临床流行病学的关键概念：解决和报告随机对照K-的偏倚来源

Bias can arise from the randomization process when unreliable methods are used for generating the random allocation sequence, when treatment allocation is not adequately concealed, or when the randomization process is not well implemented. This can result in potentially confounding variables being unbalanced between trial arms at baseline [7]. Lack of concealment of treatment assignment (blinding) often cannot be avoided and does not necessarily lead to bias but it should be reported so that risk of bias can be adequately assessed.

Bias due to deviations from intended interventions can arise when treatments are not delivered fully as intended (lack of fidelity). It can also arise from exposure of participants to factors influencing the outcome other than the intervention to which they have been assigned, including accidental exposure to an intervention in another trial arm. This “contamination” can arise, for example, when participants fail to receive a hoped-for benefit from their intervention and seek an alternative treatment. Bias can also arise from participants not adhering fully to the treatment regimen. This is very common and often cannot be avoided, but level of adherence should always be assessed and reported. Finally, bias can arise because those delivering an intervention in one arm are more enthusiastic about their intervention than those in another arm. This “allegiance bias” can affect fidelity and even within the parameters set for delivery of the intervention, can inflate or reduce the effect size [8].

Missing outcome data are an ubiquitous problem in clinical trials, often because participants no longer wish to engage with the trial. If outcome data are missing “at random,” that is, in a way that is unrelated to the outcome, the effect is only to reduce the statistical power because of reduced sample size for analysis. However, if outcome data are missing in a way that could be related to the outcome of interest, this breaks the randomization and undermines the key strength of the trial design [9]. If outcome data are missing to different extents, or for different reasons, in different arms of the trial this can lead to an underestimate or overestimate of any effects.

Bias in outcome measurement can be due many factors, such as expectations favoring an intervention or incentives to produce data that confirm predictions. Clinical trials often rely on subjective outcome measures that are potentially subject to bias and error. Differential bias can arise from different methods being used to assess outcomes in different trials arms. Bias can also arise from repeated testing or changes in reference points used to judge outcomes (“response shift”).

Bias in selection of findings to report or highlight is common and not fully addressed by requirements to register study protocols or analysis plans prior to analyzing the data. Subgroup analyses are particularly vulnerable to this bias as they provide multiple opportunities to find and select or highlight findings that accord with a desired outcome [10]. Underpowered studies are also particularly vulnerable to reporting bias, compounded by a tendency of journals to be more inclined to accept articles reporting positive findings. The funding source or sponsor of a study can also be a factor contributing to reporting bias.

Table 1 provides an overview of the sources of bias outlined, together with recommendations for prevention, mitigation, and reporting. Regarding the latter, a trial report should be written in accordance with the CONSORT (Consolidated Standards of Reporting Trials) guidelines and relevant extensions (see for a full list of reporting guidelines: www.equator-network.org) [11]. A new, free online Paper Authoring Tool () has used these guidelines and extensive experience in writing up clinical trials to provide authors with a way to ensure that trial protocols and reports are prepared in a way that maximises transparency. We recommend to report any additional measures that were taken to reduce the risk of bias, for example, how treatment preferences, adherence and fidelity were enhanced. The trial report should provide data that have been collected in relation to the different sources of bias, if any, and results from analyses incorporating these (eg, results from sensitivity analyses). Finally, authors should not only discuss if a relevant form of bias could have been introduced but also how this would affect the interpretation of the study findings.

3. Conclusion

While randomized controlled trials can provide a high degree of confidence that outcomes are caused by interventions, in practice there are major, often unavoidable, sources of bias. Trialists must pay close attention to all of these and take steps to mitigate them where possible and always to report them to allow users of research to form a judgement about the extent to which findings can be relied upon. Our concise overview of important sources of bias – based on Cochrane's risk-of-bias (RoB 2) framework – together with our list of recommendations for their prevention, mitigation, and reporting may provide guidance in this respect.

全文翻译（仅供参考）

探索性结果表明被广泛应用看来是审计医学偏袒功效的最强劲实质上设计。虽然普遍性一般而言是有限的，但早先有助于必需观察到的功效是真正的。然而，即使在展开更佳的结果表明之前，也依赖于常见于的种族歧视就其联，这对这种解释构成了威胁。修订后的 Cochrane 结果表明偏倚安全性用以 (RoB 2) 分辨了五个意味著阻碍结果表明结果的偏倚不宜用，这些偏倚源自 (1) 早先反复，(2) 与预想偏袒举措的也就是说，(3) 毕竟结果统计数据，（4）结果校准，和（5）结果统计统计数据。我们采用 RoB 2 作为劝告构建，以协助数据数据分析人员更为严重这些种族歧视就其联并必需统计统计数据的专业性，以便数据数据分析浏览器了解它们。

1 . 背景

探索性结果表明被广泛应用看来是审计医学偏袒功效的最强劲实质上设计，因为早先可以潜在地抑制由于参加者预先依赖于的特征（偏爱是HRS原因）在偏袒和比较条件下的关联而引致的偏倚。如果实质上设计和实施得当，结果表明很强较高的实质上理论上，这意味着因果关系的推论（即偏袒引致结果的变化）并未该系统错误（或也就是说）[1]. 然而，在医学数据数据分析的许多不宜用之前，结果表明的实际最关键问题意味著才会损害早先的延续性并引致偏倚。此外，仍然依赖于只能通过早先消除的偏倚就其联，并且意味著在整个数据数据分析反复之前发生（在实质上设计、实施、数据分析和结果表明统计统计数据之后）[ 2-4 ]。这种也就是说才会减小结果表明的实质上理论上，引致真正病人功效的失真[3]。关键的是，同一结果表明之前有所不同结果的偏倚安全性意味著有所不同。

修订后的 Cochrane 结果表明偏倚安全性用以 (RoB 2) 分辨了五个意味著阻碍结果表明结果的偏倚不宜用，这些偏倚源自 (1) 早先反复，(2) 与预想偏袒举措的也就是说，(3) 毕竟结果统计数据; (4) 结果校准，和 (5) 统计统计数据结果[ 3 , 5 ]。该用以有助于审计该系统称赞之前构成的结果表明的偏倚安全性（依赖于其他用以[6]）。在这里，我们采用 RoB 2 作为劝告构建，以协助数据数据分析人员计划或展开结果表明，以更为严重这些种族歧视就其联并必需统计统计数据的专业性，以便数据数据分析浏览器了解它们。它还可以为结果表明统计统计数据的编者包括对结果表明统计统计数据展开批判性称赞的指导。

2 . 种族歧视的就其联以及预防和更为严重的劝告

当采用不合理的新方法来转化成随机扣除基因序列时，当病人扣除并未合理隐密时，或者当早先反复并未最好地实施时，早先反复意味著才会激发也就是说。这意味著引致基线时结果表明组之间的潜在群集表达式不均衡[7]。病人扣除（盲法）的欠缺隐密一般而言只能避免，也不一定才会引致偏倚，但不宜统计统计数据，以便合理审计偏倚安全性。

当病人未无论如何按预想展开时（欠缺保真度），意味著才会因偏离预想偏袒而激发也就是说。它也意味著由此而来参加者暴露于阻碍结果的原因而不是他们被扣除的偏袒举措，包括意外事故暴露于另一个结果表明组之前的偏袒举措。例如，当参加者未能从他们的偏袒之前给予预想的诱因并寻求替代病人时，就才会显现这种“废料”。也就是说也意味著由此而来参加者并未无论如何严格遵守病人计划。这是非常常见于的，一般而言是只能避免的，但不宜仍然审计和统计统计数据依从性程度。先前，意味著才会显现种族歧视，因为那些在一个手臂上展开偏袒的人比在另一个手臂上的人不够酷爱他们的偏袒。[8] .

毕竟结果统计数据是医学结果表明之前普遍依赖于的最关键问题，一般而言是因为参加者不必借此加入结果表明。如果结果统计数据“随机”有缺陷，即以与结果无关的模式有缺陷，其功效只是减小了统计功效，因为数据分析的样本量减低了。但是，如果结果统计数据以意味著与感兴趣的结果就其的模式丢失，这才会毁损早先并毁损结果表明实质上设计的最关键强度[9]。如果结果统计数据在有所不同程度上或由于有所不同原因在结果表明的有所不同组之前有缺陷，这意味著引致夸大或高估任何阻碍。

结果校准之前的也就是说意味著是由于许多原因引致的，例如有利于偏袒的期望或激发证实预测的统计数据的动机。医学结果表明一般而言依赖于意味著依赖于也就是说和错误的主观结果校准。关联偏倚意味著由此而来用于审计有所不同结果表明组结果的有所不同新方法。也就是说也意味著来自重复测试或用于推断结果的并不一定的变化（“响不宜转移”）。

在自由选择统计统计数据或突显的结果时依赖于也就是说是常见于的，并且在数据分析统计数据之前注册数据数据分析计划或数据分析计划的允许未有无论如何消除。亚组数据分析相当多非常容易受到这种种族歧视的阻碍，因为它们包括了多种机才会来辨认出和自由选择或显眼符合预想结果的结果[10]。动力欠缺的数据数据分析也相当多非常容易受到统计统计数据种族歧视的阻碍，而科学杂志不够倾向于不感兴趣统计统计数据积极辨认出的文之前。数据数据分析的资金就其联或赞助商也意味著是引致统计统计数据偏倚的一个原因。

关于后者，不宜根据 CONSORT（统计统计数据结果表明综合标准）须知和就其扩充编写结果表明统计统计数据[11]。一个新的、免费的的网站论文创作用以在撰写医学结果表明时采用了这些须知和丰富的经验，为编者包括了一种新方法，以必需以最大限度地减小专业性的模式作准备结果表明计划和统计统计数据。我们劝告统计统计数据为减小偏倚安全性而采取的任何其他举措，例如，如何减小病人一般来说、依从性和保真度。结果表明统计统计数据不宜包括已收集到的与有所不同偏倚就其联（如果有）就其的统计数据，以及构成这些偏倚的数据分析结果（例如，一般来说数据分析的结果）。先前，编者不仅不宜当谈论究竟可以带入就其基本上的种族歧视，还不宜当谈论这将如何阻碍对数据数据分析结果的解释。

3 . 推论

虽然探索性结果表明可以包括对结果由偏袒举措招致的移动性置信度，但仅仅依赖于主要的、一般而言是不太可能的偏倚就其联。结果表明者必须深厚关注所有这些最关键问题，并在意味著的情况下采取举措更为严重这些最关键问题，并仍然统计统计数据这些最关键问题，以便数据数据分析浏览器对数据数据分析结果的可信赖程度逐步形成推断。基于 Cochrane 的偏倚安全性 (RoB 2) 构建，我们对关键偏倚就其联的通俗概述，以及我们的预防、更为严重和统计统计数据劝告列表，意味著才会在这方面包括指导。

本文转载自其他的网站，不代表心理健康界观点和立场。如有内容和图片的著作权异议，请及时联系我们（邮箱：guikequan@hmkx.cn）