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Personalized prediction of antidepressant v. placebo response: evidence from the EMBARC study

Published online by Cambridge University Press:  02 July 2018

Christian A. Webb ,
Madhukar H. Trivedi ,
Zachary D. Cohen ,
Daniel G. Dillon ,
Jay C. Fournier ,
Franziska Goer ,
Maurizio Fava ,
Patrick J. McGrath ,
Myrna Weissman  and
Ramin Parsey
...Show all authors
Christian A. Webb*
Affiliation:
Harvard Medical School – McLean Hospital, Boston, MA, USA
Madhukar H. Trivedi
Affiliation:
University of Texas, Southwestern Medical Center, Dallas, TX, USA
Zachary D. Cohen
Affiliation:
University of Pennsylvania, Philadelphia, PA, USA
Daniel G. Dillon
Affiliation:
Harvard Medical School – McLean Hospital, Boston, MA, USA
Jay C. Fournier
Affiliation:
University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
Franziska Goer
Affiliation:
Harvard Medical School – McLean Hospital, Boston, MA, USA
Maurizio Fava
Affiliation:
Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
Patrick J. McGrath
Affiliation:
New York State Psychiatric Institute & Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY, USA
Myrna Weissman
Affiliation:
New York State Psychiatric Institute & Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY, USA
Ramin Parsey
Affiliation:
Stony Brook University, Stony Brook, NY, USA
Phil Adams
Affiliation:
New York State Psychiatric Institute & Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY, USA
Joseph M. Trombello
Affiliation:
University of Texas, Southwestern Medical Center, Dallas, TX, USA
Crystal Cooper
Affiliation:
University of Texas, Southwestern Medical Center, Dallas, TX, USA
Patricia Deldin
Affiliation:
University of Michigan, Ann Arbor, MI, USA
Maria A. Oquendo
Affiliation:
University of Pennsylvania, Philadelphia, PA, USA
Melvin G. McInnis
Affiliation:
University of Michigan, Ann Arbor, MI, USA
Quentin Huys
Affiliation:
University of Zurich, Zurich, Switzerland
Gerard Bruder
Affiliation:
New York State Psychiatric Institute & Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY, USA
Benji T. Kurian
Affiliation:
University of Texas, Southwestern Medical Center, Dallas, TX, USA
Manish Jha
Affiliation:
University of Texas, Southwestern Medical Center, Dallas, TX, USA
Robert J. DeRubeis
Affiliation:
University of Pennsylvania, Philadelphia, PA, USA
Diego A. Pizzagalli
Affiliation:
Harvard Medical School – McLean Hospital, Boston, MA, USA
*
Author for correspondence: Christian A. Webb, E-mail: cwebb@mclean.harvard.edu
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Abstract

Background

Major depressive disorder (MDD) is a highly heterogeneous condition in terms of symptom presentation and, likely, underlying pathophysiology. Accordingly, it is possible that only certain individuals with MDD are well-suited to antidepressants. A potentially fruitful approach to parsing this heterogeneity is to focus on promising endophenotypes of depression, such as neuroticism, anhedonia, and cognitive control deficits.

Methods

Within an 8-week multisite trial of sertraline v. placebo for depressed adults (n = 216), we examined whether the combination of machine learning with a Personalized Advantage Index (PAI) can generate individualized treatment recommendations on the basis of endophenotype profiles coupled with clinical and demographic characteristics.

Results

Five pre-treatment variables moderated treatment response. Higher depression severity and neuroticism, older age, less impairment in cognitive control, and being employed were each associated with better outcomes to sertraline than placebo. Across 1000 iterations of a 10-fold cross-validation, the PAI model predicted that 31% of the sample would exhibit a clinically meaningful advantage [post-treatment Hamilton Rating Scale for Depression (HRSD) difference ⩾3] with sertraline relative to placebo. Although there were no overall outcome differences between treatment groups (d = 0.15), those identified as optimally suited to sertraline at pre-treatment had better week 8 HRSD scores if randomized to sertraline (10.7) than placebo (14.7) (d = 0.58).

Conclusions

A subset of MDD patients optimally suited to sertraline can be identified on the basis of pre-treatment characteristics. This model must be tested prospectively before it can be used to inform treatment selection. However, findings demonstrate the potential to improve individual outcomes through algorithm-guided treatment recommendations.

Keywords

Antidepressantdepressionendophenotypemachine learningplaceboprecision medicineprediction
Type
Original Articles
Information
Psychological Medicine , Volume 49 , Issue 7 , May 2019 , pp. 1118 - 1127
Copyright
Copyright © Cambridge University Press 2018 

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References

American Psychiatric Association (2010) Treatment of Patients with major Depressive Disorder. 3rd Edn. Washington, DC: American Psychiatric Press.Google Scholar
Austin, PC and Tu, JV (2004) Bootstrap methods for developing predictive models. The American Statistician 58, 131–137.Google Scholar
Bagby, RM, Quilty, LC, Segal, ZV, McBride, CC, Kennedy, SH and Costa, PT (2008) Personality and differential treatment response in major depression: a randomized controlled trial comparing cognitive-behavioural therapy and pharmacotherapy. The Canadian Journal of Psychiatry 53, 361–370.Google Scholar
Baldessarini, RJ, Forte, A, Selle, V, Sim, K, Tondo, L, Undurraga, J and Vázquez, GH (2017) Morbidity in depressive disorders. Psychotherapy and Psychosomatics 86, 65–72.Google Scholar
Ball, S, Classi, P and Dennehy, EB (2014) What happens next?: a claims database study of second-line pharmacotherapy in patients with major depressive disorder (MDD) who initiate selective serotonin reuptake inhibitor (SSRI) treatment. Annals of General Psychiatry 13, 8.Google Scholar
Bet, PM, Hugtenburg, JG, Penninx, BWJH and Hoogendijk, WJG (2013) Side effects of antidepressants during long-term use in a naturalistic setting. European Neuropsychopharmacology 23, 1443–1451.Google Scholar
Cipriani, A, Furukawa, TA, Salanti, G, Chaimani, A, Atkinson, LZ, Ogawa, Y, Leucht, S, Ruhe, HG, Turner, EH, Higgins, JPT, Egger, M, Takeshima, N, Hayasaka, Y, Imai, H, Shinohara, K, Tajika, A, Ioannidis, JPA and Geddes, JR (2018) Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: a systematic review and network meta-analysis. The Lancet 391, 1357–1366.Google Scholar
Cohen, ZD and DeRubeis, RJ (2018) Treatment selection in depression. Annual Review of Clinical Psychology 14, 209–236.Google Scholar
Cohen, ZD, Kim, T, Van, H, Dekker, J and Driessen, E (2017) Recommending cognitive-behavioral versus psychodynamic therapy for mild to moderate adult depression. PsyArXiv, https://osf.io/6qxve/.Google Scholar
DeRubeis, RJ, Cohen, ZD, Forand, NR, Fournier, JC, Gelfand, LA and Lorenzo-Luaces, L (2014) The personalized advantage index: translating research on prediction into individualized treatment recommendations. A demonstration. PLoS ONE 9, e83875.Google Scholar
Dunner, DL (2001) Acute and maintenance treatment of chronic depression. The Journal of Clinical Psychiatry 62(Suppl. 6), 10–16.Google Scholar
Eriksen, BA and Eriksen, CW (1974) Effects of noise letters upon the identification of a target letter in a nonsearch task. Perception & Psychophysics 16, 143–149.Google Scholar
Etkin, A, Patenaude, B, Song, YJC, Usherwood, T, Rekshan, W, Schatzberg, AF, Rush, AJ and Williams, LM (2015) A cognitive–emotional biomarker for predicting remission with antidepressant medications: a report from the iSPOT-D trial. Neuropsychopharmacology 40, 1332–1342.Google Scholar
Fava, M, Rush, AJ, Alpert, JE, Balasubramani, GK, Wisniewski, SR, Carmin, CN, Biggs, MM, Zisook, S, Leuchter, A, Howland, R, Warden, D and Trivedi, MH (2008) Difference in treatment outcome in outpatients with anxious versus nonanxious depression: a STAR*D report. American Journal of Psychiatry 165, 342–351.Google Scholar
Fiedler, K (2011) Voodoo correlations are everywhere – not only in neuroscience. Perspectives on Psychological Science 6, 163–171.Google Scholar
Fournier, JC, DeRubeis, RJ, Hollon, SD, Dimidjian, S, Amsterdam, JD, Shelton, RC and Fawcett, J (2010) Antidepressant drug effects and depression severity: a patient-level meta-analysis. JAMA 303, 47–53.Google Scholar
Fournier, JC, DeRubeis, RJ, Hollon, SD, Gallop, R, Shelton, RC and Amsterdam, JD (2013) Differential change in specific depressive symptoms during antidepressant medication or cognitive therapy. Behaviour Research and Therapy 51, 392–398.Google Scholar
Fournier, JC, DeRubeis, RJ, Shelton, RC, Hollon, SD, Amsterdam, JD and Gallop, R (2009) Prediction of response to medication and cognitive therapy in the treatment of moderate to severe depression. Journal of Consulting and Clinical Psychology 77, 775–787.Google Scholar
Fried, EI and Nesse, RM (2015 a) Depression is not a consistent syndrome: an investigation of unique symptom patterns in the STAR*D study. Journal of Affective Disorders 172, 96–102.Google Scholar
Fried, EI and Nesse, RM (2015 b) Depression sum-scores don't add up: why analyzing specific depression symptoms is essential. BMC Medicine 13, 72.Google Scholar
Friedman, J, Hastie, T and Tibshirani, R (2010) Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software 33, 1–22.Google Scholar
Garge, NR, Bobashev, G and Eggleston, B (2013) Random forest methodology for model-based recursive partitioning: the mobForest package for R. BMC Bioinformatics 14, 125.Google Scholar
Gillan, CM and Whelan, R (2017) What big data can do for treatment in psychiatry. Current Opinion in Behavioral Sciences 18, 34–42.Google Scholar
Goldstein, BL and Klein, DN (2014) A review of selected candidate endophenotypes for depression. Clinical Psychology Review 34, 417–427.Google Scholar
Gottesman, II and Gould, TD (2003) The endophenotype concept in psychiatry: etymology and strategic intentions. The American Journal of Psychiatry 160, 636–645.Google Scholar
Hamilton, M (1960) A rating scale for depression. Journal of Neurology, Neurosurgery & Psychiatry 23, 56–62.Google Scholar
Hastie, T, Tibshirani, R and Friedman, J (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition. 2nd Edn. New York, NY: Springer.Google Scholar
Herrera-Guzmán, I, Gudayol-Ferré, E, Herrera-Guzmán, D, Guàrdia-Olmos, J, Hinojosa-Calvo, E and Herrera-Abarca, JE (2009) Effects of selective serotonin reuptake and dual serotonergic-noradrenergic reuptake treatments on memory and mental processing speed in patients with major depressive disorder. Journal of Psychiatric Research 43, 855–863.Google Scholar
Hollon, SD (2016) The efficacy and acceptability of psychological interventions for depression: where we are now and where we are going. Epidemiology and Psychiatric Sciences 25, 295–300.Google Scholar
Holmes, AJ, Bogdan, R and Pizzagalli, DA (2010) Serotonin transporter genotype and action monitoring dysfunction: a possible substrate underlying increased vulnerability to depression. Neuropsychopharmacology 35, 1186–1197.Google Scholar
Huibers, MJH, Cohen, ZD, Lemmens, LHJM, Arntz, A, Peeters, FPML, Cuijpers, P and DeRubeis, RJ (2015) Predicting optimal outcomes in cognitive therapy or interpersonal psychotherapy for depressed individuals using the personalized advantage index approach. PLoS ONE 10, e0140771.Google Scholar
Jakobsen, JC, Katakam, KK, Schou, A, Hellmuth, SG, Stallknecht, SE, Leth-Møller, K, Iversen, M, Banke, MB, Petersen, IJ, Klingenberg, SL, Krogh, J, Ebert, SE, Timm, A, Lindschou, J and Gluud, C (2017) Selective serotonin reuptake inhibitors versus placebo in patients with major depressive disorder. A systematic review with meta-analysis and Trial Sequential Analysis. BMC Psychiatry 17, 58.Google Scholar
Jakubovski, E and Bloch, MH (2014) Prognostic subgroups for citalopram response in the STAR*D trial. The Journal of Clinical Psychiatry 75, 738–747.Google Scholar
Julien, RM (2013) A Primer of Drug Action: A Concise Nontechnical Guide to the Actions, Uses, and Side Effects of Psychoactive Drugs, Revised and Updated. New York: Henry Holt and Company.Google Scholar
Kapelner, A and Bleich, J (2016) Bartmachine: machine learning with Bayesian additive regression trees. Journal of Statistical Software 70, 1–40.Google Scholar
Khan, A, Dager, SR, Cohen, S, Avery, DH, Scherzo, B and Dunner, DL (1991) Chronicity of depressive episode in relation to antidepressant-placebo response. Neuropsychopharmacology 4, 125–130.Google Scholar
Khan, A, Leventhal, RM, Khan, SR and Brown, WA (2002) Severity of depression and response to antidepressants and placebo: an analysis of the Food and Drug Administration database. Journal of Clinical Psychopharmacology 22, 40–45.Google Scholar
Khin, NA, Chen, Y-F, Yang, Y, Yang, P and Laughren, TP (2011) Exploratory analyses of efficacy data from major depressive disorder trials submitted to the US food and drug administration in support of new drug applications. The Journal of Clinical Psychiatry 72, 464–472.Google Scholar
Kirsch, I (2015) Clinical trial methodology and drug-placebo differences. World Psychiatry 14, 301–302.Google Scholar
Kirsch, I, Deacon, BJ, Huedo-Medina, TB, Scoboria, A, Moore, TJ and Johnson, BT (2008) Initial severity and antidepressant benefits: a meta-analysis of data submitted to the food and drug administration. PLoS Medicine 5, e45.Google Scholar
Kraemer, HC (2013) Discovering, comparing, and combining moderators of treatment on outcome after randomized clinical trials: a parametric approach. Statistics in Medicine 32, 1964–1973.Google Scholar
Kraemer, HC and Blasey, CM (2004) Centring in regression analyses: a strategy to prevent errors in statistical inference. International Journal of Methods in Psychiatric Research 13, 141–151.Google Scholar
Lam, RW, Kennedy, SH, McIntyre, RS and Khullar, A (2014) Cognitive dysfunction in major depressive disorder: effects on psychosocial functioning and implications for treatment. The Canadian Journal of Psychiatry 59, 649–654.Google Scholar
Leucht, S, Hierl, S, Kissling, W, Dold, M and Davis, JM (2012) Putting the efficacy of psychiatric and general medicine medication into perspective: review of meta-analyses. The British Journal of Psychiatry 200, 97–106.Google Scholar
Mahableshwarkar, AR, Zajecka, J, Jacobson, W, Chen, Y and Keefe, RS (2015) A randomized, placebo-controlled, active-reference, double-blind, flexible-dose study of the efficacy of vortioxetine on cognitive function in major depressive disorder. Neuropsychopharmacology 40, 2025–2037.Google Scholar
Marcus, SC, Hassan, M and Olfson, M (2009) Antidepressant switching among adherent patients treated for depression. Psychiatric Services 60, 617–623.Google Scholar
Mars, B, Heron, J, Gunnell, D, Martin, RM, Thomas, KH and Kessler, D (2017) Prevalence and patterns of antidepressant switching amongst primary care patients in the UK. Journal of Psychopharmacology 31, 553–560.Google Scholar
McCrae, RR and Costa, PT (2010) NEO Inventories Professional Manual. Lutz, FL: Psychological Assessment Resources.Google Scholar
McGrath, CL, Kelley, ME, Holtzheimer, PE, Dunlop, BW, Craighead, WE, Franco, AR, Craddock, RC and Mayberg, HS (2013) Toward a neuroimaging treatment selection biomarker for major depressive disorder. JAMA Psychiatry 70, 821–829.Google Scholar
McMakin, DL, Olino, TM, Porta, G, Dietz, LJ, Emslie, G, Clarke, G, Wagner, KD, Asarnow, JR, Ryan, ND, Birmaher, B, Shamseddeen, W, Mayes, T, Kennard, B, Spirito, A, Keller, M, Lynch, FL, Dickerson, JF and Brent, DA (2012) Anhedonia predicts poorer recovery among youth with selective serotonin reuptake inhibitor-treatment resistant depression. Journal of the American Academy of Child and Adolescent Psychiatry 51, 404–411.Google Scholar
Milea, D, Guelfucci, F, Bent-Ennakhil, N, Toumi, M and Auray, J-P (2010) Antidepressant monotherapy: a claims database analysis of treatment changes and treatment duration. Clinical Therapeutics 32, 2057–2072.Google Scholar
Moncrieff, J and Kirsch, I (2015) Empirically derived criteria cast doubt on the clinical significance of antidepressant-placebo differences. Contemporary Clinical Trials 43, 60–62.Google Scholar
Moncrieff, J, Wessely, S and Hardy, R (2004) Active placebos versus antidepressants for depression. The Cochrane Library.Google Scholar
NICE (2018) Depression in adults: recognition and management|guidance and guidelines|NICE.Google Scholar
Pizzagalli, DA, Evins, AE, Schetter, EC, Frank, MJ, Pajtas, PE, Santesso, DL and Culhane, M (2008a) Single dose of a dopamine agonist impairs reinforcement learning in humans: behavioral evidence from a laboratory-based measure of reward responsiveness. Psychopharmacology 196, 221–232.Google Scholar
Pizzagalli, DA, Goetz, E, Ostacher, M, Iosifescu, DV and Perlis, RH (2008b) Euthymic patients with bipolar disorder show decreased reward learning in a probabilistic reward task. Biological Psychiatry 64, 162–168.Google Scholar
Pizzagalli, DA, Jahn, AL and O'Shea, JP (2005) Toward an objective characterization of an anhedonic phenotype: a signal-detection approach. Biological Psychiatry 57, 319–327.Google Scholar
Pizzagalli, DA, Webb, CA, Dillon, DG, Tenke, CE, Kayser, J, Goer, F, Fava, M, McGrath, P, Weissman, M, Parsey, R, Adams, P, Trombello, J, Cooper, C, Deldin, P, Oquendo, MA, McInnis, MG, Carmody, T, Bruder, G and Trivedi, MH (2018) Pretreatment rostral anterior cingulate cortex theta activity in relation to symptom improvement in depression: a randomized clinical trial. JAMA Psychiatry 75, 547–554.Google Scholar
Quilty, LC, Meusel, L-AC and Bagby, RM (2008) Neuroticism as a mediator of treatment response to SSRIs in major depressive disorder. Journal of Affective Disorders 111, 67–73.Google Scholar
R Core Team (2013) R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.Google Scholar
Rush, AJ, Trivedi, MH, Ibrahim, HM, Carmody, TJ, Arnow, B, Klein, DN, Markowitz, JC, Ninan, PT, Kornstein, S, Manber, R, Thase, ME, Kocsis, JH and Keller, MB (2003) The 16-item quick inventory of depressive symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): a psychometric evaluation in patients with chronic major depression. Biological Psychiatry 54, 573–583.Google Scholar
Rush, AJ, Trivedi, MH, Wisniewski, SR, Nierenberg, AA, Stewart, JW, Warden, D, Niederehe, G, Thase, ME, Lavori, PW, Lebowitz, BD, McGrath, PJ, Rosenbaum, JF, Sackeim, HA, Kupfer, DJ, Luther, J and Fava, M (2006) Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. The American Journal of Psychiatry 163, 1905–1917.Google Scholar
Saragoussi, D, Chollet, J, Bineau, S, Chalem, Y and Milea, D (2012) Antidepressant switching patterns in the treatment of major depressive disorder: a General Practice Research Database (GPRD) Study. International Journal of Clinical Practice 66, 1079–1087.Google Scholar
Schultz, J and Joish, V (2009) Costs associated with changes in antidepressant treatment in a managed care population with major depressive disorder. Psychiatric Services 60, 1604–1611.Google Scholar
Snaith, RP, Hamilton, M, Morley, S, Humayan, A, Hargreaves, D and Trigwell, P (1995) A scale for the assessment of hedonic tone: the Snaith-Hamilton Pleasure Scale. British Journal of Psychiatry 167, 99–103.Google Scholar
Soskin, DP, Carl, JR, Alpert, J and Fava, M (2012) Antidepressant effects on emotional temperament: toward a biobehavioral research paradigm for major depressive disorder. CNS Neuroscience & Therapeutics 18, 441–451.Google Scholar
Souery, D, Oswald, P, Massat, I, Bailer, U, Bollen, J, Demyttenaere, K, Kasper, S, Lecrubier, Y, Montgomery, S, Serretti, A, Zohar, J and Mendlewicz, J, Group for the Study of Resistant Depression (2007) Clinical factors associated with treatment resistance in major depressive disorder: results from a European multicenter study. The Journal of Clinical Psychiatry 68, 1062–1070.Google Scholar
Stekhoven, DJ and Bühlmann, P (2012) Missforest – non-parametric missing value imputation for mixed-type data. Bioinformatics (Oxford, England) 28, 112–118.Google Scholar
Tang, TZ, DeRubeis, RJ, Hollon, SD, Amsterdam, J, Shelton, R and Schalet, B (2009) Personality change during depression treatment: a placebo-controlled trial. Archives of General Psychiatry 66, 1322–1330.Google Scholar
Thomas, L, Kessler, D, Campbell, J, Morrison, J, Peters, TJ, Williams, C, Lewis, G and Wiles, N (2013) Prevalence of treatment-resistant depression in primary care: cross-sectional data. The British Journal of General Practice 63, e852–e858.Google Scholar
Trivedi, MH, McGrath, PJ, Fava, M, Parsey, RV, Kurian, BT, Phillips, ML, Oquendo, MA, Bruder, G, Pizzagalli, D, Toups, M, Cooper, C, Adams, P, Weyandt, S, Morris, DW, Grannemann, BD, Ogden, RT, Buckner, R, McInnis, M, Kraemer, HC, Petkova, E, Carmody, TJ and Weissman, MM (2016) Establishing moderators and biosignatures of antidepressant response in clinical care (EMBARC): rationale and design. Journal of Psychiatric Research 78, 11–23.Google Scholar
Trivedi, MH, Rush, AJ, Wisniewski, SR, Nierenberg, AA, Warden, D, Ritz, L, Norquist, G, Howland, RH, Lebowitz, B, McGrath, PJ, Shores-Wilson, K, Biggs, MM, Balasubramani, GK and Fava, M (2006) Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: implications for clinical practice. American Journal of Psychiatry 163, 28–40.Google Scholar
Turner, EH, Matthews, AM, Linardatos, E, Tell, RA and Rosenthal, R (2008) Selective publication of antidepressant trials and Its influence on apparent efficacy. New England Journal of Medicine 358, 252–260.Google Scholar
Uher, R, Perlis, RH, Henigsberg, N, Zobel, A, Rietschel, M, Mors, O, Hauser, J, Dernovsek, MZ, Souery, D, Bajs, M, Maier, W, Aitchison, KJ, Farmer, A and McGuffin, P (2012 a) Depression symptom dimensions as predictors of antidepressant treatment outcome: replicable evidence for interest-activity symptoms. Psychological Medicine 42, 967–980.Google Scholar
Uher, R, Tansey, KE, Malki, K and Perlis, RH (2012 b) Biomarkers predicting treatment outcome in depression: what is clinically significant? Pharmacogenomics 13, 233–240.Google Scholar
Vrieze, E, Pizzagalli, DA, Demyttenaere, K, Hompes, T, Sienaert, P, de Boer, P, Schmidt, M and Claes, S (2013) Reduced reward learning predicts outcome in major depressive disorder. Biological Psychiatry 73, 639–645.Google Scholar
Vuorilehto, MS, Melartin, TK and Isometsä, ET (2009) Course and outcome of depressive disorders in primary care: a prospective 18-month study. Psychological Medicine 39, 1697–1707.Google Scholar
Wakefield, JC and Schmitz, MF (2013) When does depression become a disorder? Using recurrence rates to evaluate the validity of proposed changes in major depression diagnostic thresholds. World Psychiatry 12, 44–52.Google Scholar
Waljee, AK, Mukherjee, A, Singal, AG, Zhang, Y, Warren, J, Balis, U, Marrero, J, Zhu, J and Higgins, PD (2013) Comparison of imputation methods for missing laboratory data in medicine. BMJ Open 3, e002847.Google Scholar
Watson, D, Clark, LA, Weber, K, Assenheimer, JS, Strauss, ME and McCormick, RA (1995) Testing a tripartite model: II. Exploring the symptom structure of anxiety and depression in student, adult, and patient samples. Journal of Abnormal Psychology 104, 15–25.Google Scholar
Webb, CA, Dillon, DG, Pechtel, P, Goer, FK, Murray, L, Huys, QJ, Fava, M, McGrath, PJ, Weissman, M, Parsey, R, Kurian, BT, Adams, P, Weyandt, S, Trombello, JM, Grannemann, B, Cooper, CM, Deldin, P, Tenke, C, Trivedi, M, Bruder, G and Pizzagalli, DA (2016) Neural correlates of three promising endophenotypes of depression: evidence from the EMBARC study. Neuropsychopharmacology 41, 454–463.Google Scholar
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