临床干预措施效力-效果差距弥合的方法学研究进展（二）：增强临床试验结果的外推性

目的 随机对照试验（RCT）通常具有严格的实施标准,纳入的研究对象特征以及干预实施条件与真实临床环境具有较大差异,这会导致干预措施在实际临床应用中的风险-效益与RCT中表现出的风险-效益存在差异, 结果的外推性受到很大限制。因此需要一些方法增强RCT结果的外推性,以评估药物在真实人群和真实临床实践环境中的真实效果。方法 检索PubMed、Embase、Web of Science、万方数据知识服务平台、维普数据库、中国知网6个数据库从建库至2022年12月31日的中英文文献。采用概括性综述的方法,对纳入文献进行归纳整合和定性描述。结果 共纳入12篇文献。纳入文献中增强效力外推性的方法可以归纳为3类：①改善传统RCT设计,增强人群代表性;②将RCT数据与真实世界数据（RWD）结合分析;③根据真实世界患者特征,校准RCT结果。结论 改进RCT设计,增强人群代表性,可提高RCT结果的外推性;将RCT数据与RWD结合分析,可发挥不同来源数据的优势;根据真实世界人群特征校准RCT结果,可预估干预措施在真实世界患者群体中的效果。     

目标仿真试验的基本原理、设计要素及其优缺点

队列研究往往具有研究对象代表性好、样本量大、随访时间长等独特优势,但混杂因素控制困难是其因果推断不够严密的主要障碍。相反随机对照试验（RCT）研究则在混杂因素的控制上有着绝对的优势,但其组织实施往往会受到人力、物力和伦理等方面的限制。近年来,随着真实世界队列研究数据的积累,在大型队列研究中遵照RCT研究的设计原则开展目标仿真试验（ETT）愈发受到关注。基于队列数据的ETT研究能够得到较为准确的研究结论,也为真实世界队列数据分析提供了新的思路。本文旨在介绍ETT研究的基本原理、设计要素和优缺点,以期为医学研究者开展ETT提供参考。     

真实世界证据与随机对照试验：RCT DUPLICATE项目启动、实施、进展解读与启示（一）

近些年,医疗产品监管机构开始重新审视真实世界证据（RWE）对监管决策的潜在价值。RWE能否代替金标准随机对照试验（RCT）产生的证据尚不确定。哈佛大学研究团队于2018年发起了RCT DUPLICATE项目,旨在利用医疗索赔数据库模拟30个RCT,以探索效力-效果差距的量化方法并解释其潜在来源,增强RWE的可信度。本文回顾了RCT DUPLICATE项目的产生背景,重点介绍RCT DUPLICATE项目的研究目的、研究设计和实施流程,以期帮助国内学者更好地理解RWE的适用范围和应用价值。     

临床干预措施效力-效果差距弥合的方法学研究进展（一）：改善真实世界研究的效果估计

目的 干预措施在临床实践中的实际干预效果与随机对照试验（RCT）中表现的效力存在差异，即效力-效果差距。RCT结果与真实世界研究（RWS）结果的差异可能无法代表真实的效力-效果差距，这是因为当RWS与RCT在研究设计上有较大差异，或RWS结果估计存在偏倚时，效力-效果的估计可能是有偏的。其次，当发现干预措施存在效力-效果差距，不能对所有患者实行一刀切的临床决策，而需要进一步评估影响干预措施效果的真实世界因素，识别可能取得期望效用的患者群体。方法 检索PubMed、Embase、Web of Science、万方数据知识服务平台、维普数据库、中国知网6个数据库从建库至2022年12月31日的中英文文献，采用概括性综述的方法，对如何改进RWS设计从而弥合效力-效果差距的方法进行归纳整合和定性描述。结果 共纳入10篇文献，探讨如何以RCT研究方案为模板，制定相应的RWS方案，在正确估计效力-效果差距的基础上，进一步评估干预措施在患者亚群中的效果，选取能获得预期收益风险比的患者亚群，从而弥合效力-效果差距。结论 使用医疗大数据，模拟目标试验方案关键特征，可以提高研究结果的真实性和有效性，弥合效力-效果差距。     

临床真实世界研究中的实验性研究设计

真实世界研究作为解释性随机对照试验在医疗实践中评价干预措施效果的进一步验证和补充已成为医疗卫生领域关注的焦点。但是也存在错误将真实世界研究等同于观察性研究,认为真实世界研究不能实施人为干预,更不能采取随机化。实际上,真实世界研究的基本设计既可以是观察性的,也可以是实验性的。其中真实世界研究的实验性研究设计主要是指实用性随机对照试验和基于注册登记研究的随机对照试验,也可采用非随机对照、自适应设计等其他研究设计方案。     

孟德尔随机化模型及其规范化应用的统计学共识

正随机对照试验(randomized controlled trial, RCT)是评价因果效应的金标准,但由于受到伦理学、受试者依从性、研究期限等因素的制约,很多情况下难以实施。另外,RCT中纳入排除标准的限制可能导致研究样本与真实世界的人群出现异质性,因此研究结论的外推性也有待验证。相比之下,观察性研究和非随机对照研究数据更易获得,在样本的选择上也更接近真实世界的情况[1]。然而,     

倾向性评分匹配法在非随机对照研究中的应用

正随机对照试验(randomized controlled trial,RCT)是最理想的金标准设计方案~([1])。但在实际工作中,由于伦理学等因素的影响以及研究设计的理想性,RCT的应用受限。而非随机对照研究(包括观察性研究和非随机试验研究)的研究对象所具有的各种特征与真实世界研究(real world study,RWS)结果更为接近,实用性更广。但由于无法随机化,如何处理混杂偏倚成为此类研究亟待解决的难题~([2])。     

临床营养研究中随机对照研究质量评价

目的评价两种主要临床营养期刊中随机对照试验（RCT）的质量。方法查阅2000～2008年《中国临床营养杂志》和《肠外与肠内营养》发表的RCT研究，按Cochrane协作网标准评价，并进行Jadad评分。结果两种期刊共发表238篇RCT研究，Jadad评分为（1．65±0．82）分。高质量RCT仅28篇（11．76％），评分为满分5分的仅5篇（2．10％）。随机分组的方法、组间可比性、纳入排除标准、盲法、撤除和退出的数量和理由、样本含量等方面存在各种问题。结论国内临床营养领域RCT研究的设计和质量控制还存在不足或欠缺，水平尚待提高。     

真实世界研究与随机对照试验、单病例随机对照试验在临床治疗性研究中的关系比较

真实世界研究、随机对照试验及单病例随机对照试验在设计及具体的实施环节上存在明显不同.随机对照试验属于新治疗措施实施前的研究,真实世界研究属于新治疗措施实施后的研究.两者不是对同一个问题的平行论证,而是承启关系.精心设计的随机对照试验是临床上任何干预措施效果评价的基础,其结果需要真实世界研究的进一步验证及拓展补充,综合考虑二者才是最佳的选择.单病例随机对照试验更易在短时间内获得一些特殊病例的信息,是随机对照试验结果的良好补充,也是一定条件下最经济的真实世界研究.临床工作及其研究是十分复杂的过程.不同个体虽患同种疾病,但临床表现互有差异,且临床反应的变化也不尽相同.因此,无法获得同一干预措施下不同个体的相同治疗效果;加之有的治疗措施缺乏真实性和实用价值,从而使得疗效评价成为一个难题.近些年来,普遍采用试验性的研究结果作为证据指导临床实践活动,其中以随机对照试验(RCT)最为受到重视,但由于RCT属于药物面市前研究,对研究对象的选择、治疗措施的应用等均有严格的限定.     

真实世界研究与随机对照试验、单病例随机对照试验在临床治疗性研究中的关系比较

真实世界研究、随机对照试验及单病例随机对照试验在设计及具体的实施环节上存在明显不同.随机对照试验属于新治疗措施实施前的研究,真实世界研究属于新治疗措施实施后的研究.两者不是对同一个问题的平行论证,而是承启关系.精心设计的随机对照试验是临床上任何干预措施效果评价的基础,其结果需要真实世界研究的进一步验证及拓展补充,综合考虑二者才是最佳的选择.单病例随机对照试验更易在短时间内获得一些特殊病例的信息,是随机对照试验结果的良好补充,也是一定条件下最经济的真实世界研究.临床工作及其研究是十分复杂的过程.不同个体虽患同种疾病,但临床表现互有差异,且临床反应的变化也不尽相同.因此,无法获得同一干预措施下不同个体的相同治疗效果;加之有的治疗措施缺乏真实性和实用价值,从而使得疗效评价成为一个难题.近些年来,普遍采用试验性的研究结果作为证据指导临床实践活动,其中以随机对照试验(RCT)最为受到重视,但由于RCT属于药物面市前研究,对研究对象的选择、治疗措施的应用等均有严格的限定.     

Logistic回归、CMH检验与Meta分析在多中心临床试验中应用探讨

目的探讨临床随机对照试验多中心效应比较的统计方法。方法以一项多中心临床随机对照试验数据为例,运用χ2检验、CMH检验、Meta分析及logistic回归分析。结果CMH检验显示各中心间效应值的一致性检验差异有统计学意义(P<0.05),扣除中心效应后,组间比较差异有统计学意义(P<0.05);Meta分析异质性检验差异无统计学意义(P>0.05),采用固定效应模型,合并后效应值组间差异有统计学意义(P<0.05),logistic回归分析,各中心效应值差异无统计学意义(P>0.05),组间效应差异有统计学意义(P<0.05),且存在可能影响效应的协变量。结论多中心临床随机对照试验研究中,如果存在分中心组间疗效差异趋势不一致时,可选择Meta分析及logistic回归分析,然后对三种分析方法的结果作出客观的评价。如果logistic回归分析存在影响效应的协变量,建议对这些协变量进行再分析。     

利用现实世界数据仿真临床试验：目标试验的发展与应用

临床试验是评价干预措施疗效和安全性的金标准，但存在花费大、耗时长等限制。现实世界数据可为比较性研究提供强大的数据基础，但研究质量参差不齐。本文介绍了仿真目标试验，其利用现实世界数据，按照临床试验的设计，事先定义暴露和结局、设立纳入排除标准、确定时间零点、估计样本量和制定统计分析计划等，以期提高观察性研究的证据等级，并初步讨论仿真目标试验的证据等级评价标准，通过案例解读仿真目标试验。     

Estimating treatment effect via simple cross design synthesis

Randomized controlled trials (RCTs) are the traditional gold standard evidence for medical decision-making. However, protocols that limit enrollment eligibility introduce selection error that severely limits a RCT's applicability to a wide range of patients. Conversely, high quality observational data can be representative of entire populations, but freedom to choose treatment can bias estimators based on this data. Cross design synthesis (CDS) is an approach to combining both RCT and observational data in a single analysis that capitalizes on the RCT's strong internal validity and the observational study's strong external validity. We proposed and assessed a simple estimator of effect size based on the CDS approach. We evaluated its properties within a formal framework of causal estimation and compared our estimator with more traditional estimators based on single sources of evidence. We show that under ideal conditions the simple CDS estimator is unbiased whenever the observational data-based estimators' treatment selection error is constant across those who are and are not eligible for RCT participation. Whereas this assumption may not often hold in practice, assumptions required for the unbiasedness of usual single-source estimators may also be implausible. We show that, under some reasonable data assumptions, our simple CDS estimator has smaller bias and better coverage than commonly used estimates based on randomized or observational studies alone.     

浅静脉留置针反向穿刺置管输液的临床观察

目的：通过浅静脉留置针反向静脉穿刺与顺向穿刺对比,寻找减轻患者痛苦提高静脉穿刺成功率的穿刺方法。方法：抽取200例住院患者随机分为试验组和对照组,对两组一次穿刺成功率进行分析。结果：留置针反向穿刺一次穿刺成功率高。结论：留置针反向穿刺能有效地利用血管,减轻病人痛苦,提高静脉穿刺成功率,减少护士工作量。     

A simulation using data from a primary care practice database closely replicated the women's health initiative trial

OBJECTIVE: In contrast to prior observational studies, hormone replacement therapy (HRT) did not prevent coronary heart disease in the Women's Health Initiative Randomized Controlled Trial (WHI RCT). To assess the validity of a novel observational study design, we compared the WHI RCT with a simulation using data from the United Kingdom General Practice Research Database (GPRD). STUDY DESIGN AND SETTING: A cohort from GPRD was used to simulate the WHI RCT by replicating, to the extent possible, all aspects of the RCT except randomization. The study included 37,730 Unexposed and 13,658 Exposed women treated with estrogen and norgestrel. RESULTS: Myocardial infarction (adjusted hazard ratio 0.95 [0.78-1.16]) was not decreased significantly in the GPRD Exposed group. Similar to the WHI RCT, stroke, venous thromboembolic events, and breast cancer were increased; and colorectal cancer was decreased. Although death appeared to decrease in the total cohort, it was unaltered in a subset of subjects without missing data on baseline covariates. CONCLUSION: A structured comparison using data from GPRD was largely concordant with the WHI RCT and did not show a cardioprotective effect of HRT. These findings further generalize the results of WHI and reinforce the potential utility of this analytic approach.     

Propensity score methods for merging observational and experimental datasets

We consider how to merge a limited amount of data from a randomized controlled trial (RCT) into a much larger set of data from an observational data base (ODB), to estimate an average causal treatment effect. Our methods are based on stratification. The strata are defined in terms of effect moderators as well as propensity scores estimated in the ODB. Data from the RCT are placed into the strata they would have occupied, had they been in the ODB instead. We assume that treatment differences are comparable in the two data sources. Our first “spiked-in” method simply inserts the RCT data into their corresponding ODB strata. We also consider a data-driven convex combination of the ODB and RCT treatment effect estimates within each stratum. Using the delta method and simulations, we identify a bias problem with the spiked-in estimator that is ameliorated by the convex combination estimator. We apply our methods to data from the Women's Health Initiative, a study of thousands of postmenopausal women which has both observational and experimental data on hormone therapy (HT). Using half of the RCT to define a gold standard, we find that a version of the spiked-in estimator yields lower-MSE estimates of the causal impact of HT on coronary heart disease than would be achieved using either a small RCT or the observational component on its own.     

The value of explicitly emulating a target trial when using real world evidence: an application to colorectal cancer screening

Observational analyses for causal inference often rely on real world data collected for purposes other than research. A frequent goal of these observational analyses is to use the data to emulate a hypothetical randomized experiment, i.e., the target trial, that mimics the design features of a true experiment, including a clear definition of time zero with synchronization of treatment assignment and determination of eligibility. We review a recent observational analysis that explicitly emulated a target trial of screening colonoscopy using insurance claims from U.S. Medicare. We then compare this explicit emulation with alternative, simpler observational analyses that do not synchronize treatment assignment and eligibility determination at time zero and/or do not allow for repeated eligibility. This empirical comparison suggests that lack of an explicit emulation of the target trial leads to biased estimates, and shows that allowing for repeated eligibility increases the statistical efficiency of the estimates.     

Simulation study comparing exposure matching with regression adjustment in an observational safety setting with group sequential monitoring

Sequential methods are well established for randomized clinical trials (RCTs), and their use in observational settings has increased with the development of national vaccine and drug safety surveillance systems that monitor large healthcare databases. Observational safety monitoring requires that sequential testing methods be better equipped to incorporate confounder adjustment and accommodate rare adverse events. New methods designed specifically for observational surveillance include a group sequential likelihood ratio test that uses exposure matching and generalized estimating equations approach that involves regression adjustment. However, little is known about the statistical performance of these methods or how they compare to RCT methods in both observational and rare outcome settings. We conducted a simulation study to determine the type I error, power and time‐to‐surveillance‐end of group sequential likelihood ratio test, generalized estimating equations and RCT methods that construct group sequential Lan–DeMets boundaries using data from a matched (group sequential Lan–DeMets‐matching) or unmatched regression (group sequential Lan–DeMets‐regression) setting. We also compared the methods using data from a multisite vaccine safety study. All methods had acceptable type I error, but regression methods were more powerful, faster at detecting true safety signals and less prone to implementation difficulties with rare events than exposure matching methods. Method performance also depended on the distribution of information and extent of confounding by site. Our results suggest that choice of sequential method, especially the confounder control strategy, is critical in rare event observational settings. These findings provide guidance for choosing methods in this context and, in particular, suggest caution when conducting exposure matching. Copyright © 2014 John Wiley & Sons, Ltd.     

Can Observational Analyses of Routinely Collected Data Emulate Randomized Trials? Design and Feasibility of the Observational Patient Evidence for Regulatory Approval Science and Understanding Disease Project

ObjectivesThe Observational Patient Evidence for Regulatory Approval Science and Understanding Disease (OPERAND) project examines whether real-world data (RWD) can be used to inform regulatory decision making.MethodsOPERAND evaluates whether observational analyses using RWD to emulate index trials can produce effect estimates similar to those of the trials and examines the impact of relaxing the eligibility criteria of the observational analyses to obtain samples that more closely match the real-world populations receiving the treatments. In OPERAND, 2 research teams independently attempt to emulate the ROCKET Atrial Fibrillation and LEAD-2 trials using OptumLabs data. This article describes the design of the project, summarizes the approaches of the 2 research teams, and presents feasibility results for 2 emulations using new-user designs.ResultsThere were differences in the teams’ conceptualizations of the emulation, design decisions for cohort identification, and resulting RWD cohorts. These differences occurred even though both teams were guided by the same index trials and had access to the same source of RWD.ConclusionsReasonable alternative design and analysis approaches may be taken to answer the same research question, even when attempting to emulate the same index trial. Researcher decision making is an understudied and potentially important source of variability across RWD analyses.     

真实世界数据作为外部对照用于药械临床评价的特殊考虑

随机对照试验(RCT)被视为药械临床疗效评价的"金标准"。但针对罕见、重大且无有效治疗方式等疾病, 考虑伦理、成本等因素, RCT并不适宜。此时, 采用真实世界数据(RWD)作为试验的外部对照支持药械临床评价, 可降低患者招募难度、缩减研发时间及成本。本文基于国内外最新发布的RWD有关的指导原则, 并结合团队前期研究经验, 介绍了采用RWD作为外部对照用于支持药械临床评价的常见应用场景、数据来源、研究设计、外部对照选择基本原则和统计分析方法, 以期为相关学者、申办方开展RWD研究提供参考与借鉴。