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

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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.CC BY · PubMed Central

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.CC BY · PubMed Central

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“…out by a computer) that converts well-specified inputs (e.g., predictive features like patient demographics or clinical factors) into a recommendation that an individual should receive one treatment instead of another (e.g., cognitive therapy vs. antidepressant medication; DeRubeis et al., 2014).”
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“Increasing the accepted Type 1 error rate is a strategy sometimes used to identify more interactions as significant in these underpowered studies (e.g., DeRubeis et al., 2014), but this is counter to the usual advice to tighten criteria in reaction to multiplicity of possible tests.”
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“Consistent with recent calls to personalize treatments according to patient characteristics (DeRubeis et al., 2014), this type of research would facilitate the tailoring of psychological interventions to specific individuals.”
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“Here we might simplify the number and nature of treatments by identifying mechanisms of action or algorithms that identify best treatments for certain symptom profiles (e.g. such as seen in Personalized Advantage Index (PAI), DeRubeis, Cohen, Forand, Fournier, & Gelfand et al., 2014).”
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Self-citation
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Background
Benefits of Sequentially Adding Cognitive-Behavioral Therapy or Antidepressant Medication for Adults With Nonremitting Depression
Dunlop, B. W., LoParo, D., Kinkead, B. L., Mletzko, T., Cole, S. P., Nemeroff, C. B., Mayberg, H. S., & Craighead, W. E. (2019) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2018.18091075 · Cited by 69
Intervenciones psicológicas basadas en mindfulness y sus beneficios: estado actual de la cuestión
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“…eficacia de dos técnicas es equivalente a nivel grupal, conocer los perfiles que se pueden beneficiar más de una técnica o de otra, de forma que se pueda maximizar la eficacia, asignando a cada paciente el tratamiento que mejor encaje con sus características pretratamiento (DeRubeis et al., 2014).”
Background
In rape trauma PTSD, patient characteristics indicate which trauma-focused treatment they are most likely to complete
Keefe, J. R., Stirman, S. W., Cohen, Z. D., DeRubeis, R. J., Smith, B. N., & Resick, P. A. (2018) · Depression and Anxiety · doi:10.1002/da.22731 · Cited by 65
“…gap (Cloitre, 2015) and to illustrate methodologies of treatment selection that may ultimately result in better patient engagement and outcomes (DeRubeis et al., 2014; Wallace, Frank, & Kraemer, 2013; Zilcha-Mano et al., 2016), we conducted a comprehensive moderator analysis from a clinical…”
BackgroundMethodResultInfluentialSelf-citation
Personalized Medicine and Cognitive Behavioral Therapies for Depression: Small Effects, Big Problems, and Bigger Data
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Self-citation
A Randomized Controlled Trial in Routine Clinical Practice Comparing Acceptance and Commitment Therapy with Cognitive Behavioral Therapy for the Treatment of Major Depressive Disorder
A-Tjak, J. G. L., Morina, N., Topper, M., & Emmelkamp, P. M. G. (2018) · Psychotherapy and Psychosomatics · doi:10.1159/000486807 · Cited by 61
Cross-trial prediction in psychotherapy: External validation of the Personalized Advantage Index using machine learning in two Dutch randomized trials comparing CBT versus IPT for depression
van Bronswijk, S. C., Bruijniks, S. J. E., Lorenzo‐Luaces, L., DeRubeis, R. J., Lemmens, L. H. J. M., Peeters, F. P. M. L., & Huibers, M. J. H. (2020) · Psychotherapy Research · doi:10.1080/10503307.2020.1823029 · Cited by 59
Self-citation
Predicting personalized process-outcome associations in psychotherapy using machine learning approaches-A demonstration.
Rubel, J. A., Zilcha-Mano, S., Giesemann, J., Prinz, J., & Lutz, W. (2020) · Psychotherapy Research · doi:10.1080/10503307.2019.1597994 · Cited by 56
“The PAI predicts the optimal treatment for new patients based on information on patients who have previously been treated (e.g., DeRubeis et al., 2014; Deisenhofer et al., 2018).”
BackgroundMethodInfluential
Dropping out of a transdiagnostic online intervention: A qualitative analysis of client's experiences
Fernández-Álvarez, J., Díaz-García, A., González-Robles, A., Baños, R., García-Palacios, A., & Botella, C. (2017) · Internet Interventions · doi:10.1016/j.invent.2017.09.001 · Cited by 60
“Similar to face-to-face approaches, if technological advancements make it possible, algorithms to generate expected outcome curves could make useful contributions to personalizing program contents and to better specifying all assessment processes (DeRubeis et al., 2014; Delgadillo et al., 2016).”
Background
One year follow-up and mediation in cognitive behavioral therapy and acceptance and commitment therapy for adult depression
A-Tjak, J. G. L., Morina, N., Topper, M., & Emmelkamp, P. M. G. (2021) · BMC Psychiatry · doi:10.1186/s12888-020-03020-1 · Cited by 57
Defining and Predicting Patterns of Early Response in a Web-Based Intervention for Depression
Lutz, W., Arndt, A., Rubel, J. A., Berger, T., Schröder, J., Späth, C., Meyer, B., Greiner, W., Gräfe, V., Hautzinger, M., et al. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7367 · Cited by 58
Miles to Go Before We Sleep: Advancing the Understanding of Psychotherapy by Modeling Complex Processes
Lorenzo-Luaces, L., & DeRubeis, R. J. (2018) · Cognitive Therapy and Research · doi:10.1007/s10608-018-9893-x · Cited by 56
Self-citation
Reducing Dropout in Treatment for Depression: Translating Dropout Predictors Into Individualized Treatment Recommendations
Zilcha-Mano, S., Keefe, J. R., Chui, H., Rubin, A., Barrett, M. S., & Barber, J. P. (2016) · The Journal of Clinical Psychiatry · doi:10.4088/jcp.15m10081 · Cited by 55
Predicting treatment outcome in depression: an introduction into current concepts and challenges
Rost, N., Binder, E. B., & Brückl, T. M. (2022) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/s00406-022-01418-4 · Cited by 66
Is it worth it to personalize the treatment of PTSD? – A variance-ratio meta-analysis and estimation of treatment effect heterogeneity in RCTs of PTSD
Herzog, P., & Kaiser, T. (2022) · Journal of Anxiety Disorders · doi:10.1016/j.janxdis.2022.102611 · Cited by 64
The Potential Role of Learning Capacity in Cognitive Behavior Therapy for Depression: A Systematic Review of the Evidence and Future Directions for Improving Therapeutic Learning
Bruijniks, S. J. E., DeRubeis, R. J., Hollon, S. D., & Huibers, M. J. H. (2019) · Clinical Psychological Science · doi:10.1177/2167702619830391 · Cited by 57
Self-citation
Isolating the Unique Effects of the Unified Protocol Treatment Modules Using Single Case Experimental Design
Sauer-Zavala, S. E., Cassiello-Robbins, C. F., Conklin, L. R., Bullis, J. R., Thompson‐Hollands, J., & Kennedy, K. (2016) · Behavior Modification · doi:10.1177/0145445516673827 · Cited by 54
“One possibility for personalization involves using any baseline and demographic data that may be predictive to develop an algorithm to match patients to modules that will likely lead to the greatest symptom improvement (e.g., DeRubeis et al., 2014).”
How data science can advance mental health research
Russ, T. C., Woelbert, E., Davis, K. A. S., Hafferty, J. D., Ibrahim, Z. M., Inkster, B., John, A. M., Lee, W. E., Maxwell, M., McIntosh, A. M., et al. (2018) · Nature Human Behaviour · doi:10.1038/s41562-018-0470-9 · Cited by 65
Predicting cognitive behavioral therapy outcome in the outpatient sector based on clinical routine data: A machine learning approach
Hilbert, K., Kunas, S. L., Lueken, U., Kathmann, N., Fydrich, T., & Fehm, L. (2019) · Behaviour Research and Therapy · doi:10.1016/j.brat.2019.103530 · Cited by 54
Predicting treatment outcome in psychological treatment services by identifying latent profiles of patients
Saunders, R., Cape, J. D., Fearon, P. R. M., & Pilling, S. (2016) · Journal of Affective Disorders · doi:10.1016/j.jad.2016.03.011 · Cited by 53
A genome-wide association meta-analysis of prognostic outcomes following cognitive behavioural therapy in individuals with anxiety and depressive disorders
Rayner, C., Coleman, J. R. I., Purves, K. L., Hodsoll, J. P., Goldsmith, K., Alpers, G. W., Andersson, E., Arolt, V., Boberg, J., Bögels, S. M., et al. (2019) · Translational Psychiatry · doi:10.1038/s41398-019-0481-y · Cited by 57
Predictors and mediators of outcome in cognitive behavioral therapy for chronic pain: The contributions of psychological flexibility
Åkerblom, S., Perrin, S., Rivano Fischer, M., & McCracken, L. M. (2021) · Journal of Behavioral Medicine · doi:10.1007/s10865-020-00168-9 · Cited by 52
“It is expected that this information will help to refine and personalize existing treatments (DeRubeis et al., 2014; Gilpin, Keyes, Stahl, Greig, & McCracken, 2017; Kraemer, Wilson, Fairburn, & Agras, 2002; Turner et al., 2007; Williams et al., 2012).”
Background
The human connectome in health and psychopathology
Essen, D. C. V., & Barch, D. M. (2015) · World Psychiatry · doi:10.1002/wps.20228 · Cited by 52
Digital Technology and Clinical Decision-Making in Depression Treatment: Current Findings and Future Opportunities
Hallgren, K. A., Bauer, A. M., & Atkins, D. C. (2017) · Depression and Anxiety · doi:10.1002/da.22640 · Cited by 45
“Research suggests that individual patient characteristics can predict responses to different depression treatments (DeRubeis et al., 2014); however, developing more personalized depression treatments will likely require “big data” on clinical outcomes across large combinations depression treatment…”
Background
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Richards, D. A., Rhodes, S., Ekers, D., McMillan, D., Taylor, R. S., Byford, S., Barrett, B. M., Finning, K., Ganguli, P., Warren, F. C., et al. (2017) · Health Technology Assessment · doi:10.3310/hta21460 · Cited by 56
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“…time series and network analyses rather than focusing on baseline variables (Hayes et al., 2019), use of the dynamic p-technique to flexibly generalize individuallevel inferences to the group (Kurz et al., 2019) and the personalized advantage index (Cohen & DeRubeis, 2018; DeRubeis et al., 2014).”
Method
Treatment selection in borderline personality disorder between dialectical behavior therapy and psychodynamic psychiatric management
Keefe, J. R., Kim, T. T., DeRubeis, R. J., Streiner, D. L., Links, P. S., & McMain, S. (2020) · Psychological Medicine · doi:10.1017/s0033291720000550 · Cited by 50
Self-citation
Psychotherapy and Neuroimaging
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Tourism and depressive symptoms
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Latent variable mixture modelling and individual treatment prediction
Saunders, R., Buckman, J. E. J., & Pilling, S. (2019) · Behaviour Research and Therapy · doi:10.1016/j.brat.2019.103505 · Cited by 53
Psychotherapy or medication for depression? Using individual symptom meta-analyses to derive a Symptom-Oriented Therapy (SOrT) metric for a personalised psychiatry
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Dismantling, personalising and optimising internet cognitive–behavioural therapy for depression: a study protocol for individual participant data component network meta-analysis
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The history, current status, and possible future of precision mental health
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Self-citation
The importance of transdiagnostic symptom level assessment to understanding prognosis for depressed adults: analysis of data from six randomised control trials
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Self-citation
What Four Decades of Meta-Analysis Have Taught Us About Youth Psychotherapy and the Science of Research Synthesis
Weisz, J. R., Venturo‐Conerly, K. E., Fitzpatrick, O. M., Frederick, J., & Ng, M. Y. (2023) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-080921-082920 · Cited by 52
Adding mindfulness-based cognitive therapy to maintenance antidepressant medication for prevention of relapse/recurrence in major depressive disorder: Randomised controlled trial
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Cognitive and Interpersonal Vulnerabilities to Adolescent Depression: Classification of Risk Profiles for a Personalized Prevention Approach
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“…Index (PAI) have used machine learning techniques to analyze whether individual differences, assessed before depression treatment initiation in their RCTs for adult major depression, serve as significant post-hoc moderators of treatment efficacy (DeRubeis et al. 2014; Huibers et al. 2015).”
Not Just a Non-specific Factor: Moderators of the Effect of Within- and Between-Clients Alliance on Outcome in CBT
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A Machine Learning Approach to Identifying Placebo Responders in Late-Life Depression Trials
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“Novel, systematic approaches to subgroup analysis have recently shown differential effects for different treatment conditions across patient subgroups (17-18).”
BackgroundMethodResult
Long-term outcomes of acute treatment with cognitive therapyv.interpersonal psychotherapy for adult depression: follow-up of a randomized controlled trial
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Routine Cognitive Behavioural Therapy for Anxiety and Depression is More Effective at Repairing Symptoms of Psychopathology than Enhancing Wellbeing
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Cognitive and emotional variables predicting treatment outcome of cognitive behavior therapies for patients with medically unexplained symptoms: A meta-analysis.
Sarter, L., Heider, J., Kirchner, L., Schenkel, S., Witthöft, M., Rief, W., & Kleinstäuber, M. (2021) · Journal of Psychosomatic Research · doi:10.1016/j.jpsychores.2021.110486 · Cited by 38

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: DeRubeis, Robert J.
Cohen, Zachary D.
Forand, Nicholas R.
Fournier, Jay C.
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: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
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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The Personalized Advantage Index: Translating Research on Prediction into Individualized Treatment Recommendations. A Demonstration

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