An exploratory machine learning approach to identify placebo responders in pharmacological binge eating disorder trials.

ABSTRACT: Randomized, placebo-controlled trials for binge eating disorder (BED) have revealed highly variable, and often marked, rates of short-term placebo response. Several quantitative based analyses in patients with BED have inconsistently demonstrated which patient factors attribute to an increase in placebo response. The objective of this study is to utilize machine learning (ML) algorithms to identify moderators of placebo response in patients with BED. Data were pooled from 12 randomized placebo-controlled trials evaluating different treatment options for BED. The final dataset consisted of 189 adults receiving placebo with complete information of baseline variables. Placebo responders were defined as patients experiencing ≥75% reduction in binge eating frequency (BEF) at study end point. Ni

SUBMITTER: Goyal RK 

PROVIDER: S-EPMC9747128 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature