Patient Expectations of Assigned Treatments Impact Strength of Randomised Control Trials

Patient engagement with treatments potentially poses problems for interpreting the results and meaning of Randomised Control Trials (RCTs). If patients are assigned to treatments that do, or do not, match their expectations, and this impacts their motivation to engage with that treatment, it will af...

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Bibliographic Details
Main Authors: Roberto Truzoli, Phil Reed, Lisa A. Osborne
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-06-01
Series:Frontiers in Medicine
Subjects:
RCT
Online Access:https://www.frontiersin.org/articles/10.3389/fmed.2021.648403/full
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spelling doaj-579aa77899fa439c92c21753428da5b32021-06-17T07:22:48ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2021-06-01810.3389/fmed.2021.648403648403Patient Expectations of Assigned Treatments Impact Strength of Randomised Control TrialsRoberto Truzoli0Phil Reed1Lisa A. Osborne2Lisa A. Osborne3Department of Biomedical and Clinical Sciences “L. Sacco”, University of Milan, Milan, ItalyDepartment of Psychology, Swansea University, Swansea, United KingdomSchool of Psychology and Counselling, The Open University, Milton Keynes, United KingdomWomen's Health, Swansea Bay University Health Board, Swansea, United KingdomPatient engagement with treatments potentially poses problems for interpreting the results and meaning of Randomised Control Trials (RCTs). If patients are assigned to treatments that do, or do not, match their expectations, and this impacts their motivation to engage with that treatment, it will affect the distribution of outcomes. In turn, this will impact the obtained power and error rates of RCTs. Simple Monto Carlo simulations demonstrate that these patient variables affect sample variance, and sample kurtosis. These effects reduce the power of RCTs, and may lead to false negatives, even when the randomisation process works, and equally distributes those with positive and negative views about a treatment to a trial arm.https://www.frontiersin.org/articles/10.3389/fmed.2021.648403/fullRCTclinical outcome-effectivenesspatient expectationspatient variablesfalse negativesMonte Carlo simulations
collection DOAJ
language English
format Article
sources DOAJ
author Roberto Truzoli
Phil Reed
Lisa A. Osborne
Lisa A. Osborne
spellingShingle Roberto Truzoli
Phil Reed
Lisa A. Osborne
Lisa A. Osborne
Patient Expectations of Assigned Treatments Impact Strength of Randomised Control Trials
Frontiers in Medicine
RCT
clinical outcome-effectiveness
patient expectations
patient variables
false negatives
Monte Carlo simulations
author_facet Roberto Truzoli
Phil Reed
Lisa A. Osborne
Lisa A. Osborne
author_sort Roberto Truzoli
title Patient Expectations of Assigned Treatments Impact Strength of Randomised Control Trials
title_short Patient Expectations of Assigned Treatments Impact Strength of Randomised Control Trials
title_full Patient Expectations of Assigned Treatments Impact Strength of Randomised Control Trials
title_fullStr Patient Expectations of Assigned Treatments Impact Strength of Randomised Control Trials
title_full_unstemmed Patient Expectations of Assigned Treatments Impact Strength of Randomised Control Trials
title_sort patient expectations of assigned treatments impact strength of randomised control trials
publisher Frontiers Media S.A.
series Frontiers in Medicine
issn 2296-858X
publishDate 2021-06-01
description Patient engagement with treatments potentially poses problems for interpreting the results and meaning of Randomised Control Trials (RCTs). If patients are assigned to treatments that do, or do not, match their expectations, and this impacts their motivation to engage with that treatment, it will affect the distribution of outcomes. In turn, this will impact the obtained power and error rates of RCTs. Simple Monto Carlo simulations demonstrate that these patient variables affect sample variance, and sample kurtosis. These effects reduce the power of RCTs, and may lead to false negatives, even when the randomisation process works, and equally distributes those with positive and negative views about a treatment to a trial arm.
topic RCT
clinical outcome-effectiveness
patient expectations
patient variables
false negatives
Monte Carlo simulations
url https://www.frontiersin.org/articles/10.3389/fmed.2021.648403/full
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