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.     

再论观察与实验：大数据现实世界研究不能取代随机对照试验

传统上,流行病学多以干预划分观察和实验,干预研究等于实验研究,还认为干预研究的科学性高于观察性研究。在一般科学实验里,干预指人为施加的改变自然状况的措施。干预并不一定是有益的,也并不一定是研究者当下施加的,研究者、受试者或第三者目前或过去施加的措施都可以形成"有效的"干预。例如,由研究者、受试者和第三者通过某种方法致使视神经损伤,都可以形成有效改变视神经正常功能的干预,研究者可以由此观察到视神经和视力的关系。以此推论,由受试者自己过去施加的不良干预（如吸烟）也属于干预,那么研究吸烟和肺癌的观察性队列研究就等同于实验研究了。由此看来,干预本身并不足以有效地区分观察和实验。如果认为实验的科学性高于观察,那么在干预的基础上,只能从科学性上（即设计特征）区分观察和实验。在评估医学干预效果的临床试验中,随机分组是在传统认为的观察研究基础上引入的最重要的偏倚控制措施,应该是区分观察和实验的核心属性。如果一定要把人群研究分成观察和实验,随机对照试验才是真正的实验研究,非随机分组形成的干预研究属于试验,但不是实验。基于大数据的现实世界研究,如果没有随机分组,不能构成实验,也不能成为对干预效果的最终检验。大数据现实世界研究不能取代随机对照试验,这是本文希望传达的最重要的信息。     

模拟目标试验在真实世界中的应用：他汀类药物对糖尿病患者死亡率的影响

模拟目标试验是一种观察性研究方法，该方法在无法进行随机对照试验（RCT）的情况下，可以利用真实世界数据（如观察性数据及历史性数据）按照RCT的设计原则进行研究设计，模拟随机分组形成干预组和对照组，最终得到近似RCT的高可信度结论。本研究以他汀类药物对糖尿病患者心肌梗死预后的影响为例，对模拟目标试验的基本概念和应用流程进行介绍，为其在真实世界中的应用提供参考。     

大型随机对照试验:精准流行病学研究的典范与陷阱

现代流行病学是医学应用型研究的方法论,是在人群中定量地研究有关健康、疾病和医疗服务实践问题一般规律的科学和艺术。流行病学研究结果的准确度主要取决于研究的设计类型,精确度主要取决于样本量大小。大型随机对照试验是最精、最准的流行病学研究设计类型,但是由于伦理的限制,只能用于评估医学干预效果。一项研究需要的设计严谨性和样本量与预期效果的大小成反比：效果越小,所需的研究设计就越严谨,需要的样本量就越大。因此,只有当疗效比较小时,才需要大型随机对照试验,当疗效十分明显时,中小型随机对照试验甚至观察性研究就足以证明其有效性。从研究阶段上看,它是确认性、终结性研究,而不是提出假设的原创性研究。然而,研究的价值最终取决于研究问题的意义和原创性,而不是研究方法和P值的大小。过度推崇大型随机对照试验会引发：①对中、小疗效干预的过度强调;②对确认性研究的过度重视,以及对项目大小和经费多少而不是科学问题的追逐,进而弱化原创性研究工作;③增加研究资源、医学活动和患者利益被制药公司绑架的风险。     

观察性研究文献评价标准

临床研究根据有无设计的干预措施,分为观察性研究和试验性研究,试验性研究主要类型包括随机对照研究(RCT)和非随机对照研究,观察性研究中最常用的是病例对照研究和队列研究.在阅读文献或荟萃分析时均须对文献质量进行客观评价,本文对观察性研究文献评价标准进行介绍.     

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。     

大型公共卫生项目评价研究设计的有关问题探讨

对源于临床医疗领域的随机对照试验(RCT)在大型公共卫生干预尤其是社区干预中拓展应用存在的外部效度和内部效度方面的局限性进行了重点阐释,认为外部效度受到社会和行为效应修饰问题的严重制约,且其传统的内部效度优势也因公共卫生项目因果路径长等特征而不能充分发挥,并指出一系列限制其现实可行性的因素。建议在大型公共卫生干预项目中,主要根据评价结论的用途并结合现实条件来确定合适的评价研究设计。针对意在检验因果关系的探索性项目,建议采用更广泛意义上的实验设计,包括整群随机试验设计、分阶渐进随机试验设计和N-of-1设计等,以改善传统RCT的现实可行性问题,同时,应发挥定性研究方法的优势,尽可能开展过程评价和项目监测以弥补内部效度方面存在的缺陷;对于因果关系已得到验证的示范项目或应用型项目,建议采用准实验设计,如时间序列设计或多重基线设计等,以及流行病学观察性研究方法,以保证充分的外部效度。     

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。     

如何撰写高质量的流行病学研究论文 第八讲 非劣效性和等效性随机对照试验的报告规范——CONSORT声明的扩展

非劣效性和等效性随机对照试验的应用日益广泛，为医疗卫生干预提供了重要的证据。对其结果的准确评价有赖于充分、准确的报告。CONSORT声明向非劣效性和等效性随机对照试验的扩展是2006年提出的又一个报告规范。本文介绍非劣效性和等效性随机对照试验的特殊方法学问题、实施与报告现状以及CONSORT扩展声明的清单，并对清单中的部分条目进行了解释与说明。     

Clinical effectiveness research in managed-care systems: lessons from the Pediatric Asthma Care PORT. Patient Outcomes Research Team

OBJECTIVE: To highlight the unique challenges of evaluative research on practice behavior change in the "real world" settings of contemporary managed-care organizations, using the experience of the Pediatric Asthma Care PORT (Patient Outcomes Research Team). STUDY SETTING: The Pediatric Asthma Care PORT is a five-year initiative funded by the Agency for Healthcare Research and Quality to study strategies for asthma care improvement in three managed-care plans in Chicago, Seattle, and Boston. At its core is a randomized trial of two care improvement strategies compared with usual care: (1) a targeted physician education program using practice based Peer Leaders (PL) as change agents, (2) adding to the PL intervention a "Planned Asthma Care Intervention" incorporating joint "asthma check-tips" by nurse-physician teams. During the trial, each of the participating organizations viewed asthma care improvement as an immediate priority and had their own corporate improvement programs underway. DATA COLLECTION: Investigators at each health plan described the organizational and implementation challenges in conducting the PAC PORT randomized trial. These experiences were reviewed for common themes and "lessons" that might be useful to investigators planning interventional research in similar care-delivery settings. CONCLUSIONS: Randomized trials in "real world" settings represent the most robust design available to test care improvement strategies. In complex, rapidly changing managed-care organizations, blinding is not feasible, corporate initiatives may complicate implementation, and the assumption that a "usual care" arm will be static is highly likely to be mistaken. Investigators must be prepared to use innovative strategies to maintain the integrity of the study design, including: continuous improvement within the intervention arms, comanagement by researchers and health plan managers of condition-related quality improvement initiatives, procedures for avoiding respondent burden in health plan enrollees, and anticipation and minimization of risks from experimental arm contamination and major organizational change. With attention to these delivery system issues, as well as the usual design features of randomized trials, we believe managed-care organizations can serve as important laboratories to test care improvement strategies.     

Cluster randomized controlled trials in primary care: An introduction

Background: Cluster randomized trials occur when groups or clusters of individuals, rather than the individuals themselves, are randomized to intervention and control groups and outcomes are measured on individuals within those clusters. Within primary care, between 1997 and 2000, there has been a virtual doubling in the number of published cluster randomized trials. A recent systematic review, specifically within primary care, found study quality to be both generally lower than that reported elsewhere and not to have shown any recent quality improvement. Objective: To discuss the design, conduct and analysis of cluster randomized trials within primary care in terms of the appropriate expertise required, potential bias, ethical considerations and expense. Discussion: Compared with trials that involve the randomization of individual participants, cluster randomized trials are more complex to design and analyse and, for a given sample size, have decreased power and a broadening of confidence intervals. Cluster randomized trials are specifically prone to potential bias at two levels—the cluster and individual. Regarding the former, it is recommended that cluster allocation be undertaken by a party independent to the research team and careful consideration be given to ensure minimal cluster attrition. Bias at the individual level can be overcome by identifying trial participants before randomization and at this time obtaining consent for intervention, data collection or both. A unique ethical aspect to cluster randomized trials is that cluster leaders may consent to the trial on behalf of potential cluster members. Additional costs of cluster randomized trials include the increased number of patients required, the complexity in their design and conduct and, usually, the need to recruit clusters de novo.

Conclusion: Cluster randomized trials are a powerful and increasingly popular research tool. They are uniquely placed for the conduct of research within primary-care clusters where intracluster contamination can occur. Associated methodological issues are straightforward and surmountable and just need careful consideration and management.     

真实世界研究在医疗器械临床评价中的应用

该文通过阐明真实世界研究(RWS)的相关概念,以及分析RWS与随机对照试验(RCT)的区别及联系、医疗器械临床评价的发展趋势,并结合RWS在医疗器械临床评价中的发展与应用,剖析将真实世界数据(RWD)应用于医疗器械临床评价中的各类情形,同时结合实际指出RWS在开展过程中存在的问题,探讨如何利用RWS在医疗器械临床评价中发挥重要作用,以及如何利用RWS将其数据更好地应用于医疗器械的上市、监管、成果转化及全生命周期管理中。     

Chronic disease prevention: public health potential and research needs

This paper, arising out of an event to honour the statistical and scientific contributions of Professor Peter Armitage, is concerned with research strategies and needs for chronic disease prevention. A few highlights from recent intervention trials for the prevention of cancer, cardiovascular disease, fractures and diabetes is provided, along with a discussion of some settings where intervention trial results seem discrepant with a body of preceding observational data. This background is used to identify research strategies and infrastructure needs for moving this vitally important research area forward, for both chemoprevention and lifestyle modification interventions.     

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.     

An alternative trial design to overcome validity and recruitment problems in primary care research

BACKGROUND: Although the randomized controlled trial is widely accepted as the best design to investigate new interventions, conducting a trial in primary care may present researchers with many methodological problems. OBJECTIVE: Our aim was to present an alternative trial design to overcome internal validity and recruitment problems. METHODS: In a randomized controlled trial, fatigued employees absent from work were selected among the population of an occupational health service in the South of The Netherlands. Patients randomly assigned to the experimental condition received cognitive behavioural therapy by a research GP near their home address, whereas patients in the control group received no intervention. We describe our considerations for building an alternative design. Research GPs and patients were recruited separately for the study. The pre-randomization design was applied. RESULTS: Nine research GPs performed all the interventions. Seventy-six experimental patients and 75 control patients were selected for study participation. Of these, only six patients in the experimental group and seven patients in the control group withdrew from the study at some point during follow-up. CONCLUSION: Results suggest that recruitment and randomization procedures in the alternative design served their purpose well. The alternative design proposed here might have several advantages compared with conventional trial procedures. However, our design is not widely applicable and there are ethical aspects involved that should be considered. Researchers should address their creativity when trying to minimize the problems they may encounter in designing a study.     

Assessing social work effectiveness in child care practice: the contribution of randomized controlled trials

This article discusses the role of randomized controlled trials (RCTs) in evaluating the impact of social work interventions with children. While recognizing the difficulties of applying RCTs to all aspects of practice, we argue that controlled trials can provide the most convincing evidence of the impact of social work activities on the welfare of children and families. Accumulating evidence of the effectiveness of interventions, we propose, should constitute the core business of social work research. To this end, it is necessary to recognize the primacy of the randomized controlled trial in exploring the relationship between social work activities and client outcomes.     

Should meta-analyses of interventions include observational studies in addition to randomized controlled trials? A critical examination of underlying principles

Some authors argue that systematic reviews and meta-analyses of intervention studies should include only randomized controlled trials because the randomized controlled trial is a more valid study design for causal inference compared with the observational study design. However, a review of the principal elements underlying this claim (randomization removes the chance of confounding, and the double-blind process minimizes biases caused by the placebo effect) suggests that both classes of study designs have strengths and weaknesses, and including information from observational studies may improve the inference based on only randomized controlled trials. Furthermore, a review of empirical studies suggests that meta-analyses based on observational studies generally produce estimates of effect similar to those from meta-analyses based on randomized controlled trials. The authors found that the advantages of including both observational studies and randomized studies in a meta-analysis could outweigh the disadvantages in many situations and that observational studies should not be excluded a priori.     

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.