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...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2021-06-01
|
Series: | Frontiers in Medicine |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2021.648403/full |
id |
doaj-579aa77899fa439c92c21753428da5b3 |
---|---|
record_format |
Article |
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 |
work_keys_str_mv |
AT robertotruzoli patientexpectationsofassignedtreatmentsimpactstrengthofrandomisedcontroltrials AT philreed patientexpectationsofassignedtreatmentsimpactstrengthofrandomisedcontroltrials AT lisaaosborne patientexpectationsofassignedtreatmentsimpactstrengthofrandomisedcontroltrials AT lisaaosborne patientexpectationsofassignedtreatmentsimpactstrengthofrandomisedcontroltrials |
_version_ |
1721374296501125120 |