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 increa...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-12-01
|
| Series: | Clinical and Translational Science |
| Online Access: | https://doi.org/10.1111/cts.13406 |
| _version_ | 1811177820148203520 |
|---|---|
| author | Rahul K. Goyal Shamir N. Kalaria Susan L. McElroy Mathangi Gopalakrishnan |
| author_facet | Rahul K. Goyal Shamir N. Kalaria Susan L. McElroy Mathangi Gopalakrishnan |
| author_sort | Rahul K. Goyal |
| collection | DOAJ |
| description | 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. Nine patient prerandomization variables were included as predictors. Patients were divided into training and testing subsets according to an 75%:25% distribution while preserving the proportion of placebo responders. All analysis was performed in the software Pumas 2.0. Gaussian Naïve Bayes algorithm showed the best cross‐validation accuracy (~64%) and was chosen as the final algorithm. Shapley analysis suggested that patients with low baseline BEF and anxiety status were strong moderators of placebo response. Upon applying the final algorithm on the test dataset, the resulting sensitivity was 88% and prediction accuracy was 72%. This is the first application of ML to identify moderators of placebo response in BED. The results of this analysis confirm previous findings of lesser baseline disease severity and adds that patients with no anxiety are more susceptible to placebo response. |
| first_indexed | 2024-04-11T06:07:50Z |
| format | Article |
| id | doaj.art-92955ec62b284fdb9875437b2ef1d618 |
| institution | Directory Open Access Journal |
| issn | 1752-8054 1752-8062 |
| language | English |
| last_indexed | 2024-04-11T06:07:50Z |
| publishDate | 2022-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Clinical and Translational Science |
| spelling | doaj.art-92955ec62b284fdb9875437b2ef1d6182022-12-22T04:41:26ZengWileyClinical and Translational Science1752-80541752-80622022-12-0115122878288710.1111/cts.13406An exploratory machine learning approach to identify placebo responders in pharmacological binge eating disorder trialsRahul K. Goyal0Shamir N. Kalaria1Susan L. McElroy2Mathangi Gopalakrishnan3Center for Translational Medicine School of Pharmacy, University of Maryland Baltimore Maryland USACenter for Translational Medicine School of Pharmacy, University of Maryland Baltimore Maryland USADepartment of Psychiatry and Behavioral Neuroscience University of Cincinnati, College of Medicine Mason Ohio USACenter for Translational Medicine School of Pharmacy, University of Maryland Baltimore Maryland USAAbstract 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. Nine patient prerandomization variables were included as predictors. Patients were divided into training and testing subsets according to an 75%:25% distribution while preserving the proportion of placebo responders. All analysis was performed in the software Pumas 2.0. Gaussian Naïve Bayes algorithm showed the best cross‐validation accuracy (~64%) and was chosen as the final algorithm. Shapley analysis suggested that patients with low baseline BEF and anxiety status were strong moderators of placebo response. Upon applying the final algorithm on the test dataset, the resulting sensitivity was 88% and prediction accuracy was 72%. This is the first application of ML to identify moderators of placebo response in BED. The results of this analysis confirm previous findings of lesser baseline disease severity and adds that patients with no anxiety are more susceptible to placebo response.https://doi.org/10.1111/cts.13406 |
| spellingShingle | Rahul K. Goyal Shamir N. Kalaria Susan L. McElroy Mathangi Gopalakrishnan An exploratory machine learning approach to identify placebo responders in pharmacological binge eating disorder trials Clinical and Translational Science |
| title | An exploratory machine learning approach to identify placebo responders in pharmacological binge eating disorder trials |
| title_full | An exploratory machine learning approach to identify placebo responders in pharmacological binge eating disorder trials |
| title_fullStr | An exploratory machine learning approach to identify placebo responders in pharmacological binge eating disorder trials |
| title_full_unstemmed | An exploratory machine learning approach to identify placebo responders in pharmacological binge eating disorder trials |
| title_short | An exploratory machine learning approach to identify placebo responders in pharmacological binge eating disorder trials |
| title_sort | exploratory machine learning approach to identify placebo responders in pharmacological binge eating disorder trials |
| url | https://doi.org/10.1111/cts.13406 |
| work_keys_str_mv | AT rahulkgoyal anexploratorymachinelearningapproachtoidentifyplaceborespondersinpharmacologicalbingeeatingdisordertrials AT shamirnkalaria anexploratorymachinelearningapproachtoidentifyplaceborespondersinpharmacologicalbingeeatingdisordertrials AT susanlmcelroy anexploratorymachinelearningapproachtoidentifyplaceborespondersinpharmacologicalbingeeatingdisordertrials AT mathangigopalakrishnan anexploratorymachinelearningapproachtoidentifyplaceborespondersinpharmacologicalbingeeatingdisordertrials AT rahulkgoyal exploratorymachinelearningapproachtoidentifyplaceborespondersinpharmacologicalbingeeatingdisordertrials AT shamirnkalaria exploratorymachinelearningapproachtoidentifyplaceborespondersinpharmacologicalbingeeatingdisordertrials AT susanlmcelroy exploratorymachinelearningapproachtoidentifyplaceborespondersinpharmacologicalbingeeatingdisordertrials AT mathangigopalakrishnan exploratorymachinelearningapproachtoidentifyplaceborespondersinpharmacologicalbingeeatingdisordertrials |