Evidence map›Paper›PMID 24416178›Full record

ArticlePloS one2014

The Personalized Advantage Index: translating research on prediction into individualized treatment recommendations. A demonstration.

Robert J DeRubeis, Zachary D Cohen, Nicholas R Forand, Jay C Fournier, Lois A Gelfand, Lorenzo Lorenzo-Luaces

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 178 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
178citing papers in PubMed, 6 pooled it
27.4field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

178 citing papers in PubMed, 6 syntheses or guidelines pooled it, 501 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Pooled it
  7. Trial
  8. Trial
  9. Trial
  10. Trial
  11. Trial
  12. Trial
  13. Trial
  14. Trial
  15. Trial
  16. Trial
  17. Trial
  18. Trial
  19. Trial
  20. Trial

118 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 1 country.

Robert J DeRubeisDepartment of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Zachary D CohenDepartment of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Nicholas R ForandDepartment of Psychiatry, The Ohio State University, Columbus, Ohio, United States of America.
Jay C FournierDepartment of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
Lois A GelfandDepartment of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Lorenzo Lorenzo-LuacesDepartment of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
University of Pennsylvania · USThe Ohio State University · USUniversity of Pittsburgh · US

Funding

PREVENTION OF RECURRENCE IN DEPRESSION WITH DRUGS AND CTR01MH060998 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI DERUBEIS, ROBERT J · 2002 to 2011
$6.5M
Neural Markers of Individual Difference in Emotion Regulation in Depressed AdultsK23MH097889 · NIMH · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI FOURNIER, JAY C · 2013 to 2017
$786k
NIMH NIH HHS 2-R01-MH-060998-06NIMH NIH HHS R01 MH060998
6 · The paper itself

Abstract

backgroundAdvances 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.

objectiveTo illustrate and test a new method for integrating predictive information to aid in treatment selection, using data from a randomized treatment comparison.

methodData 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.

resultsFor 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).

conclusionsThis 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.

Indexed as

Health Planning GuidelinesPrecision MedicineTranslational Research, BiomedicalAntidepressive AgentsCognitive Behavioral TherapyHumansMajor Depressive DisorderPrognosisPsychiatric Status Rating ScalesAntidepressive Agents

Identifiers

PMID24416178
PMCPMC3885521
OpenAlexW2079702662

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.