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Cited by 509Treatment of Major DepressionPsychotherapy Techniques and ApplicationsStatistical Methods in Clinical Trials

The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.

The Personalized Advantage Index: Translating Research on Prediction into Individualized Treatment Recommendations. A Demonstration

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DeRubeis, Robert J., Cohen, Zachary D., Forand, Nicholas R., Fournier, Jay C., Gelfand, Lois A., Lorenzo-Luaces, Lorenzo·2014·PLoS ONE
DOI 10.1371/journal.pone.0083875PMC PMC3885521
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Abstract

BACKGROUND: Advances in personalized medicine require the identification of variables that predict differential response to treatments as well as the development and refinement of methods to transform predictive information into actionable recommendations. OBJECTIVE: To illustrate and test a new method for integrating predictive information to aid in treatment selection, using data from a randomized treatment comparison. METHOD: Data from a trial of antidepressant medications (N = 104) versus cognitive behavioral therapy (N = 50) for Major Depressive Disorder were used to produce predictions of post-treatment scores on the Hamilton Rating Scale for Depression (HRSD) in each of the two treatments for each of the 154 patients. The patient's own data were not used in the models that yielded these predictions. Five pre-randomization variables that predicted differential response (marital status, employment status, life events, comorbid personality disorder, and prior medication trials) were included in regression models, permitting the calculation of each patient's Personalized Advantage Index (PAI), in HRSD units. RESULTS: For 60% of the sample a clinically meaningful advantage (PAI≥3) was predicted for one of the treatments, relative to the other. When these patients were divided into those randomly assigned to their "Optimal" treatment versus those assigned to their "Non-optimal" treatment, outcomes in the former group were superior (d = 0.58, 95% CI .17-1.01). CONCLUSIONS: This approach to treatment selection, implemented in the context of two equally effective treatments, yielded effects that, if obtained prospectively, would rival those routinely observed in comparisons of active versus control treatments.

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Figure 1 Frequency histogram showing predicted end-HRSD scores for each patient in their Optimal and their Non-Optimal treatment, as indicated by the treatment selection algorithm.
Figure 1 Frequency histogram showing predicted end-HRSD scores for each patient in their Optimal and their Non-Optimal treatment, as indicated by the treatment selection algorithm.CC BY · PubMed Central
Figure 2 Frequency histogram showing Personalized Advantage Index (PAI) scores for all patients in the sample.
Figure 2 Frequency histogram showing Personalized Advantage Index (PAI) scores for all patients in the sample.CC BY · PubMed Central
Figure 3 Comparison of mean end-HRSD scores for patients randomly assigned to their Optimal treatment versus those assigned to their Non-Optimal treatment. The left side gives the results for the full sample. The right side includes only patients for whom the algorithm predicted a clinically significant advantage on the PAI of ≥3.
Figure 3 Comparison of mean end-HRSD scores for patients randomly assigned to their Optimal treatment versus those assigned to their Non-Optimal treatment. The left side gives the results for the full sample. The right side includes only patients for whom the algorithm predicted a clinically significant advantage on the PAI of ≥3.CC BY · PubMed Central

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Metadata

Authors
DeRubeis, R. J., Cohen, Z. D., Forand, N. R., Fournier, J. C., Gelfand, L. A., & Lorenzo-Luaces, L.
Affiliations
  • University of Pennsylvania (US)
  • The Ohio State University (US)
  • University of Pittsburgh (US)
Corresponding author
DeRubeis, Robert J.
ORCID
Venue
PLoS ONE
Publisher
Public Library of Science (PLoS)
Published
2014-01-08
Volume
9
Issue
1
Pages
e83875
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0083875
PMC
PMC3885521
OpenAlex
W2079702662
Cited by
509
References
42
Citations by year
2026: 36 · 2025: 25 · 2024: 33 · 2023: 41 · 2022: 52 · 2021: 99 · 2020: 46 · 2019: 51 · 2018: 34 · 2017: 35 · 2016: 26 · 2015: 23 · 2014: 8
FWCI
22.60
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • D-Scholarship@Pitt (University of Pittsburgh) (acceptedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Institute of Mental Health (2-R01-MH-060998-06, R01MH060998)
Topics
  • Health Sciences › Medicine › Pharmacology (medical) › Treatment of Major Depression
  • Social Sciences › Psychology › Clinical Psychology › Psychotherapy Techniques and Applications
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Keywords
personalized medicine, treatment selection, differential treatment response, prediction of treatment outcome, major depressive disorder, antidepressants, cognitive behavioral therapy, Hamilton Depression Rating Scale, treatment response prediction
MeSH terms
Antidepressive Agents, Depressive Disorder, Major, Major Depressive Disorder, Health Planning Guidelines, Humans, Prognosis, Psychiatric Status Rating Scales, Cognitive Behavioral Therapy, Translational Research, Biomedical, Precision Medicine

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