Patient-centered or 'central' patient: Raising the veil of ignorance over randomization.
Patient-centered or 'central' patient: Raising the veil of ignorance over randomization.
复制标题
以患者为中心或“中心”患者：揭开随机化的无知面纱。
DOI：
10.1002/sim.5398
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发表时间：
2012
影响因子：
2
通讯作者：
Basu,Anirban
中科院分区：
文献类型：
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作者：
Basu,Anirban
在随附的评论中，劳尔支持大规模随机临床试验（RCT）在比较有效性研究（CER）中的作用，并认为目前关于CER的辩论将重振临床试验事业。[1]虽然我同意劳尔的观点，即随机化将继续在未来的比较有效性研究中发挥重要作用，但我认为，目前的临床试验企业需要重新发明，而不是重新振兴，因为它福尔斯缺乏实现最近CER立法规定的目标所需的数据生产基础设施。[2]我讨论了一些问题，围绕目前的RCT基础设施的关注，以及我们如何可能会想到重建它，以满足卫生保健的需求。比较有效性研究（CER）的意思是“帮助消费者，临床医生，购买者和决策者作出明智的决定”。[3]然而，每个决策者的信息需求是截然不同的。此外，各级的决策是密切相关的。个体患者和他们的医生通常需要细致入微的信息来为他们做出正确的治疗决定。制造商和采购商需要关于人口中治疗的潜在吸收以及在接受治疗的患者中产生的潜在价值的信息，以做出正确的定价和数量决定。负责保险决策的政策制定者考虑所有这些信息以及他们决策的预算影响。Tunis等人[4]认识到决策的复杂性，并推荐了实用的临床试验，其中假设和研究设计是专门为回答决策者面临的问题而开发的。然而，他们也认识到，我们没有足够的时间和预算来在临床背景下为每个决策水平单独进行CER研究。因此，如何从一个或几个CER研究中产生的结果可以为各级决策提供信息，仍然是未来CER研究设计中的最大挑战。随机对照试验经常被吹捧为随机化的力量，因为治疗的随机分配将等同于影响治疗组之间结局的所有可能因素的分布。因此，治疗组之间平均结局的任何差异都可归因于治疗分配的差异。虽然这个简单而强大的想法有助于建立一组患者之间治疗分配的因果效应，但目前还不清楚这种效应如何为决策提供信息。
In the accompanying commentary, Lauer supports the role of large-scale randomized clinical trials (RCTs) in comparative effectiveness research (CER) and argues that the current debate on CER will reinvigorate the clinical trial enterprise.[1] Although I agree with Lauer that randomization will continue to play a big role in comparative effectiveness research going forward, I believe that the current clinical trial enterprise needs re-invention rather than reinvigoration as it falls short of the data production infrastructure required to achieve the goals set out by the recent legislation on CER.[2] I discuss some of the issues surrounding the concerns about current RCT infrastructure and how we might think of rebuilding it to meet the needs of health care.Comparative effectiveness research (CER) is meant “to assist consumers, clinicians, purchasers and policymakers to make informed decisions”.[3] However, the informational requirements for each of these decision makers are starkly different. Moreover, decision making at all levels are strongly interrelated. Individual patients and their physicians usually require nuanced information to make the right treatment decision for them. Manufacturers and purchasers require information on the potential uptake of treatments in the population, and potential value generated among the patients taking a treatment to make correct pricing and quantity decisions. Policy makers in charge of insurance coverage decisions consider all of this information plus the budget impact of their decisions. Tunis et al [4] recognize this complexity in decision making and recommend practical clinical trials for which the hypothesis and study design are developed specifically to answer the questions faced by decision makers. However, they also recognize that we do not have the luxury of time and budget to conduct a CER study separately for each level of decision within a clinical context. Therefore, how results emanating from a single or a few CER study (ies) can inform all levels of decision making remain to be the biggest challenge in the designs of CER studies going forward. RCTs are often touted on the powers of randomization as random allocations of treatment would equate the distribution of all possible factors that affect outcomes among the treatment groups. Therefore, any difference in average outcomes between the treatment groups can be attributed to differences in treatment allocation. While this simple and powerful idea help establish a causal effect of treatment allocation among a group of patients, it is far from clear how such an effect should inform decision making for