临床试验亚组分析结果可靠性评价及监管决策影响

精准医学是全球医学科技发展的前沿方向,根据内外因素将患者分成不同亚组开展针对性的亚组分析,以评价不同特征人群处理效应差异,在临床研究中越来越常见.根据不同目的,亚组分析可以分为确证性亚组分析、支持性亚组分析和探索性亚组分析3种类型.亚组分析结果已成为监管部门决策适应证能否批准及批准范围的重要依据,也是申办方制定临床开发...     

再评洋地黄在心血管疾病中的临床应用

最近，通过对DIG试验的主体研究、次要研究、亚组分析，及其他涉及心衰研究的再分析和基础研究的进展等，大家对洋地黄在心血管疾病中的应用又有了新的认识，进一步肯定了这个在临床使用了228年的老药在心衰治疗中的价值。     

蜂蜜防治便秘的疗效观察

目的观察蜂蜜对功能性便秘和心肌梗死、脑卒中患者便秘的防治作用。方法将120例便秘患者分为3组，第一组功能性便秘60例，第二组心肌梗死20例，第三组脑卒中40例，每组随机分为试验亚组和对照亚组。对照亚组给予常规的内科护理及饮食指导，试验亚组在常规基础上每天晨空腹给予蜂蜜一汤匙20～25g，用凉开水或温水100—200ml送服或放入杯中饮用，连服14d，记录患者每天的排便情况，便秘的诊断依据罗马Ⅱ标准。结果对照亚组便秘的发生率分别为70％、52％、46％；试验亚组便秘的发生率分别为13．3％、20％、20％，2组对比差异有统计学意义（P〈0．05）。结论蜂蜜对功能性便秘和心肌梗死、脑卒中患者的便秘有良好的防治作用，能有效的防治并发症的发生，且食用方便、安全、无不良反应，可以长期使用。     

药物流行病学研究中亚组分析和效应修饰的探讨

摘要：随着全民健康事业推进和《中国药物流行病学研究方法学指南》发布,我国药物流行病学研究进展迅速。恰当运用亚组分析和效应修饰的方法可提高药物流行病学研究质量,促进合理用药,但在应用过程中存在诸多问题。本文从亚组分析和效应修饰的概述、处理方法、实例分析、注意事项等方面,初步探讨亚组分析和效应修饰在药物流行病学研究中的应用,以便正确理解和恰当运用亚组分析和效应修饰,为药物流行病学研究方法学的完善提供理论支持和参考依据。     

Ixabepilone用于治疗ER-和HER2＋乳腺癌

根据2007年乳腺癌专题讨论会上两项亚组研究成果，联合使用Ixempra（ixabepilone）（I）与卡培他滨（capecitabine）（Ⅱ），对以前用葸环类和taxane类治疗过的ER-和HER2＋的转移性乳腺癌患者具有疗效。事先设定的两个亚组分析在一项ER-或HER＋乳腺癌患者的Ⅲ期临床试验中评价了使用（I）加（Ⅱ）与单独使用（Ⅱ）的资料。     

阿哌沙班Ⅲ期临床试验结果与华法林一致

百时美施贵宝公司和辉瑞公司宣布了阿哌沙班（apixaban）Ⅲ期临床ARISTOTLE试验的进一步亚组分析资料。该试验是阳性药对照、随机、双盲、多国的Ⅲ期临床研究，为证实阿哌沙班与华法林预防卒中或全身性血栓的疗效和安全性。     

双膦酸盐临床试验最新进展——2011年SABCS会议速递

2010年,SABCS最大的新闻之一就是AZURE试验。试验结果表明唑来膦酸对乳腺癌复发和整体存活率无影响。然而,亚组分析显示,对绝经后妇女的（绝经后5年以上）复发和生存有显著效果,但对绝经前妇女无影响^[1]。     

溴己新不良反应的Meta分析

目的初步评价国内溴己新临床应用的安全性。方法检索中国知网、维普、万方数据库,纳入有关溴己新随机对照实验(RTC)的临床研究,采用Rev Man5.1软件,对溴己新的不良反应进行Meta分析,并按照PRISMA指南,对不同剂型(注射剂和片剂)、不同适应证(肺炎和支气管炎)、不同对照措施(常规治疗和其他药物治疗)的不良反应发生率进行亚组分析。结果符合纳入标准的随机对照实验有10个,涉及到临床病例1987例。Meta分析结果显示,溴己新不良反应发生率高于对照组,合并OR=4.42,95%CI[2.55,7.66],P<0.00001,差异有统计学意义。这一差异同样存在于各亚组分析中,注射剂亚组OR=4.41,95%CI[2.35,8.28];片剂亚组OR=4.44,95%CI[1.44,13.68];肺炎亚组OR=4.41,95%CI[3.25,8.28],P<0.00001;支气管炎亚组OR=4.1,95%CI[1.22,13.79],P=0.02;常规治疗组OR=5.92,95%CI[2.71,12.91],P<0.00001;其他药物治疗组OR=2.96,95%CI[1.34,6.53],P=0.007,均具有统计学差异。结论溴己新的不良反应发生率高于对照组,临床应用需要注意,但这个结果还需要更多高质量的随机对照试验进行验证。     

恶性肿瘤临床试验中短期中间指标与终点指标相关性Meta分析的系统评价

目的:对肿瘤临床试验中短期中间指标[客观缓解率(ORR)、完全缓解(CR)等]与终点指标[无进展生存期(PFS)、总生存期(OS)]相关性研究的Meta分析进行系统评价，以探讨不同肿瘤领域下短期中间指标与终点指标的相关性。方法:在Cooper等(2020)研究基础上，更新检索2019年3月—2022年1月Medline, Embase, Cochrane, Web of science和CINAHL数据库，系统评价短期中间指标与终点指标的个体水平与试验水平相关性分析的Meta分析，并对研究数量最多的3种肿瘤的研究进行亚组分析。结果:共纳入77篇文献，分析了20种不同的肿瘤，其中非小细胞肺癌、结直肠癌和乳腺癌是3种最常见的肿瘤类型。相关性分析结果显示，纳入的研究主要分析了ORR与OS(91%)、ORR与PFS(35%)之间的关系，无论是在个体水平还是试验水平上，ORR等短期中间指标与PFS和OS均无明显的相关性。亚组分析结果显示，在非小细胞肺癌领域，超过一半的研究显示ORR与PFS在试验水平上的相关性R²>0.4,两者具有较好或很好的相关性。且在非小细胞肺癌和...     

脂蛋白脂酶基因rs328多态性与汉族人脑梗死的相关性研究

目的探讨脂蛋白脂酶(LPL)基因rs328多态性与汉族人脑梗死的相关性。方法选择150例汉族脑梗死患者,按类肝素药物治疗急性缺血性脑卒中试验(TOAST)分型标准,分为大动脉粥样硬化(LAA)亚组和小动脉闭塞(SAO)亚组,并选择160名健康体检人群为对照组。以rs328位点为遗传标记,应用聚合酶链反应分析LPL基因rs328多态性与脑梗死的相关性。结果 1脑梗死组基因型频率与对照组相比,差异无统计学意义(P>0.05)。LAA亚组基因型频率与对照组相比,差异有统计学意义(P<0.05)。2LAA亚组rs328位点G等位基因频率为12.50%,与对照组的6.56%比较,LAA亚组明显增高[P<0.05,OR=2.034,95%CI(1.183,2.485)]。SAO亚组rs328位点等位基因频率与对照组差异无统计学意义(P>0.05)。结论 LPL rs328位点基因多态性与汉族人脑梗死的发病相关,G等位基因携带者患大动脉粥样硬化性脑梗死的风险高于非携带者。     

General guidance on exploratory and confirmatory subgroup analysis in late-stage clinical trials

Abstract

This article focuses on a broad class of statistical and clinical considerations related to the assessment of treatment effects across patient subgroups in late-stage clinical trials. This article begins with a comprehensive review of clinical trial literature and regulatory guidelines to help define scientifically sound approaches to evaluating subgroup effects in clinical trials. All commonly used types of subgroup analysis are considered in the article, including different variations of prospectively defined and post-hoc subgroup investigations. In the context of confirmatory subgroup analysis, key design and analysis options are presented, which includes conventional and innovative trial designs that support multi-population tailoring approaches. A detailed summary of exploratory subgroup analysis (with the purpose of either consistency assessment or subgroup identification) is also provided. The article promotes a more disciplined approach to post-hoc subgroup identification and formulates key principles that support reliable evaluation of subgroup effects in this setting.     

Statistical Considerations on Subgroup Analysis in Clinical Trials

The objective of subgroup analysis of a clinical trial is to investigate consistency or heterogeneity of the treatment effect across subgroups, defined based on background characteristics. As such, subgroup analysis plays an essential role in the interpretation of the clinical trial findings. Consistency of treatment effect across trial subgroups indicates that the average treatment effect is in general applicable regardless of the specific background characteristics. Substantial heterogeneity in treatment effect may be indicative that treatment benefit pertains only to a subset of the population. However, heterogeneity in the observed treatment effect across subgroups can arise due to chance as a result of partitioning the population into several subgroups. Furthermore, as it is known, clinical trials are generally not powered for detecting heterogeneity, thus statistical tests may miss detection of existing heterogeneity due to low power. In this article, we aim to: (i) outline the major issues underlying subgroup analysis in clinical trials and provide general statistical guidance for interpretation of its findings, (ii) provide statistical perspectives on the design and analysis of a clinical trial that aims for establishing efficacy in a targeted subgroup along with that of its overall population, and (iii) highlight some of the underlying assumptions and issues relevant to Bayesian subgroup analysis, subgroup considerations for noninferiority trials, personalized medicine, subgroup misclassification, and finally, subgroup analysis for safety assessment.     

Issues related to subgroup analysis in clinical trials

Interpretation of subgroup findings is a difficult task. The attempt of this article is to clarify confusions on subgroup analysis and to give some practical suggestions on how to avoid mistakes in interpreting subgroup outcome. We believe that the correct interpretation of subgroup findings is closely related to the intrinsic statistical property and validity of the subgroup analysis. A systematic discussion on subgroup analysis from a statistical point of view will be helpful to clinical trial practitioners.     

Multiplicity issues in exploratory subgroup analysis

ABSTRACT

The general topic of subgroup identification has attracted much attention in the clinical trial literature due to its important role in the development of tailored therapies and personalized medicine. Subgroup search methods are commonly used in late-phase clinical trials to identify subsets of the trial population with certain desirable characteristics. Post-hoc or exploratory subgroup exploration has been criticized for being extremely unreliable. Principled approaches to exploratory subgroup analysis based on recent advances in machine learning and data mining have been developed to address this criticism. These approaches emphasize fundamental statistical principles, including the importance of performing multiplicity adjustments to account for selection bias inherent in subgroup search.

This article provides a detailed review of multiplicity issues arising in exploratory subgroup analysis. Multiplicity corrections in the context of principled subgroup search will be illustrated using the family of SIDES (subgroup identification based on differential effect search) methods. A case study based on a Phase III oncology trial will be presented to discuss the details of subgroup search algorithms with resampling-based multiplicity adjustment procedures.     

Design and Analysis Considerations in Clinical Trials With a Sensitive Subpopulation

This article deals with clinical trials with a sensitive subpopulation of patients, that is, a subgroup that is more likely to benefit from the treatment than the overall population. Given a sensitive subgroup defined by a prespecified classifier, for example, a clinical marker or pharmacogenomic marker, the trial’s outcome is declared positive if the treatment effect is established in the overall population or in the subgroup. We provide a summary of key considerations in clinical trials with a sensitive subgroup, including multiplicity and enrichment adjustments as well as optimality considerations in the analysis strategy. The methodology proposed in this article is illustrated using a neuroscience clinical trial and its operating characteristics are assessed via a simulation study.     

4种治疗支气管扩张合并感染方案的最小成本分析

目的: 考察4种治疗方案对支气管扩张合并感染的临床疗效与药物经济学相关指标,为临床合理用药提供参考。方法: 收集莆田学院附属医院2013年1月—2014年1月之间符合支气管扩张合并感染诊断标准的患者,釆用前瞻性随机对照开放临床试验设计的方法,按照有、无铜绿假单胞菌(PA)感染高危因素将受试者分为A、B两大组,A组患者不存在PA感染高危因素,B组患者存在PA感染高危因素。A组患者随机给予头孢哌酮舒巴坦(ACS亚组)和美洛西林舒巴坦(AMS亚组)治疗;B组患者随机给予头孢哌酮舒巴坦+阿米卡星(BCA亚组)、头孢哌酮舒巴坦(BCS亚组)、美洛西林舒巴坦+阿米卡星(BMA亚组)及美洛西林舒巴坦(BMS亚组)治疗。分别从临床症状、相关实验室指标、病原学检测结果三方面考察各种治疗方案的临床疗效,并进行药物经济学分析。结果: (1)A组中ACS与AMS 2个亚组的临床有效率分别为84.0%和77.42%(P>0.05),AMS亚组总成本低于ACS亚组总成本(P<0.05),但疗程较长(P<0.05)。(2)B组中BCA、BCS、BMA和BMS 4个亚组的临床有效率分别为84.21%,81.82%,80.00%和77.27%(P>0.05),其中BMS亚组的疗程最长(P<0.05),其余3个亚组组间差异无统计学意义(P>0.05)。BMA亚组的总成本最低(P<0.05),其余3个亚组组间差异无统计学意义(P>0.05)。(3)经敏感性分析,A、B两组的药物经济学分析结果稳定。结论: (1)治疗支气管扩张合并感染患者无铜绿假单胞菌感染高危因素的患者,美洛西林舒巴坦治疗方案较头孢哌酮舒巴坦治疗方案更为经济,但疗程较长。(2)治疗支气管扩张合并感染患者伴有铜绿假单胞菌感染高危因素的患者,美洛西林舒巴坦治疗方案的疗程最长,美洛西林舒巴坦+阿米卡星治疗方案最为经济。     

Practical Considerations for Subgroups Quantification,Selection and Adaptive Enrichment in Confirmatory Trials

Abstract

Subgroup analyses (e.g., baseline information, biomarkers measurements) are commonly encountered and conducted in confirmatory clinical trials to ensure the risk–benefit consistency and appropriate interpretation of the study results. However, there are natural methodological complications that come with multiple analyses which can result in incorrect scientific or regulatory conclusions for subgroup analysis. Typical issues that may arise include, but are not limited to (a) How to make sure subgroup results are reliable and reproducible? (b) How to quantify subgroup reversal effects which may be due to chance finding, or a lack of power in subgroup and/or treatment interaction tests? (c) How to design efficient trials to establish treatment effect in subgroups or/and overall population with proper Type I error control? These are challenges statisticians and clinical trialist regularly face in the drug development process. In this article, we discuss and present different approaches corresponding to these general issues of subgroup analysis, and their impacts and implications on the interpretation of clinical trials as well as the innovations and opportunities in the era of precision medicine. We also conduct simulations to illustrate the operating characteristics of different methodological tools and designs, along with further practical recommendations and guidance from a statistical perspective.     

Subgroup analyses in randomized clinical trials: statistical and regulatory issues

Recently, two CPMP Points to Consider, one on adjustment for baseline covariates and the other on multiplicity issues in clinical trials, have included recommendations on the use of subgroup analysis for regulatory purposes. However, despite their regular use and regulatory attention, the validity and nature of subgroup analyses are still frequently questioned. This article provides guidance on when subgroup analyses can be done, when they should be done, and their interpretation. The validity of common regulatory claims based on subgroup analyses is then discussed.     

Adaptive Designs for Confirmatory Clinical Trials with Subgroup Selection

Growing interest in stratified medicine is leading to increasing importance of subgroup analyses in confirmatory clinical trials. Conventionally, confirmatory clinical trials either focus on a subgroup identified in advance or assess subgroup effects once the trial is completed. The focus of this article is methodology for adaptive clinical trials that both identify whether a treatment is particularly effective in a predefined subgroup, potentially enabling alteration of recruitment, and assess the effectiveness in the subgroup and/or whole population. Methods for such adaptive trials are described and compared, and the logistical and regulatory issues associated with such approaches are discussed.     

Population enrichment designs: case study of a large multinational trial

A method is proposed for modifying a group-sequential clinical trial by restricting future enrollment to a subgroup and possibly altering the sample size of the subgroup, based on an interim analysis of the data already obtained. The method provides strong control of type 1 error without requiring prespecification of the list of possible subgroups or of the decision rule for selecting among them. Nevertheless, for regulatory submissions it is recommended that the subgroups and decision rule be prespecified. The method is applied to a large cardiology trial in which the subgroups are prespecified and the decision rules for subgroup selection and sample size alteration are based on conditional power. It is shown by simulation that substantial gains in power can be attained if there is a subgroup by treatment interaction.