PRECIS-2:基于研究目标的试验设计

采用随机对照试验（RCT）评价的干预措施效果，涉及理论疗效和实际的临床效果两方面。PRECIS（Pragmatic-Explanatory Continuum Indicator Summary）通过评价RCT设计的解释性和实用性两方面的程度，指导研究者如何实行干预和试验设计，使RCT在内部真实性和外部真实性之间达到一个平衡。在临床试验方案设计过程中，PRECIS的作用逐渐被研究者所认同。为了确保试验的设计与结果相关联，便于患者、临床医生和政策决策者应用，国际多团队专家对PRECIS进行了修正和升级，提出了新的PRECIS-2。PRECIS-2的9个维度都是为了帮助试验设计者思考研究设计的预期效果与试验结果的一致性和特定场景的适用性。本文旨在介绍PRECIS-2的研发、基本原理、特点及应用，以期为国内临床试验设计、决策提供帮助。     

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。     

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.     

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。     

临床随机对照试验的外部真实性及其评价的探讨

一、临床试验及其真实性 临床随机对照试验(RCT)严格遵循随机、对照、盲法等三大设计原则,并在试验过程中有效控制了潜在的混杂与偏倚,从而保证试验结果的真实可靠.现已成为验证临床药物或某项干预措施是否安全有效的重要研究手段.     

临床随机对照试验的外部真实性及其评价的探讨

一、临床试验及其真实性 临床随机对照试验(RCT)严格遵循随机、对照、盲法等三大设计原则,并在试验过程中有效控制了潜在的混杂与偏倚,从而保证试验结果的真实可靠.现已成为验证临床药物或某项干预措施是否安全有效的重要研究手段.     

临床随机对照试验的外部真实性及其评价的探讨

一、临床试验及其真实性 临床随机对照试验(RCT)严格遵循随机、对照、盲法等三大设计原则,并在试验过程中有效控制了潜在的混杂与偏倚,从而保证试验结果的真实可靠.现已成为验证临床药物或某项干预措施是否安全有效的重要研究手段.     

Pragmatic vs. explanatory: an adaptation of the PRECIS tool helps to judge the applicability of systematic reviews for daily practice

Objective

The Pragmatic-Explanatory Continuum Indicator Summary (PRECIS) tool was designed to classify randomized clinical trials (RCT) as being more pragmatic or explanatory. We modified the PRECIS tool (called PRECIS-Review tool [PR-tool]) to grade individual trials and systematic reviews of trials. This should help policy makers, clinicians, researchers, and guideline developers to judge the applicability of individual trials and systematic reviews.

Study Design and Setting

To illustrate the usefulness and applicability of the PR-tool, we applied it to two systematic reviews. Each included RCT was scored on the 10 PRECIS domains on a scale of 1-5. After this scoring, a 10-domain average for each individual trial and for the systematic review a single domain average and an overall average was calculated.

Results

One review was more pragmatic with an average score of 3.7 (range, 2.9-4.6) on our PR-tool, whereas the other review was more explanatory with an average score of 1.9 (range, 1.1-3.3). The results also suggest that the included studies within each systematic review were rather uniform in their approach, although some domains seemed more prone to heterogeneity.

Conclusion

The PR-tool provides a useful estimate that gives insight by estimating quantitatively how pragmatic each RCT in the review is, which methodological domains are pragmatic or explanatory, and how pragmatic the review is.     

A pragmatic–explanatory continuum indicator summary (PRECIS): a tool to help trial designers

ObjectiveTo propose a tool to assist trialists in making design decisions that are consistent with their trial's stated purpose.Study Design and SettingRandomized trials have been broadly categorized as either having a pragmatic or explanatory attitude. Pragmatic trials seek to answer the question, “Does this intervention work under usual conditions?,” whereas explanatory trials are focused on the question, “Can this intervention work under ideal conditions?” Design decisions make a trial more (or less) pragmatic or explanatory, but no tool currently exists to help researchers make the best decisions possible in accordance with their trial's primary goal. During the course of two international meetings, participants with experience in clinical care, research commissioning, health care financing, trial methodology, and reporting defined and refined aspects of trial design that distinguish pragmatic attitudes from explanatory.ResultsWe have developed a tool (called PRECIS) with 10 key domains and which identifies criteria to help researchers determine how pragmatic or explanatory their trial is. The assessment is summarized graphically.ConclusionWe believe that PRECIS is a useful first step toward a tool that can help trialists to ensure that their design decisions are consistent with the stated purpose of the trial.     

A new 'mechanistic-practical" framework for designing and interpreting randomized trials

Methodologists have traditionally categorized randomized controlled trials (RCTs) as explanatory (representing the ideal setting) and pragmatic (representing the real-world setting). Although this framework has greatly advanced the design and interpretation of RCTs, current interpretations of the explanatory-pragmatic framework suffer from two major limitations. First, they confound purpose with structure. Second, they ignore the varying perspective of those using RCT results to make clinical and policy decisions in the real world. The purpose of a trial should determine researchers' choices regarding the trial's structure and the structure of a trial determines the extent to which a decision maker will find the results useful. In this article, we introduce two terms that refer explicitly to the purpose of a trial: A trial is mechanistic to the extent that it addresses a biological relationship. In contrast, a trial is practical to the extent that it provides comprehensive information that bears directly on specific health care decisions. This revised framework facilitates investigators' choice of optimal trial design, and clinicians' optimal interpretation of RCT results. If our goal is clinical trials most relevant to individual patient decision making, we will eschew the use of trials that enroll patients unlikely to benefit (e.g., those with uncertain diagnosis); those likely to be noncompliant; treated by practitioners whose differing expertise is likely to result in differing outcomes; and permitting cointerventions that are likely to influence treatment effectiveness-i.e., the conventional pragmatic trial. Instead we will design, implement, and apply the results of practical trials to our patients.     

Form follows function: pragmatic controlled trials (PCTs) have to answer different questions and require different designs than randomized controlled trials (RCTs)

Aim

Rising concern for demonstrated real world comparative effectiveness has heightened interest in “pragmatic trials” design. Pragmatic trials investigate whether the efficacy, presumed or found in explanatory trials under ideal conditions, can also be detected under real world conditions, i.e. effectiveness. It is also recognized that ‘real world’ effects which are usually addressed in public health research gain growing interest in confirming the ‘road capability’ of results obtained under ideal study conditions. This paper demonstrates that studies under ideal or real world conditions use different methods, generate different information and cannot replace each other.

Subjects and methods

The PCT design meets four requirements of public health and of effectiveness research. It includes all individuals who presented with the selected condition. It classifies the included individuals according to baseline risks. It enables plausibility controls. Finally, it compares the outcomes resulting from specified and not-specified interventions or treatments.

Proposal

We propose a pragmatic controlled trial (PCT) design in which patient preference and other co-factors crucial in determining the actual effectiveness of interventional options will not be neutralized by concealed randomization and blinding. This design is applicable to record the selected interventions and generated outcomes in day-to-day health care and is capable of incorporating preference and other participative factors into assessment of effectiveness.

Conclusions

The PCT design is useful for public health research, e.g. the effectiveness of interventions to change smoking habits or to prevent death from breast cancer, as well as for comparative effectiveness research where it will supplement the traditional randomized controlled trial (RCT).     

Study control,violators, inclusion criteria and defining explanatory and pragmatic trials

Important differences between explanatory and pragmatic studies were originally argued by Schwartz and Lellouch. Three important differences between the two types of study involve study control, study violators and inclusion criteria. It was originally argued that explanatory studies are highly controlled, and pragmatic studies may be looser and more like 'real life'. It was argued that an explanatory study should only analyse those receiving treatment, and a pragmatic study would analyse all randomized patients. Explanatory trials are said to use homogeneous groups, and pragmatic studies have less selection (better generalizability). Some suggestions are put forward to update the original distinctions between these two attitudes for future study design. Poor study control is undesirable (but might be necessary) and should not be welcomed as pragmatic. The intention-to-treat strategy is now considered as standard for nearly all trials. Homogeneity is a red herring for studies in humans. Inclusion criteria should be minimized and they should not be used to justify claims of representativeness. Routine criticism of randomized controlled trials for being unrepresentative is unwarranted. We should accept that most trials in humans are 'explanatory'. The division line should be moved, so that pragmatic studies are in the domain of non-therapeutics and complex treatments.     

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。     

Evidence-based public health: moving beyond randomized trials

Randomized controlled trials (RCTs) are essential for evaluating the efficacy of clinical interventions, where the causal chain between the agent and the outcome is relatively short and simple and where results may be safely extrapolated to other settings.

However, causal chains in public health interventions are complex, making RCT results subject to effect modification in different populations. Both the internal and external validity of RCT findings can be greatly enhanced by observational studies using adequacy or plausibility designs. For evaluating large-scale interventions, studies with plausibility designs are often the only feasible option and may provide valid evidence of impact.

There is an urgent need to develop evaluation standards and protocols for use in circumstances where RCTs are not appropriate.