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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“At the same time, there has been a growing body of research supporting the need for personalization of treatment based on the patients’ baseline characteristics, through the selection and adaptation of interventions (DeRubeis et al., 2014).”
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Bayesian Predictive Modeling for Genomic Based Personalized Treatment Selection
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“We further conducted leave-one-out cross-validation (LOOCV) analyses using the summary measure as the criteria for model (rank) selection (Foster et al., 2011; DeRubeis et al., 2014).”
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“estimated benefit that a patient will have from a certain treatment (DeRubeis et al., 2014; Lorenzo-Luaces, DeRubeis, van Straten, & Tiemens, 2017; Lutz, Zimmermann, Müller, Deisenhofer, & Rubel, 2017).”
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“A PAI of zero implies no predicted difference in outcome between two treatments and therefore no advantage of one treatment over another for an individual patient.”
BackgroundMethodInfluential
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Background
Retos en el desarrollo de intervenciones psicológicas y la práctica asistencial en salud mental
Tortella‐Feliu, M., Vázquez, C., Valiente, C., Quero, S., Soler, J., Montorio, I., Jiménez‐Múrcia, S., Hervás, G., García‐Palacios, A., García‐Campayo, J., et al. (2016) · Clínica y Salud · doi:10.1016/j.clysa.2016.03.002 · Cited by 15
“…muestran que se consiguen índices de éxito significativamente mayores cuando los pacientes reciben el tratamiento “óptimo” para su caso concreto (DeRubeis et al., 2014). b. Otra de las vías que se ha ido apuntando tiene que ver con el desarrollo de nuevas aproximaciones al tratamiento que se…”
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“…mechanisms account for patient improvement in different treatments, researchers must clarify these to further our understanding of the various causes of psychopathology and help us choose the most suitable treatment for each patient presentation (Barber and Muenz, 1996; DeRubeis et al., 2014).”
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The Appalachia Mind Health Initiative (AMHI): a pragmatic randomized clinical trial of adjunctive internet-based cognitive behavior therapy for treating major depressive disorder among primary care patients
Bossarte, R. M., Kessler, R. C., Nierenberg, A. A., Chattopadhyay, A., Cuijpers, P. J., Enrique, Á., Foxworth, P. M., Gildea, S. M., Belnap, B. H., Haut, M. W., et al. (2022) · Trials · doi:10.1186/s13063-022-06438-y · Cited by 9
Predictors and moderators of three online interventions for eating disorder symptoms in a randomized controlled trial
Rohrbach, P. J., Fokkema, M., Spinhoven, P., van Furth, E. F., & Dingemans, A. E. (2023) · International Journal of Eating Disorders · doi:10.1002/eat.24021 · Cited by 10
Identifying who benefits most from supportive versus expressive techniques in psychotherapy for depression: Moderators of within- versus between-individual effects.
Zilcha‐Mano, S., & Webb, C. A. (2023) · Journal of Consulting and Clinical Psychology · doi:10.1037/ccp0000868 · Cited by 9
Individual treatment selection for patients with post-traumatic stress disorder: External validation of a personalised advantage index
Tait, J., Kellett, S., Saxon, D. H., Deisenhofer, A., Lutz, W., Barkham, M., & Delgadillo, J. (2024) · Psychotherapy Research · doi:10.1080/10503307.2024.2360449 · Cited by 12
“To address this, DeRubeis et al. (2014) developed a method called the Personalised Advantage Index (PAI).”
Early Symptom Trajectories as Predictors of Treatment Outcome for Citalopram Versus Placebo
Zilcha‐Mano, S., Roose, S. P., Brown, P. J., & Rutherford, B. R. (2017) · American Journal of Geriatric Psychiatry · doi:10.1016/j.jagp.2017.02.001 · Cited by 10
Cognitive Behavior Therapy for Depression From an Evolutionary Perspective
Hollon, S. D., Andrews, P. W., & Thomson, J. A. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.667592 · Cited by 11
“About 30% of patients with more severe depression are more likely to respond to ADM than to CBT and a different 30% of the more severely depressed show the opposite pattern (83).”
Background
Predictors and moderators of treatment outcome for single incident paediatric PTSD: a multi-centre randomized clinical trial
de Roos, C., Zijlstra, B. J. H., Perrin, S. G., der Oord, S. V., Lucassen, S., Emmelkamp, P. M. G., & de Jongh, A. (2021) · European Journal of Psychotraumatology · doi:10.1080/20008198.2021.1968138 · Cited by 9
“Future studies may also consider combining baseline patient characteristics to create a single strong moderator as a more powerful and precise measure to detect differential treatment responses (Wallace, Frank, & Kraemer, 2013) or consider the use of the Personalized Advantage Index approach (PAI; DeRubeis et al., 201…”
Background
Study protocol for pragmatic trials of Internet-delivered guided and unguided cognitive behavior therapy for treating depression and anxiety in university students of two Latin American countries: the Yo Puedo Sentirme Bien study
Benjet, C. L., Kessler, R. C., Kazdin, A. E., Cuijpers, P. J., Albor, Y., Tapias, N. E. C., Contreras‐Ibáñez, C. C., González, M. S. D., Gildea, S. M., González, N., et al. (2022) · Trials · doi:10.1186/s13063-022-06255-3 · Cited by 10
“The difference score, sometimes referred to in linear interaction models as the personalized advantage index (PAI) [93], quantifies the effect of using a precision treatment assignment rule to determine which patients should receive which treatments.”
Background
Separation Anxiety Symptom Profiles and Parental Accommodation Across Pediatric Anxiety Disorders
Phillips, K. E., Norris, L. A., & Kendall, P. C. (2020) · Child Psychiatry & Human Development · doi:10.1007/s10578-019-00949-7 · Cited by 8
Individual psychotherapy for cluster-C personality disorders: protocol of a pragmatic RCT comparing short-term psychodynamic supportive psychotherapy, affect phobia therapy and schema therapy (I-FORCE)
Daniëls, M., Van, H. L., van den Heuvel, B., Dekker, J. J. M., Peen, J., Bosmans, J., Arntz, A., & Huibers, M. J. H. (2023) · Trials · doi:10.1186/s13063-023-07136-z · Cited by 9
“Bruijniks SJ, van Bronswijk SC, DeRubeis RJ, Delgadillo J, Cuijpers P, Huibers MJ. Individual differences in response to once versus twice weekly sessions of CBT and IPT for depression.”
Method
Developing an individualized treatment rule for Veterans with major depressive disorder using electronic health records
Zainal, N. H., Bossarte, R. M., Gildea, S. M., Hwang, I. H., Kennedy, C. J., Liu, H., Leung, L. B., Luedtke, A. R., Marx, B. P., Petukhova, M. V., et al. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02500-0 · Cited by 9
Voorbij het oordeel van de dodo
Huibers, M. J. H. (2015) · Tijdschrift voor Psychotherapie · doi:10.1007/s12485-015-0027-6 · Cited by 8
Studying Effects and Process in Psychotherapy for Personality Disorders
Caspar, F. (2018) · Psychopathology · doi:10.1159/000487895 · Cited by 8
Investigating patient-specific mechanisms of change in SET vs. EFT for depression: study protocol for a mechanistic randomized controlled trial
Zilcha‐Mano, S., Shahar, B., Fisher, H., Dolev‐Amit, T., Greenberg, L. S., & Barber, J. P. (2021) · BMC Psychiatry · doi:10.1186/s12888-021-03279-y · Cited by 8
TFP Extended: Development and Recent Advances
Clarkin, J. F., Caligor, E., & Sowislo, J. F. (2021) · Psychodynamic Psychiatry · doi:10.1521/pdps.2021.49.2.188 · Cited by 9
Development of the C4 Inventory: A Measure of Common Characteristics that Complicate Care in Outpatient Psychiatry
Maunder, R., Wiesenfeld, L. A., Rawkins, S., & Park, J. (2016) · Journal of Comorbidity · doi:10.15256/joc.2016.6.66 · Cited by 7
Baseline Client Interpersonal Agency Moderates the Indirect Effect of Treatment on Long-term Worry in Variants of CBT for Generalized Anxiety Disorder
Penedo, J. M. G., Constantino, M. J., Coyne, A. E., Romano, F. M., Westra, H. A., & Antony, M. M. (2019) · Behavior Therapy · doi:10.1016/j.beth.2019.01.007 · Cited by 9
Recognizing individual variability in misophonia: Identifying symptom-based subgroups with Gaussian mixture modeling
Woolley, M. G., Ramos, A. M., Bowers, E. M., Muñoz, K. F., Petersen, J. M., & Twohig, M. P. (2025) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2025.02.054 · Cited by 7
“For example, the Personalized Advantage Index (PAI; DeRubeis et al., 2014) demonstrates the potential of predictive modeling to allocate individuals to treatments likely to yield the greatest benefit.”
Developing a multivariable prediction model to support personalized selection among five major empirically-supported treatments for adult depression. Study protocol of a systematic review and individual participant data network meta-analysis
Driessen, E., Efthimiou, O., Wienicke, F. J., Breunese, J., Cuijpers, P. J., Debray, T. P. A., Fisher, D. J., Fokkema, M., Furukawa, T. A., Hollon, S. D., et al. (2025) · PLoS ONE · doi:10.1371/journal.pone.0322124 · Cited by 9
“Concep-tually, this evaluation will follow the Personalized Advantage Index approach described by DeRubeis and colleagues [71] in which outcomes of participants who received the treatment recommended by the model are compared against the outcomes of participants who received a non-recommended…”
Self-citation
Outcomes of People of Color in an Efficacy Trial of Cognitive-Behavioral Treatments for Anxiety, Depression, and Related Disorders
Cardona, N. D., Ametaj, A., Cassiello-Robbins, C. F., Wilner, J. G., Olesnycky, O. S., Sauer-Zavala, S. E., Farchione, T. J., & Barlow, D. H. (2023) · The Journal of Nervous and Mental Disease · doi:10.1097/nmd.0000000000001692 · Cited by 6
Effectiveness of Cognitive Behavioral Group Therapy, Psychodrama, and Their Integration for Treatment of Social Anxiety Disorder: A Randomized Controlled Trial
Abeditehrani, H., Dijk, C., Neyshabouri, M. D., & Arntz, A. R. (2021) · Journal of Behavior Therapy and Experimental Psychiatry · doi:10.1016/j.jbtep.2023.101908 · Cited by 7
Interpersonal pathoplasticity and trajectories of change in routine adolescent and young adult residential substance abuse treatment.
Boswell, J. F., Cain, N. M., Oswald, J. M., McAleavey, A. A., & Adelman, R. W. (2017) · Journal of Consulting and Clinical Psychology · doi:10.1037/ccp0000219 · Cited by 7
“Consistent with increased calls for personalized medicine (DeRubeis et al., 2014; Fisher & Boswell, 2016; Hamburg & Collins, 2010), a focus on patient variables, for example, facilitates process investigations of what works for whom (Castonguay & Beutler, 2006).”
Background
What sticks? Patients’ perspectives on treatment three years after psychotherapy: A mixed-methods approach
Wucherpfennig, F., Boyle, K. S., Rubel, J. A., Weinmann‐Lutz, B., & Lutz, W. (2019) · Psychotherapy Research · doi:10.1080/10503307.2019.1671630 · Cited by 7
“In line with other researchers (Cohen & DeRubeis, 2018; DeRubeis et al., 2014), we assume that the interaction of patient and treatment characteristics may result in differential response patterns.”
Result
Precision Psychiatry: Using Neuroscience Insights to Inform Personally Tailored, Measurement-Based Care
Hack, L. M. (2021) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9781615379118 · Cited by 8
Integrating genomic signatures for treatment selection with Bayesian predictive failure time models
Ma, J., Hobbs, B. P., & Stingo, F. C. (2016) · Statistical Methods in Medical Research · doi:10.1177/0962280216675373 · Cited by 7
“For implementation of the RAFT and OTR methods, we used the LOOCV procedure that is similar to that described by Derubeis et al.32 We implemented the proposed BPFT approach by first selecting the optimal number of clusters (rank) via evaluating the 5-year restricted mean survival times (RMSTs) on the training data wit…”
Patient-appraised beneficial moments during inpatient psychiatric treatment
Locher, C., Mansour, R., Koechlin, H., & Büchi, S. (2020) · BMC Health Services Research · doi:10.1186/s12913-020-05617-4 · Cited by 7
“Furthermore, there has been an increased interest on the use of patient characteristics in order to match patients to the treatments that might be most suitable to them [43, 44].”
Background
Making Therapy Widely Available: Clinical Research Triumph or Existential Catastrophe?
Rush, A. J. (2022) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2021.21121201 · Cited by 7
How, When, and Why Do People Change Through Psychological Interventions?—Patient-Focused Psychotherapy Research
Rubel, J. A., & Lutz, W. (2017) · Springer eBooks · doi:10.1007/978-3-319-50675-3_13 · Cited by 7
Development of a model to predict combined antidepressant medication and psychotherapy treatment response for depression among veterans
Bossarte, R. M., Ross, E. L., Liu, H., Turner, B., Bryant, C., Zainal, N. H., Puac‐Polanco, V., Ziobrowski, H. N., Cui, R., Cipriani, A., et al. (2023) · Journal of Affective Disorders · doi:10.1016/j.jad.2023.01.082 · Cited by 6
Is personalization of psychological pain treatments necessary? Evidence from a Bayesian variance ratio meta-analysis
Scholten, S., Herzog, P., Glombiewski, J. A., & Kaiser, T. (2024) · Pain · doi:10.1097/j.pain.0000000000003363 · Cited by 9
Initial evaluation of a personalized advantage index to determine which individuals may benefit from mindfulness-based cognitive therapy for suicide prevention
Myers, C. E., Dave, C. V., Chesin, M. S., Marx, B. P., Hill, L. M. S., Reddy, V., Miller, R. B., King, A. R., & Interian, A. (2024) · Behaviour Research and Therapy · doi:10.1016/j.brat.2024.104637 · Cited by 7
Looking into the Future : How to Use Advanced Statistical Methods for Predicting Psychotherapy Outcomes in Routine Care
Uckelstam, C.-J. (2021) · Linköping studies in behavioural science · doi:10.3384/diss.diva-172754 · Cited by 6
Patients’ symptoms and strengths as predictors of long-term outcomes of CBT for generalized anxiety disorder – A three-level, multi-predictor analysis
Probst, G., Vîslă, A., & Flückiger, C. (2022) · Journal of Anxiety Disorders · doi:10.1016/j.janxdis.2022.102635 · Cited by 6
Behavioural and nondirective parent training for children with externalising disorders: First steps towards personalised treatment recommendations
Hautmann, C., Dose, C., Hellmich, M., Scholz, K., Katzmann, J., Pinior, J., Gebauer, S., Nordmann, L., Metternich-Kaizman, T. W., Schürmann, S., et al. (2023) · Behaviour Research and Therapy · doi:10.1016/j.brat.2023.104271 · Cited by 6
Personalised psychotherapy in primary care: evaluation of data-driven treatment allocation to cognitive–behavioural therapy versus counselling for depression
Bauer‐Staeb, C., Griffith, E., Faraway, J. J., & Button, K. S. (2023) · BJPsych Open · doi:10.1192/bjo.2022.628 · Cited by 6
Advantages of developing clinical practice guidelines using international standards.
Hollon, S. D., & Teachman, B. A. (2019) · Psychotherapy · doi:10.1037/pst0000240 · Cited by 6
Trajectories of change in chronic depression: Differences in self-criticism and somatic symptoms between users of antidepressants and nonmedicated patients.
Høstmælingen, A., Ulvenes, P., Nissen‐Lie, H. A., Eielsen, M., & Wampold, B. E. (2021) · Journal of Counseling Psychology · doi:10.1037/cou0000572 · Cited by 5
Using Personalized Assessment and Network Model Feedback in Psychotherapy: Proof of Principle for the TheraNet ProjectPreprint
Hall, M., Wagner, A. A., Scherner, P. V., Michael, K. L., Lawyer, G., Lutz, W., & Rubel, J. A. (2022) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/8deyj · Cited by 8
Feedback-Informed Psychological Therapy in Practice: The Trier Treatment Navigator (TTN)
Lutz, W., Danilo Moggia, N., Bommer, J., Schaffrath, J., Deisenhofer, A., Eberhardt, S. T., Vehlen, A., Schwartz, B., & Rubel, J. A. (2025) · CBT: science into practice · doi:10.1007/978-3-031-90428-8_2 · Cited by 5
Psychotherapy process research: Identifying productive in-session processes to enhance treatment outcomes and therapist responsiveness
Watson, J. C. (2023) · Psychotherapy Research · doi:10.1080/10503307.2023.2252160 · Cited by 5
“…et al., 2009, 2011a) building links between clients’ pre-therapy characteristics, in-session process, post-session outcome and final therapy outcome (DeRubeis et al., 2014; Watson & Greenberg, 1996a; Wiseman & Rice, 1989); and naturalistic observation and task analysis to study client and…”
Differential response to depression prevention among a sample of informal caregivers: Moderator analysis of longer-term follow-up trial data
Otero, P., Smit, F., Cuijpers, P. J., DeRubeis, R. J., Torres, Á., & Vázquez, F. L. (2015) · Psychiatry Research · doi:10.1016/j.psychres.2015.09.005 · Cited by 5
Self-citation
A comparison of prolonged exposure therapy, pharmacotherapy, and their combination for PTSD: What works best and for whom; study protocol for a randomized trial
Bredemeier, K., Larsen, S. E., Shivakumar, G., Grubbs, K. M., McLean, C. P., Tress, C. R., Rosenfield, D., DeRubeis, R. J., Xu, C., Foa, E. B., et al. (2022) · Contemporary Clinical Trials · doi:10.1016/j.cct.2022.106850 · Cited by 5
Self-citation
G-FORCE: the effectiveness of group psychotherapy for Cluster-C personality disorders: protocol of a pragmatic RCT comparing psychodynamic and two forms of schema group therapy
van den Heuvel, B. B., Dekker, J., Daniëls, M., Van, H. L., Peen, J., Bosmans, J. E., Arntz, A. R., & Huibers, M. J. H. (2023) · Trials · doi:10.1186/s13063-023-07309-w · Cited by 6
“Bruijniks SJ, van Bronswijk SC, DeRubeis RJ, Delgadillo J, Cuijpers P, Huibers MJ. Individual differences in response to once versus twice weekly sessions of CBT and IPT for depression.”
Method
Revealing subgroup-specific mechanisms of change via moderated mediation: A meditation intervention example.
Webb, C. A., Hirshberg, M. J., González, O., Davidson, R. J., & Goldberg, S. B. (2023) · Journal of Consulting and Clinical Psychology · doi:10.1037/ccp0000842 · Cited by 6
“For example, DeRubeis et al. (2014) developed a predictive algorithm (“Personalized Advantage Index”; PAI), based on pre-treatment patient characteristics, which identified subgroups of individuals who derived greater benefit from CBT vs. an antidepressant medication.”
Method
Predicting optimal treatment outcomes in phase‐based treatment and direct trauma‐focused treatment among patients with posttraumatic stress disorder stemming from childhood abuse
Bremer, S., van Vliet, N. I., van Bronswijk, S. C., Huntjens, R. J. C., de Jongh, A., & van Dijk, M. K. (2023) · Journal of Traumatic Stress · doi:10.1002/jts.22980 · Cited by 5
Compatibility of Components in Cognitive Behavioral Therapies: A Call for Combinatory Congruency
O’Toole, M. S., Mikkelsen, M. B., Arch, J. J., Tauber, N. M., Elkjær, E., & Michalak, J. (2024) · Cognitive and Behavioral Practice · doi:10.1016/j.cbpra.2023.12.006 · Cited by 5
If You Give a Therapist a Network: A Qualitative Analysis of Therapists' Reactions to Their Patients' EMA‐Based Network Models
Hall, M., Wagner, A. A., Scherner, P. V., & Rubel, J. A. (2024) · Clinical Psychology & Psychotherapy · doi:10.1002/cpp.70013 · Cited by 9

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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