASS过程能得到一致结果;当检验水准和对照组样本率确定时,试验组样本率越大、检验效能越小、界值越大,所需样本含量越小。结论 利用本文提供的公式、SAS程序和PASS过程,可以帮助研究者系统快速得到二分类资料2组平行非劣效设计时的样本含量。试验组样本率、检验效能和非劣效界值是非劣效临床试验估计样本含量必须认真考虑的参数。     

两冠心病诊断试剂盒双正态ROC曲线下面积及其等效性或非劣效性检验

目的 探讨在双正态假定下,应用标准化差法进行定量资料ROC曲线下面积的估计及其等效性检验或非劣效性检验,比较两氧化低密度脂蛋白试剂盒在诊断冠心病中的价值.方法 从ROC曲线的定义出发,根据模型中参数的统计学意义,完成ROC曲线的构建、曲线下面积的估计,并利用标准化差结合等效性检验、非劣效性检验原理,进行参数检验,或在Bootstrap基础上利用可信区间法得到结论.结果 两试剂盒均显示氧化低密度脂蛋白在冠心病诊断中具有较高的准确性.从非劣效性检验的结果可以看出,CHN试剂盒在冠心病诊断上非劣于已经投入临床使用的SWZ试剂盒.结论 两试剂盒具有较高的临床推广价值,且具有较高性价比的CHN试剂盒在国内临床市场有较好的前景.同时为类似问题的解决提供了方法学参考.     

基于相对度量指标的非劣效界值计算方法的比较

目的美国FDA在其非劣效临床试验指南中提供了两种基于相对度量指标的非劣效界值计算方法,一种是与绝对度量指标非劣效界值计算方法相对应的对数转换法(简称对数转换法),另外一种是直接基于相对风险的计算方法(简称直接计算法)。本文探讨了这两种方法计算结果的差异,以评估该差异对非劣效试验结果的影响。方法以风险比(hazard ratio,HR)为例,指代阳性对照药与安慰剂的疗效差异的保守估计值,从计算公式上阐述两种方法的关系,并展示两种方法在高优与低优指标中计算得到的非劣效界值的差异。结果当HR在0.80~1.25时,两种方法计算得到的非劣效界值差别在1%以内;当HR越远离1,差别越大。当取相同的效应保留比例f时,直接计算法计算出的非劣效界值总大于对数转换法,此时会导致低优指标使用直接计算法计算出的界值相对更激进,而高优指标则相对保守。当设定相同的非劣效界值δ时,对于低优指标,使用直接计算法所需设定的效应保留比例f高于对数转换法,对于高优指标则相反。结论在以相对度量指标作为主要评价指标的非劣效试验中,研究者应该认识到这两种计算方法对非劣效界值设定和试验结论的影响,综合考虑临床实践和试验药物的获益-风险等,慎重选取非劣效界值。     

Newcombe-Wilson得分方法在有效率为100％非劣效临床试验中的应用

目的 探讨试验组和对照组实际有效率为100％的非劣效临床试验的设计和推断方法.方法 分别介绍获得率差的传统正态近似方法和Newcombe-Wilson得分方法的基本理论,再通过实例计算两组率为100％时的率差及合理的试验设计.结果 Newcombe-Wilson得分方法解决了两组有效率都为100％率差置信区间的计算问题;实例采用传统近似正态进行试验设计,保守估计成功率为98％,非劣效界值取10％时,每组样本为33例, 当实际成功率都为100％,两组率差点估计和95％置信区间估计为0.0％(-10.4％,10.4％),试验失败.结论 Newcombe-Wilson得分方法能够计算非劣效临床试验中试验组和对照组有效率都为100％率差的置信区间;在高成功率非劣效试验设计中还应考虑Newcombe-Wilson得分方法进行试验设计.     

Non-inferiority trials: the 'at least as good as' criterion with dichotomous data

The 'at least as good as' criterion, introduced by Laster and Johnson for a continuous response variate, is developed here for applications with dichotomous data. This approach is adaptive in nature, as the margin of non-inferiority is not taken as a fixed difference; it varies as a function of the positive control response. When the non-inferiority margin is referenced as a high fraction of the positive control response, the procedure is seen to be uniformly more efficient than the fixed margin approach, yielding smaller sample sizes when sizing non-inferiority trials under identically specified conditions. Extending this method to proportions is straightforward, but highlights special considerations in the design of non-inferiority trials versus superiority trials, including potential trade-offs in statistical efficiency and interpretability.     

On non-inferiority margin and statistical tests in active control trials

The problem of selecting a non-inferiority margin and the corresponding statistical test for non-inferiority in active control trials is considered. For selection of non-inferiority margin, the guideline by the International Conference on Harmonization (ICH) recommends that the non-inferiority margin should be chosen in such a way that if the non-inferiority of the test therapy to the active control agent is claimed, the test therapy is not only non-inferior to the active control agent, but also superior to the placebo. Furthermore, variability should be taken into account. Along this line, a method for selecting non-inferiority margins with some statistical justification is proposed. Statistical tests for non-inferiority designed in the situation where the non-inferiority margin is an unknown parameter are derived. An example concerning a cancer trail for testing non-inferiority with the primary study endpoint of the time to disease progression is presented to illustrate the proposed method.     

A hybrid Bayesian-frequentist approach to evaluate clinical trial designs for tests of superiority and non-inferiority

Specification of the study objective of superiority or non-inferiority at the design stage of a phase III clinical trial can sometimes be very difficult due to the uncertainty that surrounds the efficacy level of the experimental treatment. This uncertainty makes it tempting for investigators to design a trial that would allow testing of both superiority and non-inferiority hypotheses. However, when a conventional single-stage design is used to test both hypotheses, the sample size is based on the chosen primary objective of either superiority or non-inferiority. In this situation, the power of the test for the secondary objective can be low, which may lead to a large loss of resources. Potentially low reproducibility is another major concern for the single-stage design in phase III trials, because significant findings of confirmatory trials are required to be reproducible. In this paper, we propose a hybrid Bayesian-frequentist approach to evaluate reproducibility and power in single-stage designs for phase III trials to test both superiority and non-inferiority. The essence of the proposed approach is to express the uncertainty that surrounds the efficacy of the experimental treatment as a probability distribution. Then one can use Bayes formula with simple graphical techniques to evaluate reproducibility and power adequacy.     

Sample sizes for clinical trials with normal data

This article gives an overview of sample size calculations for parallel group and cross-over studies with Normal data. Sample size derivation is given for trials where the objective is to demonstrate: superiority, equivalence, non-inferiority, bioequivalence and estimation to a given precision, for different types I and II errors. It is demonstrated how the different trial objectives influence the null and alternative hypotheses of the trials and how these hypotheses influence the calculations. Sample size tables for the different types of trials and worked examples are given.     

On the three-arm non-inferiority trial including a placebo with a prespecified margin

Three-arm trials including the experimental treatment, an active reference treatment and a placebo are recommended in the guidelines of the ICH and EMEA/CPMP as a useful approach to the assessment of assay sensitivity. Generally, the acceptable non-inferiority margin Δ has been defined as the maximum clinically irrelevant difference between treatments in many two-arm non-inferiority trials. However, many recent articles discussing three-arm trials have considered a design with unknown Δ which is the prespecified fraction f of unknown effect size of the reference drug, where the prespecified fraction f is treated as if it were a revised margin. Therefore, these methods cannot be applied to the case where the acceptable non-inferiority margin must be a prespecified difference between treatments. In this paper, we propose a statistical test procedure for three-arm non-inferiority trials with the margin Δ defined as a prespecified difference between treatments under the situation that the primary endpoints are normally distributed with a common, but unknown, variance. In addition, we derive the optimal allocation that minimizes the total sample size. The proposed method is illustrated with data on a randomized controlled trial on major depressive disorder.     

Choice of lambda-margin and dependency of non-inferiority trials

For a two-arm active control clinical trial designed to test for non-inferiority of the test treatment compared with the active control standard treatment, data of historical studies are often utilized. For example, with a cross-trial comparison approach (also called synthetic approach or lambda-margin approach), the trial is conducted to test the hypothesis that the mean difference or the ratio between the current test product and the active control is no larger than a certain portion of the mean difference or the ratio of the active control and placebo obtained in the historical data when the positive response indicates treatment effectiveness. The regulatory agency usually requires that the clinical trials of two different test treatments are independent in most regular cases. It also requires, in general, two independent trials of the same test treatment in order to provide confirmatory evidence of the efficacy of the test product. In this article, we derived the relationship between the correlation of the test statistics of two trials with the choice of lambda (the percentage to preserve), the sample sizes and variances under the normality assumption. We showed that the smaller a lambda, the higher the correlation between the two non-inferiority tests. It is further shown that when an 80 per cent or larger lambda is used, the correlation can be controlled to be less than 10 per cent if the variances of the response variables in the current trial are not much smaller than those of the historical studies.     

A note on the conservativeness of the confidence interval approach for the selection of non-inferiority margin in the two-arm active-control trial

Compared with placebo-control clinical trials, the interpretation of efficacy results from active-control trials requires more caution. This is because efficacy results from such trials cannot be reliably interpreted without a thorough understanding of the efficacy evidence that formed the basis for the approval of the active control, especially when such drug efficacy is to be established on the basis of clinical evidence from the traditional two-arm active-control clinical equivalence studies as opposed to the multi-arm active control. This is because in addition to over-reliance on the quantification of a clinically irrelevant acceptable margin of inferiority from historical data, such interpretation also depends on cross-trial inference for demonstration of experimental drug effect. We provide a brief overview of some design issues with the traditional two-arm active-control clinical trial and discuss regulators' concern regarding Type I error rate control (with the two most popular methods for the quantification of the non-inferiority margin) in cross-trial demonstration of experimental drug effect. Simulation results are presented to show that the point estimate method provides adequate control of the Type I error rate with > or =75 per cent retention of known active-control effect and that the confidence interval approach is uniformly ultra-conservative. We also report (via a numerical example from real clinical trial data) a couple of potentially less stringent alternative approaches for establishing the non-inferiority of a test drug over a control, which have been used in the past to provide additional efficacy evidence in NDA submission.     

Tree-structured gatekeeping tests in clinical trials with hierarchically ordered multiple objectives

This paper discusses a new class of multiple testing procedures, tree-structured gatekeeping procedures, with clinical trial applications. These procedures arise in clinical trials with hierarchically ordered multiple objectives, for example, in the context of multiple dose-control tests with logical restrictions or analysis of multiple endpoints. The proposed approach is based on the principle of closed testing and generalizes the serial and parallel gatekeeping approaches developed by Westfall and Krishen (J. Statist. Planning Infer. 2001; 99:25-41) and Dmitrienko et al. (Statist. Med. 2003; 22:2387-2400). The proposed testing methodology is illustrated using a clinical trial with multiple endpoints (primary, secondary and tertiary) and multiple objectives (superiority and non-inferiority testing) as well as a dose-finding trial with multiple endpoints.     

校正中心效应的非劣性检验

目的解决校正中心效应的非劣性检验的问题。方法借助多重回归中的回归系数及其标准误表达式,试图得到校正中心效应的非劣性检验统计量。结果导出校正中心效应的非劣性检验的方法,并且可以借助多重回归校正中心效应的部分结果进行非劣性检验。并用实例说明了具体的计算步骤。结论可以用本文所介绍统计方法进行校正中心效应的非劣性检验。