Cutoff criteria for the placebo response: a cluster and machine learning analysis of placebo analgesia

Abstract Computations of placebo effects are essential in randomized controlled trials (RCTs) for separating the specific effects of treatments from unspecific effects associated with the therapeutic intervention. Thus, the identification of placebo responders is important for testing the efficacy o...

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Autor principal:	Per M. Aslaksen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4c153a9f34a54ddf8d9b71a3ea78ebd8
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spelling	oai:doaj.org-article:4c153a9f34a54ddf8d9b71a3ea78ebd82021-12-02T17:37:12ZCutoff criteria for the placebo response: a cluster and machine learning analysis of placebo analgesia10.1038/s41598-021-98874-02045-2322https://doaj.org/article/4c153a9f34a54ddf8d9b71a3ea78ebd82021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98874-0https://doaj.org/toc/2045-2322Abstract Computations of placebo effects are essential in randomized controlled trials (RCTs) for separating the specific effects of treatments from unspecific effects associated with the therapeutic intervention. Thus, the identification of placebo responders is important for testing the efficacy of treatments and drugs. The present study uses data from an experimental study on placebo analgesia to suggest a statistical procedure to separate placebo responders from nonresponders and suggests cutoff values for when responses to placebo treatment are large enough to be separated from reported symptom changes in a no-treatment condition. Unsupervised cluster analysis was used to classify responders and nonresponders, and logistic regression implemented in machine learning was used to obtain cutoff values for placebo analgesic responses. The results showed that placebo responders can be statistically separated from nonresponders by cluster analysis and machine learning classification, and this procedure is potentially useful in other fields for the identification of responders to a treatment.Per M. AslaksenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Per M. Aslaksen Cutoff criteria for the placebo response: a cluster and machine learning analysis of placebo analgesia
description	Abstract Computations of placebo effects are essential in randomized controlled trials (RCTs) for separating the specific effects of treatments from unspecific effects associated with the therapeutic intervention. Thus, the identification of placebo responders is important for testing the efficacy of treatments and drugs. The present study uses data from an experimental study on placebo analgesia to suggest a statistical procedure to separate placebo responders from nonresponders and suggests cutoff values for when responses to placebo treatment are large enough to be separated from reported symptom changes in a no-treatment condition. Unsupervised cluster analysis was used to classify responders and nonresponders, and logistic regression implemented in machine learning was used to obtain cutoff values for placebo analgesic responses. The results showed that placebo responders can be statistically separated from nonresponders by cluster analysis and machine learning classification, and this procedure is potentially useful in other fields for the identification of responders to a treatment.
format	article
author	Per M. Aslaksen
author_facet	Per M. Aslaksen
author_sort	Per M. Aslaksen
title	Cutoff criteria for the placebo response: a cluster and machine learning analysis of placebo analgesia
title_short	Cutoff criteria for the placebo response: a cluster and machine learning analysis of placebo analgesia
title_full	Cutoff criteria for the placebo response: a cluster and machine learning analysis of placebo analgesia
title_fullStr	Cutoff criteria for the placebo response: a cluster and machine learning analysis of placebo analgesia
title_full_unstemmed	Cutoff criteria for the placebo response: a cluster and machine learning analysis of placebo analgesia
title_sort	cutoff criteria for the placebo response: a cluster and machine learning analysis of placebo analgesia
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4c153a9f34a54ddf8d9b71a3ea78ebd8
work_keys_str_mv	AT permaslaksen cutoffcriteriafortheplaceboresponseaclusterandmachinelearninganalysisofplaceboanalgesia
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Cutoff criteria for the placebo response: a cluster and machine learning analysis of placebo analgesia

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