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. 2021 Sep 28;11(1):19205.
doi: 10.1038/s41598-021-98874-0.

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

Per M Aslaksen  1
Affiliations

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

Per M Aslaksen. Sci Rep. .

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.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Box and violin plots showing the density of placebo responders versus nonresponders.
Figure 2
Figure 2
Histograms showing the distribution of responders and nonresponders classified by the two-step cluster analysis.
See this image and copyright information in PMC

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