The personalized advantage index: translating research on prediction into individualized treatment recommendations. A demonstration
Open accessAbstract
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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The use of waiting list control groups in psychotherapy research may lead to an overestimation of the effects of treatment.
“The use of waiting list control groups as often the case in psychotherapy research may lead to an overestimation of the effects of treatment. 592,593”
From Time for united action on depression: a Lancet–World Psychiatric Association Commission (2022) · cites this paper as [592]
Literature on patient factors predicting treatment response to MBCT is emerging but still in its infancy, particularly compared to predictors of outcome of cognitive therapy.
“Literature on patient factors predicting treatment response to MBCT is emerging but still in its infancy, particularly compared to predictors of outcome of cognitive therapy (DeRubeis et al., 2014).”
From Adding mindfulness-based cognitive therapy to maintenance antidepressant medication for prevention of relapse/recurrence in major depressive disorder: Randomised controlled trial (2015) · cites this paper as DeRubeis et al., 2014
There is a consensus in the literature that future improvements in outcomes from CBT for chronic pain will require research to identify patient and treatment variables that help explain outcomes.
“There is now a consensus in the literature that future improvements in outcomes obtained from cognitive behavioral therapy (CBT) for chronic pain will require research to identify patient and treatment variables that help explain outcomes.”
From Predictors and mediators of outcome in cognitive behavioral therapy for chronic pain: The contributions of psychological flexibility (2021) · cites this paper as DeRubeis et al., 2014
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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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“…multivariable prediction algorithms have been developed to identify patients who respond differentially to CBT vs. antidepressant medication (DeRubeis et al., 2014), CBT vs. interpersonal psychotherapy (Huibers et al., 2015), CBT vs. eye-movement desensitization and reprocessing…”
MethodResultInfluentialDivergent outcomes in cognitive-behavioral therapy and pharmacotherapy for adult depression
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“Even so, empirical guidance for selecting CBT or pharmacotherapy for a given patient based on average post-treatment symptom scores is available [16,38].”
BackgroundSelf-citationTreatment as usual and routine care in research and clinical practice
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“…with a better prognosis benefit as much from non-specific interventions such as BT as they do from empiricallysupported treatments like CBT but patients with a poorer prognosis require treatments with more active ingredients (e.g., CBT) to experience treatment benefit (see DeRubeis et al. (2014b)).”
BackgroundInfluentialSelf-citationCognitive behavioral therapy: Current status and future research directions
McMain, S., Newman, M. G., Segal, Z. V., & DeRubeis, R. J. (2015) · Psychotherapy Research · doi:10.1080/10503307.2014.1002440 · Cited by 100 · see citing works
“McGrath et al. (2013) used indexes of brain metabolism from positron emission tomography to predict response, and found that hypometabolism in the insula was associated with a better response to CBT while hypermetabolism predicted a better response to ADM. DeRubeis et al. (2014) applied linear regression models to imp…”
BackgroundSelf-citationSchwartz, B., Cohen, Z. D., Rubel, J. A., Zimmermann, D., Wittmann, W. W., & Lutz, W. (2020) · Psychotherapy Research · doi:10.1080/10503307.2020.1769219 · Cited by 93 · see citing works
Self-citationPredictors of Dropout in Internet-Based Cognitive Behavioral Therapy for Depression
Ezawa, I. D., Forand, N. R., & Strunk, D. R. (2018) · Cognitive Therapy and Research · doi:10.1007/s10608-018-9979-5 · Cited by 92 · see citing works
Self-citationMajor developments in methods addressing for whom psychotherapy may work and why
Zilcha‐Mano, S. (2018) · Psychotherapy Research · doi:10.1080/10503307.2018.1429691 · Cited by 88 · see citing works
Zilcha‐Mano, S., & Errázuriz, P. (2015) · Journal of Counseling Psychology · doi:10.1037/cou0000103 · Cited by 88 · see citing works
Individual treatment selection for patients with posttraumatic stress disorder
Deisenhofer, A., Delgadillo, J., Rubel, J. A., Boehnke, J. R., Zimmermann, D., Schwartz, B., & Lutz, W. (2018) · Depression and Anxiety · doi:10.1002/da.22755 · Cited by 85 · see citing works
“Our results are consistent with existing literature in which the PAI predicted a meaningful advantage for approximately 60% of cases receiving their optimal treatment (DeRubeis et al., 2014; Huibers et al., 2015).”
BackgroundMethodResultInfluentialResearch Design in Clinical Psychology
Kazdin, A. E. (2021) · doi:10.1017/9781108993647 · Cited by 240 · see citing works
Machine learning methods for developing precision treatment rules with observational data
Kessler, R. C., Bossarte, R. M., Luedtke, A. R., Zaslavsky, A. M., & Zubizarreta, J. R. (2019) · Behaviour Research and Therapy · doi:10.1016/j.brat.2019.103412 · Cited by 84 · see citing works
“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.”
BackgroundMethodLorenzo‐Luaces, L., DeRubeis, R. J., & Webb, C. A. (2014) · Journal of Consulting and Clinical Psychology · doi:10.1037/a0035994 · Cited by 81 · see citing works
“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.”
BackgroundSelf-citationSaunders, R., Buckman, J. E. J., Stott, J., Leibowitz, J., Aguirre, E., John, A., Lewis, G., Cape, J., & Pilling, S. (2021) · Journal of Affective Disorders · doi:10.1016/j.jad.2021.06.084 · Cited by 81 · see citing works
“…those seen for psychological treatment might systematically differ from those not seen for treatment on unmeasured factors associated with outcomes (e.g., chronicity of illness, relationship status, comorbid personality disorders and the number of previous treatments (DeRubeis et al., 2014)).”
BackgroundEarly Maladaptive Schemas and Schema Modes in clinical disorders: A systematic review
Bär, A., Bär, H. E., Rijkeboer, M. M., & Lobbestael, J. (2023) · Psychology and Psychotherapy: Theory, Research and Practice · doi:10.1111/papt.12465 · Cited by 79 · see citing works
A decennial review of psychotraumatology: what did we learn and where are we going?
Olff, M., Amstadter, A. B., Armour, C., Birkeland, M. S., Bui, É., Cloître, M., Ehlers, A., Ford, J. D., Greene, T., Hansen, M., et al. (2019) · European Journal of Psychotraumatology · doi:10.1080/20008198.2019.1672948 · Cited by 78 · see citing works
“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).”
Methodvan Bronswijk, S. C., DeRubeis, R. J., Lemmens, L. H. J. M., Peeters, F. P. M. L., Keefe, J. R., Cohen, Z. D., & Huibers, M. J. H. (2021) · Psychological Medicine · doi:10.1017/s0033291719003192 · Cited by 75 · see citing works
Self-citationHeterogeneity of treatment effects in trials on psychotherapy of depression.
Kaiser, T., Volkmann, C., Volkmann, A., Karyotaki, E., Cuijpers, P. J., & Brakemeier, E. (2022) · Clinical Psychology Science and Practice · doi:10.1037/cps0000079 · Cited by 77 · see citing works
Zilcha‐Mano, S., & Errázuriz, P. (2017) · Journal of Consulting and Clinical Psychology · doi:10.1037/ccp0000192 · Cited by 78 · see citing works
Kjell, O., Kjell, K., & Schwartz, H. A. (2023) · Psychiatry Research · doi:10.1016/j.psychres.2023.115667 · Cited by 96 · see citing works
“Natural language responses can also be used directly for precision in healthcare: To predict the likelihood of intervention success for a person in time without first predicting a psychiatric condition (e.g., DeRubeis et al., 2014).”
Hoeboer, C. M., de Kleine, R. A., Molendijk, M. L., Schoorl, M., Oprel, D. A. C., Mouthaan, J., van der Does, W. A. J., & van Minnen, A. (2020) · BJPsych Open · doi:10.1192/bjo.2020.30 · Cited by 75 · see citing works
Development and initial implementation of the Dynamic Assessment Treatment Algorithm (DATA)
Fernandez, K. C., Fisher, A. J., & Chi, C. (2017) · PLOS ONE · doi:10.1371/journal.pone.0178806 · Cited by 68 · see citing works
Wu, C., Dillon, D. G., Hsu, H.-C., Huang, S., Barrick, E. M., & Liu, Y. (2018) · Applied Sciences · doi:10.3390/app8081244 · Cited by 74 · see citing works
Buckman, J. E. J., Saunders, R., Cohen, Z. D., Barnett, P., Clarke, K., Ambler, G., DeRubeis, R. J., Gilbody, S. M., Hollon, S. D., Kendrick, T., et al. (2021) · Psychological Medicine · doi:10.1017/s0033291721001367 · Cited by 70 · see citing works
“There had been some suggestion from past studies that the duration of depression might be associated with prognosis (Carter et al., 2012; DeRubeis et al., 2014; Fournier et al., 2009; Lorenzo-Luaces et al., 2020; Noma et al., 2019) although there were inconsistencies and contradictory findings in…”
Self-citationEvidence-based psychotherapies I: qualifiers and limitations in what we know
Kazdin, A. E. (2014) · South African Journal of Psychology · doi:10.1177/0081246314533750 · Cited by 67 · see citing works
“The question continues to dominate the treatment research agenda (e.g., DeRubeis et al., 2014; Kaplan, 2008; Roth & Fonagy, 2005).”
BackgroundDunlop, 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 65 · see citing works
Intervenciones psicológicas basadas en mindfulness y sus beneficios: estado actual de la cuestión
Hervás, G., Cebolla, A., & Soler, J. (2016) · Clínica y Salud · doi:10.1016/j.clysa.2016.09.002 · Cited by 67 · see citing works
“…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).”
BackgroundKeefe, 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 63 · see citing works
“…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-citationLorenzo‐Luaces, L., Peipert, A., Jesús-Romero, R. D., Rutter, L. A., & Rodríguez‐Quintana, N. (2020) · International Journal of Cognitive Therapy · doi:10.1007/s41811-020-00094-3 · Cited by 62 · see citing works
Self-citationA-Tjak, J. G. L., Morina, N., Topper, M., & Emmelkamp, P. M. G. (2018) · Psychotherapy and Psychosomatics · doi:10.1159/000486807 · Cited by 61 · see citing works
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 60 · see citing works
Self-citationRubel, J. A., Zilcha-Mano, S., Giesemann, J., Prinz, J., & Lutz, W. (2020) · Psychotherapy Research · doi:10.1080/10503307.2019.1597994 · Cited by 56 · see citing works
“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).”
BackgroundMethodInfluentialFernández-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 · see citing works
“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).”
BackgroundA-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 · see citing works
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 · see citing works
Lorenzo-Luaces, L., & DeRubeis, R. J. (2018) · Cognitive Therapy and Research · doi:10.1007/s10608-018-9893-x · Cited by 56 · see citing works
Self-citationZilcha-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 · see citing works
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 58 · see citing works
Herzog, P., & Kaiser, T. (2022) · Journal of Anxiety Disorders · doi:10.1016/j.janxdis.2022.102611 · Cited by 60 · see citing works
Bruijniks, S. J. E., DeRubeis, R. J., Hollon, S. D., & Huibers, M. J. H. (2019) · Clinical Psychological Science · doi:10.1177/2167702619830391 · Cited by 54 · see citing works
Self-citationSauer-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 · see citing works
“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 57 · see citing works
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 · see citing works
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 · see citing works
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 53 · see citing works
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 · see citing works
“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).”
BackgroundThe human connectome in health and psychopathology
Essen, D. C. V., & Barch, D. M. (2015) · World Psychiatry · doi:10.1002/wps.20228 · Cited by 52 · see citing works
Hallgren, K. A., Bauer, A. M., & Atkins, D. C. (2017) · Depression and Anxiety · doi:10.1002/da.22640 · Cited by 45 · see citing works
“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…”
BackgroundRichards, 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 51 · see citing works
Moderators of Outcome for Youth Anxiety Treatments: Current Findings and Future Directions
Norris, L. A., & Kendall, P. C. (2021) · Journal of Clinical Child & Adolescent Psychology · doi:10.1080/15374416.2020.1833337 · Cited by 46 · see citing works
“…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).”
MethodKeefe, 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 · see citing works
Self-citationPsychotherapy and Neuroimaging
Fournier, J. C. J., & Price, R. B. (2014) · FOCUS The Journal of Lifelong Learning in Psychiatry · doi:10.1176/appi.focus.12.3.290 · Cited by 48 · see citing works
Self-citationTourism and depressive symptoms
Levi, E., Dolev, T., Collins‐Kreiner, N., & Zilcha‐Mano, S. (2018) · Annals of Tourism Research · doi:10.1016/j.annals.2018.04.001 · Cited by 48 · see citing works
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 47 · see citing works
Kappelmann, N., Rein, M., Fietz, J., Mayberg, H. S., Craighead, W. E., Dunlop, B. W., Nemeroff, C. B., Keller, M. B., Klein, D. N., Arnow, B. A., et al. (2020) · BMC Medicine · doi:10.1186/s12916-020-01623-9 · Cited by 49 · see citing works
Furukawa, T. A., Karyotaki, E., Suganuma, A. M., Pompoli, A., Ostinelli, E. G., Cipriani, A., Cuijpers, P. J., & Efthimiou, O. (2018) · BMJ Open · doi:10.1136/bmjopen-2018-026137 · Cited by 46 · see citing works
The history, current status, and possible future of precision mental health
DeRubeis, R. J. (2019) · Behaviour Research and Therapy · doi:10.1016/j.brat.2019.103506 · Cited by 46 · see citing works
Self-citationO’Driscoll, C., Buckman, J. E. J., Fried, E. I., Saunders, R., Cohen, Z. D., Ambler, G., DeRubeis, R. J., Gilbody, S. M., Hollon, S. D., Kendrick, T., et al. (2021) · BMC Medicine · doi:10.1186/s12916-021-01971-0 · Cited by 46 · see citing works
Self-citationWeisz, 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 46 · see citing works
Huijbers, M. J., Spinhoven, P., Spijker, J., Ruhé, H. G., van Schaik, D. J. F., van Oppen, P., Nolen, W. A., Ormel, J., Kuyken, W., van der Wilt, G. J., et al. (2015) · Journal of Affective Disorders · doi:10.1016/j.jad.2015.08.023 · Cited by 43 · see citing works
Hankin, B. L., Young, J. F., Gallop, R., & Garber, J. (2018) · Journal of Abnormal Child Psychology · doi:10.1007/s10802-018-0401-2 · Cited by 43 · see citing works
“…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).”
Zilcha‐Mano, S., Muran, J. C., Eubanks, C. F., Safran, J. D., & Winston, A. (2017) · Cognitive Therapy and Research · doi:10.1007/s10608-017-9866-5 · Cited by 42 · see citing works
A Machine Learning Approach to Identifying Placebo Responders in Late-Life Depression Trials
Zilcha‐Mano, S., Roose, S. P., Brown, P. J., & Rutherford, B. R. (2018) · American Journal of Geriatric Psychiatry · doi:10.1016/j.jagp.2018.01.001 · Cited by 42 · see citing works
“Novel, systematic approaches to subgroup analysis have recently shown differential effects for different treatment conditions across patient subgroups (17-18).”
BackgroundMethodResultLemmens, L. H. J. M., van Bronswijk, S. C., Peeters, F. P. M. L., Arntz, A. R., Hollon, S. D., & Huibers, M. J. H. (2018) · Psychological Medicine · doi:10.1017/s0033291718001083 · Cited by 42 · see citing works
Widnall, E., Price, A., Trompetter, H. R., & Dunn, B. D. (2019) · Cognitive Therapy and Research · doi:10.1007/s10608-019-10041-y · Cited by 39 · see citing works
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 · see citing works
- 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.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2014-01-08
- Volume
- 9
- Issue
- 1
- Pages
- e83875
- ISSN
- 1932-6203 (ISSN-L)
- 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
- 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 › 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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