图示法在多重性问题中的应用及KeyNote-048案例分析

控制总I类错误是监管机构审查药物临床试验的基本标准,而实际临床研究中普遍存在多重性问题导致I类错误的膨胀。图示法是一种使用图形方式表现的多重性检验程序,其可以直观展示不同假设检验间的相关性和Ⅰ类错误的重新分配策略,并可以融合多种多重性调整方法以应对复杂临床试验。KeyNote-048是一项帕博利珠单抗治疗晚期头颈癌患者的随机对照研究,其包含多个临床终点、多组间比较、亚组分析和期中分析共4种多重性问题。通过图示法,KeyNote-048展示了14个假设检验的相关性和预设Ⅰ类错误,并根据α重新分配策略设定不同情况下假设检验的检验水平。最后本文讨论了实际应用中图示法应注意的问题。     

关于肿瘤化疗辅助中药新药临床疗效指标的研究与分析

通过对我科20年来16种化疗减毒增效药新药临床试验中临床疗效指标设置、试验数据及统计分析结果的回顾性分析,探讨新药临床试验疗效评价中疗效指标的特点、不足和变化趋势,分析并提出进一步完善肿瘤化疗辅助中药新药疗效评价指标和体系的几点建议.     

基于有效性及伦理学原则探讨新药Ⅱ、Ⅲ期临床试验的设计思路与方法

基于新药临床研究中选择阳性药物为对照存在可能无法真实评价新药疗效的可能性,以及新药临床试验本身存在的广义伦理学问题,提出新药临床研究过程中,至少应在Ⅱ期选择安慰剂作为对照组,视研究目标疾病情况可以选择病情较轻的受试者参加试验,在首先验证新药的绝对有效性的基础上安排Ⅱ期二阶段和Ⅲ期临床试验:Ⅱ期二阶段可以采用add-on研究模式,适当扩大适应征;Ⅲ期临床试验可以选择安慰剂,也可以选择合适的阳性药物作为对照组,从而既保证了新药的绝对有效性,又使新药临床试验更加符合伦理学原则。     

多中心临床试验的研究者培训*

研究者培训是多中心临床试验实施过程中质量控制的重要环节，合格的研究者是保证临床试验质量的关键。上市药品的大样本多中心临床试验与新药临床试验不同，研究者培训由课题领导小组统一负责，由于协作单位多、观察周期长、方法学要求高、主要评价终点事件的发生情况，方案实施起来难度较大，而且协作单位并非都是国家批准的药品临床试验研究基地，因此必须采取有针对性的措施，进行有效的研究者培训，保证多中心临床试验遵循GCP和试验方案的要求实施。     

临床试验方案设计中主要疗效指标及试验时间的考虑要素

新药上市前,需要进行临床试验,以便对药物的疗效、安全性及风险效益比作出评价。科学合理的临床试验方案是评价的基础;在制定试验方案时,必须考虑到一些重要因素。为此,本文对主要疗效指标和试验时间这2个重要因素进行阐述。     

新药临床试验中药物临床价值的考虑

新药上市的目的是为满足临床需求、实现临床价值.疗效是药物临床价值和临床意义的核心体现.因此,在新药研发临床试验的设计与疗效评价中,要充分重视临床价值评估.本文从药物临床试验目的和临床定位的确定、临床试验比较类型选择及相关界值的确定、临床疗效指标的选择和使用等多方面进行了分析和讨论,强调在新药临床试验中,需要重视药物临床...     

关于新药临床试验中控制混杂因素的几点意见

目的:通过探讨新药临床试验总结分析阶段几种控制混杂因素的统计方法,强调控制混杂因素对于新药临床试验的重要性.方法:通过实例说明几种方法的用途及注意事项,阐述各种方法控制中心效应、协变量等对主要结果指标的影响.结果与结论:针对资料的特点,选用适合的统计学方法控制混杂因素对主要结果指标分析的影响,尽可能使药物疗效真实的呈现.     

电子化患者报告结局在新药临床试验疗效评价中的应用

患者报告结局（PRO）作为临床结局疗效评价最重要的指标之一,被广泛用于新药临床试验。在新药临床试验电子化的趋势下,电子化患者报告结局（ePRO）数据采集系统基于其种种可以推进新药临床评价的优势,迎来了快速发展。然而,ePRO在中国仍然处在初期发展阶段,因此将对ePRO的基本概念、功能、国内外临床应用情况及其在新药临床试验疗效评价中的价值等进行介绍;同时总结概括了目前ePRO在我国临床试验应用中存在的问题,最后对ePRO在中国的发展前景进行了探讨。     

中心效应及中心间样本不均衡对临床疗效评价的影响

目的:本文分析和探讨在多中心临床试验中,中心效应、中心与处理的交互作用以及各中心样本量不均衡对治疗效果评价的影响。方法:以二分类资料两组比较为例,采用计算机模拟试验,分别探讨各中心10种不同样本分配比例、3种中心效应时,对临床疗效的检验效能及Ⅰ类错误的影响。结果:在不存在中心效应,或有中心效应但中心与处理间无交互作用情况下,不同样本量的分配比例对检验效能的影响不大,Ⅰ类错误可控。当中心与处理间存在交互作用时,即使中心间样本量均衡,检验效能也有所下降,Ⅰ类错误亦增加,随着各中心样本均衡性变差,检验效能随之略有降低,I类错误亦随之少许增加。结论:在多中心临床试验中,若中心与处理间存在交互作用,会对疗效评价有影响,而中心间样本均衡性对结果影响较小。在临床试验设计时应给予高度重视。     

ADR咨询

系列问答81—我国新药临床试验分为几期?我国新药临床试验过去分为3期,但近年己和国际接轨分为4期。1999年5月1日施行的《新药审批办法》第十二条规定”新药的临床试验分为Ⅰ、Ⅱ、Ⅲ、Ⅳ期”,其内容如下Ⅰ期临床试验初步的临床药理学及人体安全性评价试验。观察人体对于新药的耐受程度和药物代谢动力学,为制定给药方案提供依据Ⅱ期临床试验随机盲法对照临床试验。对新药有效性及安全性作出初步评价,推荐临床给药剂量Ⅲ期临床试验扩大的多中心临床试验。应遵循随机对照原则,进一步评价有效性、安全性Ⅳ期临床试验新药上市后监测。在广泛使用…     

Design of Vaccine Equivalence/Non-Inferiority Trials with Correlated Multiple Binomial Endpoints

Immunogenicity trials that study the immune responses to vaccination are often used in the vaccine development process as alternatives to clinical efficacy trials. The comparisons of immune responses among various treatment groups are conducted in a non-inferiority or equivalence framework. When there exists a level of immune response that correlates with protection against disease, it is of interest to compare the proportion of responders as defined as response above a specific level or as a predefined increase in immune levels for post-vaccination levels above pre-vaccination levels. Since vaccines often contain several antigens, the correlations between the immune responses need to be taken into account in the analysis. In this paper, we describe appropriate testing methods for demonstrating the non-inferiority/equivalence of two treatments on each of the binomial endpoints. We conduct a comprehensive simulation study to shed light on how the Type I error and power are affected and to what extent when correlated multiple binomial endpoints are present in the vaccine trials. We also illustrate the computation of power for assessment of non-inferiority/equivalence in real studies.     

Design of vaccine equivalence/non-inferiority trials with correlated multiple binomial endpoints

Immunogenicity trials that study the immune responses to vaccination are often used in the vaccine development process as alternatives to clinical efficacy trials. The comparisons of immune responses among various treatment groups are conducted in a non-inferiority or equivalence framework. When there exists a level of immune response that correlates with protection against disease, it is of interest to compare the proportion of responders as defined as response above a specific level or as a predefined increase in immune levels for post-vaccination levels above pre-vaccination levels. Since vaccines often contain several antigens, the correlations between the immune responses need to be taken into account in the analysis. In this paper, we describe appropriate testing methods for demonstrating the non-inferioritylequivalence of two treatments on each of the binomial endpoints. We conduct a comprehensive simulation study to shed light on how the Type I error and power are affected and to what extent when correlated multiple binomial endpoints are present in the vaccine trials. We also illustrate the computation of power for assessment of non-inferioritylequivalence in real studies.     

A Consistency-Adjusted Strategy for Accommodating an Underpowered Primary Endpoint

A clinical trial might involve more than one clinically important endpoint, each of which can characterize the treatment effect of the experimental drug under investigation. Underlying the concept of using such endpoints interchangeably to establish an efficacy claim, or pooling different endpoints to constitute a composite endpoint, is the assumption that findings from such endpoints are consistent with each other. While such an assumption about consistency of efficacy findings appears to be intuitive, it is seldom considered in the design and analysis literature of clinical trials with multiple endpoints. Failure to account for consistency of efficacy findings of two candidate endpoints to establish efficacy, at the design stage, has led to difficulties in interpreting study findings. This article presents a flexible testing strategy for accommodating findings of an alternative to the designated primary endpoint (or a subgroup) to support an efficacy claim. The method is built on the following two premises: (i) Efficacy findings of the designated primary endpoint, although nonsignificant, need to be supportive of those of the alternative endpoint, and (ii) the significance level allocated for testing the second endpoint is determined adaptively based on the magnitude of the p-value for the designated primary endpoint. The method takes into account the hierarchical ordering of the hypotheses tested and the correlation between the test statistics for the two endpoints to increase the chance of a positive trial. We discuss control of the type I error rate for the proposed test strategy and compare its power with that of other methods. In addition, we consider its application to two clinical trials.     

A consistency-adjusted strategy for accommodating an underpowered primary endpoint

A clinical trial might involve more than one clinically important endpoint, each of which can characterize the treatment effect of the experimental drug under investigation. Underlying the concept of using such endpoints interchangeably to establish an efficacy claim, or pooling different endpoints to constitute a composite endpoint, is the assumption that findings from such endpoints are consistent with each other. While such an assumption about consistency of efficacy findings appears to be intuitive, it is seldom considered in the design and analysis literature of clinical trials with multiple endpoints. Failure to account for consistency of efficacy findings of two candidate endpoints to establish efficacy, at the design stage, has led to difficulties in interpreting study findings. This article presents a flexible testing strategy for accommodating findings of an alternative to the designated primary endpoint (or a subgroup) to support an efficacy claim. The method is built on the following two premises: (i) Efficacy findings of the designated primary endpoint, although nonsignificant, need to be supportive of those of the alternative endpoint, and (ii) the significance level allocated for testing the second endpoint is determined adaptively based on the magnitude of the p-value for the designated primary endpoint. The method takes into account the hierarchical ordering of the hypotheses tested and the correlation between the test statistics for the two endpoints to increase the chance of a positive trial. We discuss control of the type I error rate for the proposed test strategy and compare its power with that of other methods. In addition, we consider its application to two clinical trials.     

Multiplicity Issues in Clinical Trials With Multiple Objectives

Modern clinical trials for evaluating efficacy and safety of new treatments frequently include multiple objectives with questions of varying clinical importance. Answering them generally requires performing a number of statistical tests and analyses which raise multiplicity of tests issues. These issues can be complex and multidimensional in nature. For example, one dimension may relate to the assessment of the effects of the treatment on multiple endpoints, the other to the effects of multiple doses of the treatment, and yet another to the type of the tests (e.g., superiority or noninferiority-type tests). Also, the trial may seek claims for treatment benefits either for the total patient population or for targeted subgroups. In addition, there may be interest in finding whether or not a certain consistency of results persists across certain multiple endpoints; in some situations this may be measuring different but critical features of a disorder, or in other situations may be measuring the same underlying pathophysiology of the disorder. Addressing such problems in clinical trials for the purpose of controlling the Type I error rate requires the use of advanced statistical test strategies and methods, some of which have only recently appeared. Actually, the last decade has witnessed a number of novel methods as well as innovative extensions of old methods for addressing complex multiplicity problems in clinical trials. The main purpose of this article is to present—at the conceptual level—how multiplicity issues of confirmatory clinical trials that include multiple objectives can be addressed by using some of these new statistical methods that use α-propagation and gatekeeping concepts. One additional goal of this contribution is to address some issues that often arise in the use of coprimary and composite endpoints in clinical trials.     

Multiple comparisons in non-inferiority trials: Reaction to recent regulatory guidance on multiple endpoints in clinical trials

ABSTRACT

We consider analysis of active control, non-inferiority clinical trials with multiple primary endpoints for assessing efficacy of an investigational treatment. Many of the issues with multiple endpoints for non-inferiority trials are similar to issues for superiority trials, but there are important differences. Because non-inferiority trials typically make decisions with confidence interval bounds instead of p-values, care must be taken in adjusting for multiple hypotheses. Composite endpoints are more difficult to interpret in non-inferiority trials due to difficulties in indirectly comparing the investigational treatment to placebo on each component. Otherwise many of the methods used in superiority trials (including sequential testing, graphical procedures and gatekeeping procedures) can be applied to non-inferiority trials with a little additional care. We focus on the differences between non-inferiority and superiority trials to provide guidance on application of recent regulatory guidance to non-inferiority trials.     

Practical Recommendations for Regional Consistency Evaluation in Multi-Regional Clinical Trials with Different Endpoints

In recent years, there is an increasing trend to conduct multi-regional clinical trials (MRCT) for drug development in pharmaceutical industry. A carefully designed MRCT could be used in supporting the new drug's approval in different regions simultaneously. The primary objective of an MRCT is to investigate the drug's overall efficacy across regions while also assessing the drug's performance in some specific regions. In order to claim the study drug's efficacy and get drug approval in some specific region(s), the local regulatory authority may require the sponsors to provide evidence of consistency in the treatment effect between the overall patient population and the local region. Usually, the regional specific consistency requirement needs to be pre-specified before the study conduct and the consistency in treatment effect between the region(s) of interest and overall population will be evaluated at the final analysis. In this article, we evaluate the consistency requirements in multi-regional clinical trials for different endpoints, that is, continuous, binary, and survival endpoints. We also compare the different consistency requirements of the same endpoint/measurement if multiple consistency requirements are enforced and our recommendations for each endpoint/measurement will be made based on the comprehensive consideration.     

Association between health-related quality of life and clinical efficacy endpoints in rheumatoid arthritis patients after four weeks treatment with anti-inflammatory agents

BACKGROUND: Improvement of health-related quality of life (QoL) is increasingly recognized as a maj or treatment goal for patients with rheumatoid arthritis (RA). There are several measures of general health status and of physical functioning for assessing treatment effects on QoL in patients with RA, however, the relationship between QoL outcomes and conventional clinical efficacy endpoints is not completely understood. OBJECTIVE: To describe the association between changes in QoL and changes in other efficacy measures, among patients with RA after four weeks of treatment with etoricoxib, naproxen or placebo, and to explore differences in the association of changes in efficacy and changes in QoL parameters across treatment groups. METHODS: The study used data from 1684 patients with RA enrolled in two identical clinical trials (one US and one multinational). Patients were randomized to placebo, etoricoxib 90 mg once daily, or naproxen 500 mg twice daily in a 2 : 2: 1 allocation ratio. Primary efficacy endpoints were tender joint count, swollen joint count, patient global assessment of disease activity (100 mm VAS), and investigator global assessment of disease activity (0 - 4 Likert scale). QoL assessments were based on the Health Assessment Questionnaire (HAQ) and the Medical Outcomes Survey Short Form 36 (SF-36). Mean differences between baseline and week four were calculated for each parameter studied. Linear regression analysis was performed to assess the association between changes in clinical efficacy and changes in QoL parameters, adjusted for covariates. RESULTS: The degree of association between changes in tender or swollen joint counts and changes in QoL variables was low, explaining less than 10% of the variability for most QoL variables, except bodily pain (SF-36). In contrast, changes in patient global assessment of disease activity explained 33% of the variability in the overall HAQ score, and in the physical component score (SF-36; adjusted regression models). Values for investigator global assessment of disease activity were below those for patient global assessment but above joint count measures. Results were similar between the etoricoxib, naproxen and placebo groups in the degree of association between changes in efficacy and QoL variables. CONCLUSION: Currently used efficacy endpoints are less than ideal predictors of change in QoL. There is no evidence from this study that the association between changes in CE endpoints and QoL was different across treatments. Our results highlight the need to assess both conventional efficacy measures and QoL in clinical trials of RA treatments.