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Can real-world data really replace randomised clinical trials?
BMC Medicine ( IF 9.3 ) Pub Date : 2020-01-15 , DOI: 10.1186/s12916-019-1481-8
Sreeram V Ramagopalan ₁ , Alex Simpson ₂ , Cormac Sammon ₃

Affiliation

Classically, randomised controlled trials (RCTs) are considered the gold standard for demonstrating product efficacy for the regulatory approval of medicines. However, as personalised medicine becomes increasingly common, patient recruitment into RCTs is affected and – sometimes – it is not possible to include a control arm [1].

Real-world data (RWD) are data that are collected outside of RCTs [2]. They are gaining increasing attention for their use in regulatory decision-making. The United States twenty-first Century Cures Act mandated that the US Food and Drug Administration (FDA) should provide guidance about the circumstances under which manufacturers can use RWD to support the approval of a medicine. More recently, investigators from the European Medicines Agency (EMA) detailed their views on this topic [3].

RWD for regulatory approval: opportunities and challenges

Eichler et al., from the EMA, state that, “the RCT will, in our view, remain the best available standard and be required in many circumstances, but will need to be complemented by other methodologies to address research questions where a traditional RCT may be unfeasible or unethical.” Thus, the gauntlet has been laid down for RWD to be used to support European regulatory approval. Indeed, RWD has been used by the EMA to approve several medicines for rare/orphan indications [4]. Eichler and colleagues, however, highlight that RWD methods must be critically appraised before they can be more widely accepted. They suggest that this appraisal can be undertaken via prospective validation of any proposed method with a pre-defined protocol.

Why the need for validation? Studies of the concordance between the results of RCTs and RWD studies investigating the same research question have given mixed results [5, 6]. It has been suggested that this discordance can be attributed to differences in the populations being investigated, or bias in RWD studies as a result of lack of randomisation.

Using an example of cancer risk in statin users, Dickerman and co-workers attempted to understand why RWD studies have shown a protective effect and RCTs showed no effect on neoplasm incidence [7]. One of the key principles of an RCT is to assess patient characteristics at baseline to check study eligibility based on inclusion/exclusion criteria. If eligibility is met, the next task is to randomise subjects into groups and, subsequently, to provide treatment as assigned for each group. Dickerman et al. operationalised a similar ‘target trial’ approach using RWD and followed up trial-eligible new and non-users of statins to compare rates of cancer between these groups. Performing the analysis in this way enabled the researchers to illustrate that results from RWD were in acquiescence with those from RCTs. Furthermore, previously reported differences were largely a result of two avoidable issues: immortal time and selection bias caused by the inclusion of prevalent statin users (prevalent users had to have survived without cancer up to baseline, leading to artificially lower rates of cancer in the statin group), rather than being attributed to the lack of randomisation per se.

As Dickerman et al. acknowledge, a limitation of the outcome they studied is that confounding by indication (whereby the reason for prescribing a patient medication is also associated with the outcome of interest) is unlikely to have a major role. Where the outcome is more likely to be affected by confounding by indication, then – to mimic the randomisation element of an RCT and appropriately compare treatment groups – RWD studies must carefully adjust for all baseline confounders. In this regard, Carrigan et al. recently report results exploring a research question more likely to be affected by confounding by indication [8]: whether control groups generated from RWD could approximate the control arms used in published RCTs in non-small cell lung cancer. In 10 of the 11 analyses conducted, hazard ratio estimates for overall survival derived from comparing RWD control arms with the intervention arm from the RCT were similar to those seen in the original RCT comparison. However, the analyses showed that a simple ‘target trial’ alignment of the RWD arm with the trial inclusion/exclusion criteria could not fully replicate the RCT effect estimate; additional adjustment to control for confounding using propensity scores was required. The single non-concordant analysis was thought to be associated with a biomarker that was likely enriched in the RCT but was not present in RWD and therefore could not be adjusted for. This exception to the overall consistency between RWD and RCT findings highlights the importance of needing RWD with information available on all possible confounders to avoid generating inaccurate results.

These two recent studies show that analytical methods and approaches are in place to enable consistency between RCT and RWD results. Further evidence will arise from the FDA-funded RCT DUPLICATE project, which will investigate RCT–RWD concordance on a larger scale [9]. In light of this, the question arises: how many examples are required before regulators can begin to accept RWD for regulatory decision-making? Eichler et al. state that the answer is unlikely to be simple: decision-makers should perhaps first accept RWD analyses for situations in which there is a relatively small impact (e.g. label expansion) and then gradually expand acceptability as confidence in the method grows.

Accumulating evidence suggests that appropriately conducted RWD studies have the potential to support regulatory decisions in the absence of RCT data. Further work may be needed to better illustrate the settings in which RWD analyses can robustly and consistently match the results of RCTs and, more importantly, the settings in which they cannot match them. After careful consideration of the potential for bias, regulators can then determine when they would unequivocally accept RWD in place of an RCT. If studies based on RWD are ever to replace RCTs, regulators may need to accept that the cost of accelerating patient access to treatment carries a higher level of decision-making uncertainty than that with which they are familiar.

Not applicable.

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RCT DUPLICATE. Projects. Effectiveness research with Real World Data to support FDA’s regulatory decision making: A Real World Evidence demonstration project. https://www.rctduplicate.org/projects.html. Accessed 21 Nov 2019.

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Funding

No specific funding was received for this work.

Affiliations

London School of Economics and Political Science, Houghton St, London, WC2A 2AE, UK
- Sreeram V. Ramagopalan
Bristol-Myers Squibb, Sanderson Road, Uxbridge, UB8 1DH, UK
- Alex Simpson
PHMR, Berkley Grove, London, NW1 8XY, UK
- Cormac Sammon

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Contributions

SVR wrote the first draft of the article. All authors contributed to subsequent drafts and the final manuscript. All authors read and approved the final version of the manuscript.

Corresponding author

Correspondence to Sreeram V. Ramagopalan.

Ethics approval and consent to participate

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Competing interests

SVR has been an employee of pharmaceutical and life science consultancy companies. AS is an employee of Bristol-Myers Squibb. CS is an employee of PHMR.

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Ramagopalan, S.V., Simpson, A. & Sammon, C. Can real-world data really replace randomised clinical trials?. BMC Med 18, 13 (2020) doi:10.1186/s12916-019-1481-8

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Received: 09 December 2019
Accepted: 09 December 2019
Published: 15 January 2020
DOI: https://doi.org/10.1186/s12916-019-1481-8

Keywords

Real world data
Randomised clinical trials
Epidemiology
Treatment
Effectiveness

中文翻译：

现实世界中的数据真的可以代替随机临床试验吗？

传统上，随机对照试验（RCT）被认为是证明产品功效的金标准，以用于药品的监管批准。但是，随着个性化医疗的日渐普及，患者进入RCT的机会受到影响，有时甚至无法包括控制臂[1]。

实际数据（RWD）是在RCT之外收集的数据[2]。他们越来越多地将其用于监管决策中。美国《二十一世纪治愈法》规定，美国食品药品监督管理局（FDA）应就制造商可以使用RWD支持药物批准的情况提供指导。最近，来自欧洲药品管理局（EMA）的研究人员详细介绍了他们对该主题的看法[3]。

RWD获监管部门批准：机遇与挑战

EMA的Eichler等人指出：“我们认为，RCT仍将是最佳的可用标准，在许多情况下都是必需的，但是在传统RCT的研究中，还需要其他方法来解决研究问题可能是不可行或不道德的。” 因此，已为RWD制定了规范，以用于支持欧洲监管机构的批准。确实，EMA已使用RWD批准了几种罕见/孤儿适应症药物[4]。但是，Eichler及其同事强调，在对RWD方法进行更广泛的接受之前，必须对其进行严格的评估。他们建议，可以通过使用预定义协议对任何提议方法进行前瞻性验证来进行此评估。

为什么需要验证？研究同一研究问题的RCT和RWD研究之间的一致性研究得出了不同的结果[5，6]。有人认为，这种不一致性可以归因于所研究人群的差异，或者由于缺乏随机性而导致RWD研究存在偏见。

通过使用他汀类药物使用者的癌症风险实例，Dickerman及其同事试图理解为什么RWD研究显示出保护作用而RCT对肿瘤发生率没有作用[7]。RCT的主要原则之一是在基线时评估患者特征，以根据纳入/排除标准检查研究是否合格。如果符合资格，则下一个任务是将受试者随机分组，然后提供为每个组分配的治疗。迪克曼等。使用RWD操作了类似的“目标试验”方法，并跟踪了符合试验条件的新的和未使用他汀类药物的患者，以比较这些组之间的癌症发生率。以这种方式进行分析使研究人员能够说明RWD的结果与RCT的结果是默认的。此外，

如Dickerman等。应当承认，他们研究结果的局限性在于，适应症的混淆（因此开处方患者用药的原因也与所关注的结果有关）不太可能起主要作用。如果结果更可能受到适应症混淆的影响，则–为了模仿RCT的随机因素并适当比较治疗组– RWD研究必须针对所有基线混淆因素进行仔细调整。在这方面，Carrigan等。最近的报告结果探讨了一个可能更容易受到适应症混淆影响的研究问题[8]：RWD产生的对照组是否可以近似于已发表的非小细胞肺癌RCT中使用的对照组。在进行的11项分析中，有10项，通过将RWD对照组与RCT的干预组进行比较得出的总生存率的危险比估计与原始RCT比较中看到的相似。但是，分析表明，将RWD部门与试验纳入/排除标准进行简单的“目标试验”对齐不能完全复制RCT效果估计值。需要对使用倾向得分的混杂进行控制的其他调整。单个非一致性分析被认为与可能在RCT中富集但在RWD中不存在的生物标志物相关，因此无法对其进行调整。RWD和RCT研究结果之间总体一致性的例外情况凸显了需要RWD拥有所有可能混杂因素的信息的重要性，以避免产生不准确的结果。

这两项最新研究表明，已经采用了分析方法和方法来确保RCT和RWD结果之间的一致性。FDA资助的RCT DUPLICATE项目将提供更多证据，该项目将更大规模地研究RCT-RWD的一致性[9]。鉴于此，出现了一个问题：在监管机构开始接受RWD进行监管决策之前，需要多少示例？Eichler等。指出答案不太可能是简单的：对于影响相对较小的情况（例如标签扩展），决策者可能应该首先接受RWD分析，然后随着对方法的信心增强，逐渐扩展可接受性。

越来越多的证据表明，在缺乏RCT数据的情况下，适当进行的RWD研究有可能支持监管决策。可能需要做进一步的工作来更好地说明RWD分析可以可靠且一致地匹配RCT结果的设置，更重要的是，它们不能匹配RCT的设置。在仔细考虑了潜在的偏见之后，监管者可以确定何时明确接受RWD代替RCT。如果基于RWD的研究要取代RCT，则监管机构可能需要接受加速患者获得治疗的成本比他们所熟悉的更高的决策不确定性。

不适用。

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Eichler HG，Koenig F，Arlett P，Enzmann H，Humphreys A，PétavyF等。新颖，非随机的分析方法是否适合决策？需要前瞻性，受控和透明的验证。临床药理学疗法。2019.https：//doi.org/10.1002/cpt.1638。
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隶属关系

英国伦敦霍顿街伦敦政治经济学院，WC2A 2AE，英国
- 斯里拉姆诉拉马格帕兰
百时美施贵宝（Bristol-Myers Squibb），桑德森路，乌克斯桥，UB8 1DH，英国
- 亚历克斯·辛普森
PHMR，伯克利格罗夫，伦敦，英国NW1 8XY
- 科马克·萨蒙（Cormac Sammon）

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会费

SVR撰写了本文的初稿。所有作者都为后续的草稿和最终稿做出了贡献。所有作者均阅读并批准了稿件的最终版本。

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SVR是制药和生命科学咨询公司的雇员。AS是百时美施贵宝公司的员工。CS是PHMR的雇员。

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