Prediction of treatment effect perception in cosmetics using machine learning

Samir Salah*, Loic Colomb, Amelie Marie Benize, Celine Cornillon, Ayet Shaiek, John Charbit, Anna Schritz

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Perception of treatment effect (TE) in cosmetics is multifaceted and influenced by multiple parameters that need to be considered simultaneously when evaluating TE. Here we provide a global approach to predicting TE perception using Random Forest (RF) classifier. Data from three randomized double-blind clinical studies with a total of 50 subjects were used. Different products were applied to each facial cheek of subjects at each visit, and post-application photographs were taken. Nine primary endpoints relating to skin pores were assessed by a specific image analysis algorithm. Twenty judges evaluated the relative pore visibility in all possible pairs of cheek photographs. RF was used to construct a prediction model for TE perception based on the primary endpoints and judge’s evaluation. Intra-study product ranking was done using the Bradley-Terry model on mean judges’ predicted preference. RF demonstrated overall good accuracy in predicting TE perception. Applying RF technique not only addresses issues of multiplicity, nonlinearity and interactions between multiple criteria but also focuses decision-making on one discrete parameter thereby simplifying interpretability and allowing for more consumer-centered claim substantiation in clinical trials. Abbreviations: RF: Random Forest classifier; FDA: The US Food and Drug Agency; ID: Identifier; MCID: Minimal clinical important difference; Param: Parameter; PGIC: Patients’ Global Impression of Change; TE: Treatment effect; TRT: Treatment.

Original languageEnglish
Pages (from-to)55-62
Number of pages8
JournalJournal of Biopharmaceutical Statistics
Volume31
Issue number1
DOIs
Publication statusPublished - 2 Jan 2021

Keywords

  • Cosmetics
  • claim substantiation
  • clinical evaluation
  • discrete choice modelling
  • machine learning
  • statistics
  • treatment effect perception

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