Reducing bias in trials due to reactions to measurement: experts produced recommendations informed by evidence.

Reducing bias in trials due to reactions to measurement: experts produced recommendations informed by evidence.
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DOI:
10.1016/j.jclinepi.2021.06.028
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发表时间:
2021-11
影响因子:
7.2
通讯作者:
MERIT Collaborative Group
MERIT Collaborative Group
中科院分区:
医学2区
文献类型:
--
作者:
French DP;Miles LM;Elbourne D;Farmer A;Gulliford M;Locock L;Sutton S;McCambridge J;MERIT Collaborative Group

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本研究(试验中的测量反应)旨在就如何在改善健康的干预措施的随机对照试验中最好地减少测量反应性(MR)的偏差提出建议。MERIT研究包括:(1)一项更新的系统评价,检查相对于未测量的对照组,测量参与者是否对参与者的健康相关行为有影响,以及三项快速评价,以确定:(i) MR的现有指导;(ii)对量化测量对行为或情感结果影响的研究的现有系统评价;(三)调查行为客观测量对健康相关行为影响的研究;(2)进行德尔菲研究,以确定建议的范围;(3) 2018年10月召开专家研讨会,分组讨论可能的建议。专家组提出了14项建议:(1)确定偏倚是否可能成为试验的问题;(2)决定是否收集数据,判断偏差是否可能成为问题;(3)设计试验以尽量减少这种偏倚的可能性。这些建议提高了人们对测量如何以及在何处可能在试验中产生偏倚的认识,因此有助于试验设计。
This study (MEasurement Reactions In Trials) aimed to produce recommendations on how best to minimize bias from measurement reactivity (MR) in randomized controlled trials of interventions to improve health. The MERIT study consisted of: (1) an updated systematic review that examined whether measuring participants had effects on participants’ health-related behaviors, relative to no-measurement controls, and three rapid reviews to identify:(i) existing guidance on MR; (ii) existing systematic reviews of studies that have quantified the effects of measurement on behavioral or affective outcomes; and (iii) studies that have investigated the effects of objective measurements of behavior on health-related behavior; (2) a Delphi study to identify the scope of the recommendations; and (3) an expert workshop in October 2018 to discuss potential recommendations in groups. Fourteen recommendations were produced by the expert group to: (1) identify whether bias is likely to be a problem for a trial; (2) decide whether to collect data about whether bias is likely to be a problem; (3) design trials to minimize the likelihood of this bias. These recommendations raise awareness of how and where taking measurements can produce bias in trials, and are thus helpful for trial design.
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期刊: Trials
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作者:
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