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

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 measuri...

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Published in:Journal of clinical epidemiology Vol. 139; pp. 130 - 139
Main Authors: French, David P, Miles, Lisa M, Elbourne, Diana, Farmer, Andrew, Gulliford, Martin, Locock, Louise, Sutton, Stephen, McCambridge, Jim, Bower, Prof Peter, Clemes, Dr Stacy, Conner, Prof Mark, Dundas, Ms Ruth, Elbourne, Prof Diana, Eldridge, Prof Sandra, Farmer, Prof Andrew, French, Prof David, Gamble, Prof Carrol, Gulliford, Prof Martin, Kee, Prof Frank, Leyland, Prof Alastair, Locock, Prof Louise, Lynch, Dr Rebecca, MacLennan, Prof Graeme, McCambridge, Prof Jim, Miles, Dr Lisa, Rowley, Dr Samuel CS, Sharples, Prof Linda, Sniehotta, Prof Falko F, Snowdon, Dr Claire, Sprangers, Prof Mirjam, Sutton, Prof Stephen
Format: Journal Article
Language:English
Published: United States Elsevier Inc 01-11-2021
Elsevier Limited
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Summary: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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ISSN:0895-4356
1878-5921
1878-5921
DOI:10.1016/j.jclinepi.2021.06.028