Treatment Selection in Depression

Annual Review of Clinical Psychology

Volumn 14, Issue , 2018, Pages 209-236

  Cohen, Zachary D ^a   Derubeis, Robert J ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

Depression; Mental health treatment; Personalized medicine; Precision medicine; Stratified medicine; Treatment selection

Indexed keywords

ARTICLE; DEPRESSION; HUMAN; OUTCOME ASSESSMENT; PERSONALIZED MEDICINE; PREDICTION; PROGNOSIS; BIOLOGICAL MODEL; PROCEDURES; STATISTICAL MODEL;

DEPRESSIVE DISORDER; HUMANS; MODELS, STATISTICAL; OUTCOME ASSESSMENT (HEALTH CARE); PATIENT-SPECIFIC MODELING; PRECISION MEDICINE;

EID: 85044909823     PISSN: 15485943     EISSN: 15485951     Source Type: Book Series    
DOI: 10.1146/annurev-clinpsy-050817-084746     Document Type: Article

References (189)

1. Allen J, Mattson M, MillerW, Tonigan J, Connors G, et al. 1997. Matching alcoholism treatments to client heterogeneity. J. Stud. Alcohol 58:7-29
2. American Psychiatric Association. 2010. Practice Guideline for the Treatment of Patients with Major Depressive Disorder. Arlington, VA: Am. Psych. Assoc. Pub. 3rd ed. https://psychiatryonline.org/pb/assets/raw/ sitewide/practiceguidelines/guidelines/mdd.pdf
3. Amsterdam JD, Lorenzo-Luaces L, DeRubeis RJ. 2016. Step-wise loss of antidepressant effectiveness with repeated antidepressant trials in bipolar II depression. Bipolar Disord. 18:563-70
4. Amsterdam JD, Shults J. 2009. Does tachyphylaxis occur after repeated antidepressant exposure in patients with Bipolar II major depressive episode? J. Affect. Disord. 115:234-40
5. Amsterdam JD, Williams D, Michelson D, Adler LA, Dunner DL, et al. 2009. Tachyphylaxis after repeated antidepressant drug exposure in patients with recurrent major depressive disorder. Neuropsychobiology 59:227-33
6. Ashar YK, Chang LJ, Wager TD. 2017. Brain mechanisms of the placebo effect: an affective appraisal account. Annu. Rev. Clin. Psychol. 13:73-98
7. Austin PC, Tu JV. 2004. Bootstrap methods for developing predictive models. Am. Stat. 58:131-37
8. Barber JP, Muenz LR. 1996. The role of avoidance and obsessiveness in matching patients to cognitive and interpersonal psychotherapy: empirical findings from the Treatment for Depression Collaborative Research Program. J. Consult. Clin. Psychol. 64:951-58
9. Barbui C, Cipriani A, Patel V, Ayuso-Mateos JL, van Ommeren M. 2011. Efficacy of antidepressants and benzodiazepines in minor depression: systematic review and meta-analysis. Br. J. Psychiatry 198:11-16
10. Baron RM, Kenny DA. 1986. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J. Pers. Soc. Psychol. 51:1173-82
11. Beck AT, Rush AJ, Shaw BF, Emery G. 1979. Cognitive Therapy of Depression. New York: Guilford Press
12. Beutler LE, EngleD,MohrD,DaldrupRJ, Bergan J, et al. 1991. Predictors of differential response to cognitive, experiential, and self-directed psychotherapeutic procedures. J. Consult. Clin. Psychol. 59:333-40
13. Bleich J, Kapelner A, George EI, Jensen ST. 2014. Variable selection for BART: an application to gene regulation. Ann. Appl. Stat. 8:1750-81
14. Bossuyt PM, Parvin T. 2015. Evaluating biomarkers for guiding treatment decisions. EJIFCC 26:63-70
15. Brunoni AR, Sampaio-Junior B, Moffa AH, Borrione L, Nogueira BS, et al. 2015. The Escitalopram versus Electric Current Therapy for Treating Depression Clinical Study (ELECT-TDCS): rationale and study design of a non-inferiority, triple-arm, placebo-controlled clinical trial. Sao Paulo Med. J. 133:252-63
16. Bursac Z, GaussCH, WilliamsDK, HosmerDW. 2008. Purposeful selection of variables in logistic regression. Source Code Biol. Med. 3:17
17. Byar DP. 1985. Assessing apparent treatment-covariate interactions in randomized clinical trials. Stat. Med. 4:255-63
18. Byar DP, Corle DK. 1977. Selecting optimal treatment in clinical trials using covariate information. J. Chronic Diseas. 30:445-59
19. Byrne SE, Rothschild AJ. 1998. Loss of antidepressant efficacy during maintenance therapy: possible mechanisms and treatments. J. Clin. Psychiatry 59:279-88
20. Chakraborty B, Moodie E. 2013. Statistical Methods for Dynamic Treatment Regimes. New York: Springer
21. Chambless DL, Hollon SD. 1998. Defining empirically supported therapies. J. Consult. Clin. Psychol. 66:7-18
22. Cheavens JS, Strunk DR, Lazarus SA, Goldstein LA. 2012. The compensation and capitalization models: a test of two approaches to individualizing the treatment of depression. Behav. Res. Ther. 50:699-706
23. Chekroud AM,Gueorguieva R, KrumholzHM,TrivediMH, Krystal JH, McCarthyG. 2017. Reevaluating the efficacy and predictability of antidepressant treatments: a symptom clustering approach. JAMA Psychiatry 74:370-78
24. Chekroud AM, Zotti RJ, Shehzad Z, Gueorguieva R, Johnson MK, et al. 2016. Cross-trial prediction of treatment outcome in depression: a machine learning approach. Lancet Psychiatry 3:243-50
25. Cloitre M, Petkova E, Su Z, Weiss B. 2016. Patient characteristics as a moderator of post-traumatic stress disorder treatment outcome: combining symptom burden and strengths. BJPsych Open 2:101-06
26. Cohen Z, Kim T, Van R, Dekker J, Driessen E. 2017. Individual treatment recommendations of cognitivebehavioral or psychodynamic therapy for mild to moderate adult depression: improving the Personalized Advantage Index approach. https://osf.io/6qxve/
27. Craske MG,Meuret AE, Ritz T, TreanorM,Dour HJ. 2016. Treatment for anhedonia: a neuroscience driven approach. Depress. Anxiety 33:927-38
28. Cronbach LJ. 1957. The two disciplines of scientific psychology. Am. Psychol. 12:671-84
29. Cuijpers P, Huibers MJ, Furukawa TA. 2017. The need for research on treatments of chronic depression. JAMA Psychiatry 74:242-43
30. d'Agostino RB. 1998. Tutorial in biostatistics: propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat. Med. 17:2265-81
31. Dawes RM. 1979. The robust beauty of improper linear models in decision making. Am. Psychol. 34:571-82
32. Dawes RM. 2005. The ethical implications of Paul Meehl's work on comparing clinical versus actuarial prediction methods. J. Clin. Psychol. 61:1245-55
33. Dawes RM, Faust D, Meehl PE. 1989. Clinical versus actuarial judgment. Science 243:1668-74
34. Delgadillo J, Huey D, Bennett H, McMillan D. 2017. Case complexity as a guide for psychological treatment selection. J. Consult. Clin. Psychol. 85:835-53
35. Delgadillo J, Moreea O, Lutz W. 2016. Different people respond differently to therapy: a demonstration using patient profiling and risk stratification. Behav. Res. Ther. 79:15-22
36. DeRubeis RJ, Cohen ZD, Forand NR, Fournier JC, Gelfand LA, Lorenzo-Luaces L. 2014a. The Personalized Advantage Index: translating research on prediction into individualized treatment recommendations. A demonstration. PLOS ONE 9:e83875
37. DeRubeis RJ, Gelfand LA, German RE, Fournier JC, Forand NR. 2014b. Understanding processes of change: how some patients reveal more than others-and some groups of therapists less-about what matters in psychotherapy. Psychother. Res. 24:419-28
38. DeRubeis RJ, Hollon SD, Amsterdam JD, Shelton RC, Young PR, et al. 2005. Cognitive therapy versus medications in the treatment of moderate to severe depression. Arch. Gen. Psychiatry 62:409-16
39. Doove LL, Dusseldorp E, Van Deun K, Van Mechelen I. 2014. A comparison of five recursive partitioning methods to find person subgroups involved in meaningful treatment-subgroup interactions. Adv. Data Anal. Classification 8:403-25
40. Driessen E, Cuijpers P, Hollon SD, Dekker JJ. 2010. Does pretreatment severity moderate the efficacy of psychological treatment of adult outpatient depression? A meta-analysis. J. Consult. Clin. Psychol. 78:668-80
41. Drysdale AT,Grosenick L, Downar J, Dunlop K, Mansouri F, et al. 2017. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat. Med. 23:28-38
42. Dunlop BW, Binder EB, Cubells JF, Goodman MM, Kelley ME, et al. 2012a. Predictors of remission in depression to individual and combined treatments (PReDICT): study protocol for a randomized controlled trial. Trials 13:106
43. Dunlop BW, Kelley ME, Aponte-Rivera V, Mletzko-Crowe T, Kinkead B, et al. 2017. Effects of patient preferences on outcomes in the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) Study. Am. J. Psychiatry 174:546-56
44. Dunlop BW, KelleyME, Mletzko TC, Velasquez CM, Craighead WE,Mayberg HS. 2012b. Depression beliefs, treatment preference, and outcomes in a randomized trial formajor depressive disorder. J. Psychiatr. Res. 46:375-81
45. Elkin I, Shea MT, Watkins JT, Imber SD, Sotsky SM, et al. 1989. National Institute of Mental Health treatment of depression collaborative research program: general effectiveness of treatments. Arch. Gen. Psychiatry 46:971-82
46. Fernandez KC, Fisher AJ, Chi C. 2017. Development and initial implementation of the Dynamic Assessment Treatment Algorithm (DATA). PLOS ONE 12:e0178806
47. Fineberg NA, Brown A, Reghunandanan S, Pampaloni I. 2012. Evidence-based pharmacotherapy of obsessivecompulsive disorder. Int. J. Neuropsychopharmacol. 15:1173-91
48. Fisher AJ, Boswell JF. 2016. Enhancing the personalization of psychotherapy with dynamic assessment and modeling. Assessment 23:496-506
49. Forand NR, HuibersMJ, DeRubeis RJ. 2017. Prognosis moderates the engagement-outcome relationship in unguided cCBT for depression: a proof of concept for the prognosis moderation hypothesis. J. Consult. Clin. Psychol. 85:471-83
50. Fournier JC, DeRubeis RJ, Hollon SD, Dimidjian S, Amsterdam JD, et al. 2010. Antidepressant drug effects and depression severity: a patient-level meta-analysis. JAMA 303:47-53
51. Fournier JC, DeRubeis RJ, Shelton RC, Gallop R, Amsterdam JD, Hollon SD. 2008. Antidepressant medications v. cognitive therapy in people with depression with or without personality disorder. Br. J. Psychiatry 192:124-29
52. Fournier JC, DeRubeis RJ, Shelton RC, Hollon SD, Amsterdam JD, Gallop R. 2009. Prediction of response to medication and cognitive therapy in the treatment of moderate to severe depression. J. Consult. Clin. Psychol. 77:775-85
53. Gabrieli JD, Ghosh SS, Whitfield-Gabrieli S. 2015. Prediction as a humanitarian and pragmatic contribution from human cognitive neuroscience. Neuron 85:11-26
54. Gail M, Simon R. 1985. Testing for qualitative interactions between treatment effects and patient subsets. Biometrics 41:361-72
55. Garge NR, Bobashev G, Eggleston B. 2013. Random forest methodology for model-based recursive partitioning: the mobForest package for R. BMC Bioinformat. 14:125
56. Gillan CM, Daw ND. 2016. Taking psychiatry research online. Neuron 91:19-23
57. Gillan CM, Whelan R. 2017. What big data can do for treatment in psychiatry. Curr. Opin. Behav. Sci. 18:34-42
58. Gordon E, Rush AJ, Palmer DM, Braund TA, RekshanW. 2015. Toward an online cognitive and emotional battery to predict treatment remission in depression. Neuropsychiatr. Dis. Treat. 11:517-31
59. Green KC, Armstrong JS. 2015. Simple versus complex forecasting: the evidence. J. Bus. Res. 68:1678-85
60. Grove WM, Meehl PE. 1996. Comparative efficiency of informal (subjective, impressionistic) and formal (mechanical, algorithmic) prediction procedures: the clinical-statistical controversy. Psychol. Public Policy Law 2:293-323
61. GroveWM, Zald DH, Lebow BS, Snitz BE, Nelson C. 2000. Clinical versus mechanical prediction: a meta-analysis. Psychol. Assess. 12:19-30
62. Gunn J, Wachtler C, Fletcher S, Davidson S, Mihalopoulos C, et al. 2017. Target-D: a stratified individually randomized controlled trial of the diamond clinical prediction tool to triage and target treatment for depressive symptoms in general practice: study protocol for a randomized controlled trial. Trials 18:342
63. Gunter L, Zhu J, Murphy S. 2011a. Variable selection for qualitative interactions. Stat. Methodol. 8:42-55
64. Gunter L, Zhu J, Murphy S. 2011b. Variable selection for qualitative interactions in personalized medicine while controlling the family-wise error rate. J. Biopharm. Stat. 21:1063-78
65. Hamburg MA, Collins FS. 2010. The path to personalized medicine. N. Engl. J. Med. 2010:301-4
66. Hastie T, Tibshirani R, Friedman J. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer. 2nd ed.
67. Hingorani AD, van der Windt DA, Riley RD, Abrams K, Moons KG, et al. 2013. Prognosis research strategy (PROGRESS) 4: stratified medicine research. BMJ 346:e5793
68. Hollon SD, Arean PA, CraskeMG, Crawford KA, Kivlahan DR, et al. 2014. Development of clinical practice guidelines. Annu. Rev. Clin. Psychol. 10:213-41
69. Hollon SD, Thase ME, Markowitz JC. 2002. Treatment and prevention of depression. Psychol. Sci. Public Interest 3:39-77
70. Holmes EA, Craske MG, Graybiel AM. 2014. A call for mental-health science. Nature 511:287-89
71. Howland RH. 2014. Pharmacogenetic testing in psychiatry: not (quite) ready for primetime. J. Psychosoc. Nurs. Ment. Health Serv. 52:13-16
72. Huang Y, Gilbert PB, Janes H. 2012. Assessing treatment-selection markers using a potential outcomes framework. Biometrics 68:687-96
73. Huang Y, Laber EB, Janes H. 2015. Characterizing expected benefits of biomarkers in treatment selection. Biostatistics 16:383-99
74. Huibers MJ, Cohen ZD, Lemmens LH, Arntz A, Peeters FP, et al. 2015. Predicting optimal outcomes in cognitive therapy or interpersonal psychotherapy for depressed individuals using the Personalized Advantage Index Approach. PLOS ONE 10:e0140771
75. Hunter AM, Cook IA, Greenwald S, Tran ML, Miyamoto KN, Leuchter AF. 2011. The Antidepressant Treatment Response (ATR) index and treatment outcomes in a placebo-controlled trial of fluoxetine. J. Clin. Neurophysiol. 28:478-82
76. Imai K, RatkovicM. 2013. Estimating treatment effect heterogeneity in randomized program evaluation. Ann. Appl. Stat. 7:443-70
77. Iniesta R, Malki K, Maier W, Rietschel M, Mors O, et al. 2016a. Combining clinical variables to optimize prediction of antidepressant treatment outcomes. J. Psychiatr. Res. 78:94-102
78. Iniesta R, Stahl D, McGuffin P. 2016b. Machine learning, statistical learning and the future of biological research in psychiatry. Psychol. Med. 46:2455-65
79. Ioannidis JP. 2005. Why most published research findings are false. PLOS Med. 2:e124
80. James G,Witten D, Hastie T, Tibshirani R. 2013. An Introduction to Statistical Learning. New York: Springer
81. Jamshidian M, Jalal S. 2010. Tests of homoscedasticity, normality, and missing completely at random for incomplete multivariate data. Psychometrika 75:649-74
82. Janes H, Pepe MS, Bossuyt PM, Barlow WE. 2011. Measuring the performance of markers for guiding treatment decisions. Ann. Intern. Med. 154:253-59
83. Jollans L,Whelan R. 2016. The clinical added value of imaging: a perspective from outcome prediction. Biol. Psychiatry: Cogn. Neurosci. Neuroimaging 1:423-32
84. Kapelner A, Bleich J. 2016. bartMachine: A powerful tool for machine learning. J. Stat. Softw. 70:1-40
85. Katsnelson A. 2013. Momentum grows to make "personalized" medicine more "precise." Nat. Med. 19:249
86. Keefe JR, Wiltsey-Stirman S, Cohen ZD, DeRubeis RJ, Smith BN, Resick P. 2018. In rape-trauma PTSD, patient characteristics indicate which trauma-focused treatment they are most likely to complete. Depression Anxiety. In press
87. Kessler RC. 2018. The potential of predictive analytics to provide clinical decision support in depression treatment planning. Curr. Opin. Psychiatry 31:32-39
88. Kessler RC, van Loo HM, Wardenaar KJ, Bossarte RM, Brenner LA, et al. 2016. Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports. Mol. Psychiatry 21:1366-71
89. Kessler RC, van LooHM, Wardenaar KJ, Bossarte RM, Brenner LA, et al. 2017. Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder. Epidemiol. Psychiatr. Sci. 26:22-36
90. Khan A, Leventhal RM, Khan SR, BrownWA. 2002. Severity of depression and response to antidepressants and placebo: an analysis of the Food andDrug Administration database. J. Clin. Psychopharmacol. 22:40-45
91. King M, Walker C, Levy G, Bottomley C, Royston P, Weich S. 2008. Development and validation of an international risk prediction algorithm for episodes of major depression in general practice attendees: the PredictD study. Arch. Gen. Psychiatry 65:1368-76
92. Kingslake J, Dias R, Dawson GR, Simon J, Goodwin GM, et al. 2017. The effects of using the PReDicT Test to guide the antidepressant treatment of depressed patients: study protocol for a randomised controlled trial. Trials 18:558
93. Kirsch I, Deacon BJ, Huedo-Medina TB, Scoboria A, Moore TJ, Johnson BT. 2008. Initial severity and antidepressant benefits: a meta-analysis of data submitted to the Food and Drug Administration. PLOS Med. 5:e45
94. Klerman GL, Weissman MM. 1994. Interpersonal Psychotherapy of Depression: A Brief, Focused, Specific Strategy. Lanham, MD: Jason Aronson, Inc.
95. Kocsis JH, Leon AC, Markowitz JC, Manber R, Arnow B, et al. 2009. Patient preference as a moderator of outcome for chronic forms of major depressive disorder treated with nefazodone, cognitive behavioral analysis system of psychotherapy, or their combination. J. Clin. Psychiatry 70:354-61
96. Kraemer HC. 2013. Discovering, comparing, and combining moderators of treatment on outcome after randomized clinical trials: a parametric approach. Stat. Med. 32:1964-73
97. Kraemer HC, Blasey CM. 2004. Centring in regression analyses: a strategy to prevent errors in statistical inference. Int. J. Methods Psychiatr. Res. 13:141-51
98. Kuhn M, Johnson K. 2013. Applied Predictive Modeling. New York: Springer
99. Lam RW, Milev R, Rotzinger S, Andreazza AC, Blier P, et al. 2016. Discovering biomarkers for antidepressant response: protocol from the Canadian Biomarker Integration Network in Depression (CAN-BIND) and clinical characteristics of the first patient cohort. BMC Psychiatry 16:105
100. Layard R, ClarkD, Knapp M, Mayraz G. 2007. Cost-benefit analysis of psychological therapy. Natl. Inst. Econ. Rev. 202:90-98
101. Leuchter AF, Cook IA, Gilmer WS, Marangell LB, Burgoyne KS, et al. 2009. Effectiveness of a quantitative electroencephalographic biomarker for predicting differential response or remission with escitalopram and bupropion in major depressive disorder. Psychiatry Res. 169:132-38
102. Leykin Y, Amsterdam JD, DeRubeis RJ, Gallop R, Shelton RC,Hollon SD. 2007a. Progressive resistance to a selective serotonin reuptake inhibitor but not to cognitive therapy in the treatment of major depression. J. Consult. Clin. Psychol. 75:267
103. Leykin Y, DeRubeis RJ, Gallop R, Amsterdam JD, Shelton RC, Hollon SD. 2007b. The relation of patients' treatment preferences to outcome in a randomized clinical trial. Behavior. Ther. 38:209-17
104. Lo A, Chernoff H, Zheng T, Lo S-H. 2015. Why significant variables aren't automatically good predictors. Proc. Natl. Acad. Sci. 112:13892-97
105. Lorenzo-Luaces L, DeRubeis RJ, Bennett IM. 2015. Primary care physicians' selection of low-intensity treatments for patients with depression. Fam. Med. 47:511-16
106. Lorenzo-Luaces L, DeRubeis RJ, van Straten A, Tiemens B. 2017. A prognostic index (PI) as a moderator of outcomes in the treatment of depression: a proof of concept combining multiple variables to inform risk-stratified stepped care models. J. Affect. Disord. 213:78-85
107. Luedtke AR, van der Laan MJ. 2016. Super-learning of an optimal dynamic treatment rule. Int. J. Biostat. 12:305-32
108. Lutz W, Hofmann SG, Rubel J, Boswell JF, Shear MK, et al. 2014. Patterns of early change and their relationship to outcome and early treatment termination in patients with panic disorder. J. Consult. Clin. Psychol. 82:287
109. Lutz W, Saunders SM, Leon SC, Martinovich Z, Kosfelder J, et al. 2006. Empirically and clinically useful decisionmaking in psychotherapy: differential predictions with treatment response models. Psychol. Assess. 18:133-41
110. Lutz W, Zimmermann D, M uller VN, Deisenhofer A-K, Rubel JA. 2017. Randomized controlled trial to evaluate the effects of personalized prediction and adaptation tools on treatment outcome in outpatient psychotherapy: study protocol. BMC Psychiatry 17:306
111. Ma J, Stingo FC, Hobbs BP. 2016. Bayesian predictive modeling for genomic based personalized treatment selection. Biometrics 72:575-83
112. MacKinnon DP, Fairchild AJ, Fritz MS. 2007. Mediation analysis. Annu. Rev. Psychol. 58:593-614
113. Mayberg HS, Lozano AM, Voon V, McNeely HE, Seminowicz D, et al. 2005. Deep brain stimulation for treatment-resistant depression. Neuron 45:651-60
114. McCullough JP Jr. 2003. Treatment for chronic depression: cognitive behavioral analysis system of psychotherapy (CBASP). J. Pschother. Integr. 13:241-63
115. McGirr A, Berlim M, Bond D, Fleck M, Yatham L, Lam R. 2015. A systematic review and meta-analysis of randomized, double-blind, placebo-controlled trials of ketamine in the rapid treatment ofmajor depressive episodes. Psychol. Med. 45:693-704
116. McGrath CL, KelleyME, Holtzheimer PE, Dunlop BW, Craighead WE, et al. 2013. Toward a neuroimaging treatment selection biomarker for major depressive disorder. JAMA Psychiatry 70:821-29
117. McHugh RK,Whitton SW, Peckham AD, Welge JA, Otto MW. 2013. Patient preference for psychological versus pharmacologic treatment of psychiatric disorders: a meta-analytic review. J. Clin. Psychiatry 74:595-602
118. Meehl PE. 1954. Clinical versus Statistical Prediction: A Theoretical Analysis and a Review of the Evidence. Washington, DC: Am. Psychol. Assoc. http://psycnet.apa.org/record/2006-21565-000
119. Meehl PE. 1978. Theoretical risks and tabular asterisks: Sir Karl, Sir Ronald, and the slow progress of soft psychology. J. Consult. Clin. Psychol. 46:806-34
120. Mergl R, Henkel V, Allgaier AK, Kramer D, Hautzinger M, et al. 2011. Are treatment preferences relevant in response to serotonergic antidepressants and cognitive-behavioral therapy in depressed primary care patients?Results from a randomized controlled trial including a patients' choice arm. Psychother. Psychosom. 80:39-47
121. Mickey RM, Greenland S. 1989. The impact of confounder selection criteria on effect estimation. Am. J. Epidemiol. 129:125-37
122. National Health Service. 2016. Psychological Therapies, Annual Report on the Use of IAPT Services: England 2015-16. London, UK: Health Soc. Care Inf. Cent.
123. National Institute for Health and Clinical Excellence. 2009. Depression: Treatment and Management of Depression in Adults. London: Br. Psychol. Soc.
124. National Research Council. 2011. Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease. Washington, DC: Natl. Acad. Press
125. Nemeroff CB,Heim CM, ThaseME, Klein DN, Rush AJ, et al. 2003. Differential responses to psychotherapy versus pharmacotherapy in patients with chronic forms of major depression and childhood trauma. Proc. Natl. Acad. Sci. 100:14293-96
126. Nigatu YT, Liu Y, Wang J. 2016. External validation of the international risk prediction algorithm for major depressive episode in the US general population: the PredictD-US study. BMC Psychiatry 16:256
127. Niles AN, Loerinc AG, Krull JL, Roy-Byrne P, Sullivan G, et al. 2017a. Advancing personalized medicine: application of a novel statistical method to identify treatment moderators in the Coordinated Anxiety Learning and Management Study. Behav. Ther. 48:490-500
128. Niles AN, Wolitzky-Taylor KB, Arch JJ, Craske MG. 2017b. Applying a novel statistical method to advance the personalized treatment of anxiety disorders: a composite moderator of comparative drop-out from CBT and ACT. Behav. Res. Ther. 91:13-23
129. Nuzzo R. 2014. Scientific method: statistical errors. Nature 506:150-52
130. Open Science Collaboration. 2015. Estimating the reproducibility of psychological science. Science 349:aac4716
131. Paez JG, Janne PA, Lee JC, Tracy S, Greulich H, et al. 2004. EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science 304:1497-500
132. PaoW, Miller VA. 2005. Epidermal growth factor receptor mutations, small-molecule kinase inhibitors, and non-small-cell lung cancer: current knowledge and future directions. J. Clin. Oncol. 23:2556-68
133. Papakostas GI, Fava M. 2010. Pharmacotherapy for Depression and Treatment-Resistant Depression. Singapore: World Scientific
134. Parmigiani G. 2002. Modeling in Medical Decision Making: A Bayesian Approach. Chichester, UK:Wiley
135. Passos IC, Mwangi B, Kapczinski F. 2016. Big data analytics and machine learning: 2015 and beyond. Lancet Psychiatry 3:13-15
136. Pauker SG, Kassirer JP. 1980. The threshold approach to clinical decisionmaking. N. Engl. J.Med. 302:1109-17
137. Paul GL. 1967. Strategy of outcome research in psychotherapy. J. Consult. Psychol. 31:109-18
138. Perlis R, Iosifescu D, Castro V, Murphy S, Gainer V, et al. 2012. Using electronic medical records to enable large-scale studies in psychiatry: treatment resistant depression as a model. Psychol. Med. 42:41-50
139. Perlis RH. 2013. A clinical risk stratification tool for predicting treatment resistance in major depressive disorder. Biol. Psychiatry 74:7-14
140. Perlis RH. 2016. Abandoning personalization to get to precision in the pharmacotherapy of depression. World Psychiatry 15:228-35
141. Perlis RH, Fijal B, Dharia S, Heinloth AN, Houston JP. 2010. Failure to replicate genetic associations with antidepressant treatment response in duloxetine-treated patients. Biol. Psychiatry 67:1110-13
142. Perlis RH, Patrick A, Smoller JW, Wang PS. 2009. When is pharmacogenetic testing for antidepressant response ready for the clinic? A cost-effectiveness analysis based on data from the STAR D study. Neuropsychopharmacology 34:2227-36
143. Petkova E, Ogden RT, Tarpey T, Ciarleglio A, Jiang B, et al. 2017. Statistical analysis plan for stage 1 EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care) study. Contemporary Clin. Trials Commun. 6:22-30
144. Pizzagalli DA. 2011. Frontocingulate dysfunction in depression: toward biomarkers of treatment response. Neuropsychopharmacology 36:183-206
145. Preference Collaborative Review Group. 2008. Patients' preferences within randomised trials: systematic review and patient level meta-analysis. BMJ 337:a1864
146. Raza GT, Holohan DR. 2015. Clinical treatment selection for posttraumatic stress disorder: suggestions for researchers and clinical trainers. Psychol. Trauma 7:547-54
147. Renjilian DA, Perri MG, Nezu AM, McKelvey WF, Shermer RL, Anton SD. 2001. Individual versus group therapy for obesity: effects of matching participants to their treatment preferences. J. Consult. Clin. Psychol. 69:717-21
148. Rosell R, Carcereny E, Gervais R, Vergnenegre A,Massuti B, et al. 2012. Erlotinib versus standard chemotherapy as first-line treatment for European patients with advanced EGFR mutation-positive non-small-cell lung cancer (EURTAC): a multicentre, open-label, randomised phase 3 trial. Lancet Oncol. 13:239-46
149. Rubenstein L, RayburnN,Keeler E, Ford D, Rost K, Sherbourne C. 2007. Predicting outcomes of primary care patients with major depression: development of a depression prognosis index. Psychiatr. Serv. 58:1049-56
150. Rush AJ, TrivediMH, Stewart JW, Nierenberg AA, Fava M, et al. 2011. CombiningMedications to Enhance Depression Outcomes (CO-MED): acute and long-term outcomes of a single-blind randomized study. Am. J. Psychiatry 168:689-701
151. Saunders R, Cape J, Fearon P, Pilling S. 2016. Predicting treatment outcome in psychological treatment services by identifying latent profiles of patients. J. Affect. Disord. 197:107-15
152. Schleidgen S, Klingler C, Bertram T, RogowskiWH, Marckmann G. 2013. What is personalized medicine: sharpening a vague term based on a systematic literature review. BMC Med. Ethics 14:55
153. Schneider RL, Arch JJ, Wolitzky-Taylor KB. 2015. The state of personalized treatment for anxiety disorders: a systematic review of treatment moderators. Clin. Psychol. Rev. 38:39-54
154. Schwaederle M, Zhao M, Lee JJ, Eggermont AM, Schilsky RL, et al. 2015. Impact of precision medicine in diverse cancers: a meta-analysis of phase II clinical trials. J. Clin. Oncol. 33:3817-25
155. Simon GE, Perlis RH. 2010. Personalized medicine for depression: can we match patients with treatments? Am. J. Psychiatry 167:1445-55
156. Smagula SF, Wallace ML, Anderson SJ, Karp JF, Lenze EJ, et al. 2016. Combining moderators to identify clinical profiles of patients who will, and will not, benefit from aripiprazole augmentation for treatment resistant late-life major depressive disorder. J. Psychiatr. Res. 81:112-18
157. Stephan KE, Schlagenhauf F, Huys QJ, Raman S, Aponte EA, et al. 2017. Computational neuroimaging strategies for single patient predictions. Neuroimage 145:180-99
158. Steyerberg E. 2008. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating. New York: Springer
159. Swift JK, Callahan JL. 2009. The impact of client treatment preferences on outcome: a meta-analysis. J. Clin. Psychol. 65:368-81
160. Swift JK, Callahan JL, Vollmer BM. 2011. Preferences. J. Clin. Psychol. 67(2):155-65
161. Tibshirani R. 1996. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B 58:267-88
162. Tiemens B, Bocker K, Kloos M. 2016. Prediction of treatment outcome in daily generalized mental health care practice: first steps towards personalized treatment by clinical decision support. Eur. J. Pers. Cent. Healthcare 4:24-32
163. Trivedi MH, McGrath PJ, Fava M, Parsey RV, Kurian BT, et al. 2016. Establishing moderators and biosignatures of antidepressant response in clinical care (EMBARC): rationale and design. J. Psychiatr. Res. 78:11-23
164. Trivedi MH, Rush AJ, Wisniewski SR, Nierenberg AA, Warden D, et al. 2006. Evaluation of outcomes with citalopram for depression using measurement-based care in STAR D: implications for clinical practice. Am. J. Psychiatry 163:28-40
165. Tversky A, Kahneman D. 1983. Extensional versus intuitive reasoning: the conjunction fallacy in probability judgment. Psychol. Rev. 90:293-315
166. Uher R, Huezo-Diaz P, Perroud N, Smith R, Rietschel M, et al. 2009. Genetic predictors of response to antidepressants in the GENDEP project. Pharmacogenom. J. 9:225-33
167. Uher R, Perlis R, Henigsberg N, Zobel A, Rietschel M, et al. 2012. Depression symptom dimensions as predictors of antidepressant treatment outcome: replicable evidence for interest-activity symptoms. Psychol. Med. 42:967-80
168. Uher R, Tansey KE, Dew T, Maier W, Mors O, et al. 2014. An inflammatory biomarker as a differential predictor of outcome of depression treatment with escitalopram and nortriptyline. Am. J. Psychiatry 171:1278-86
169. van Straten A, Tiemens B, Hakkaart L, Nolen W, Donker M. 2006. Stepped care versus matched care for mood and anxiety disorders: a randomized trial in routine practice. Acta Psychiatr. Scand. 113:468-76
170. Vittengl JR, Clark LA, ThaseME, Jarrett RB. 2017. Initial steps to inform selection of continuation cognitive therapy or fluoxetine for higher risk responders to cognitive therapy for recurrent major depressive disorder. Psychiatry Res. 253:174-81
171. Vittengl JR, Jarrett RB,Weitz E, Hollon SD, Twisk J, et al. 2016. Divergent outcomes in cognitive-behavioral therapy and pharmacotherapy for adult depression. Am. J. Psychiatry 173:481-90
172. Wallace ML, Frank E, Kraemer HC. 2013. A novel approach for developing and interpreting treatment moderator profiles in randomized clinical trials. JAMA Psychiatry 70:1241-47
173. Wang R, Ware JH. 2013. Detecting moderator effects using subgroup analyses. Prev. Sci. 14:111-20
174. Wasserstein RL, Lazar NA. 2016. The ASA's statement on p-values: context, process, and purpose. Am. Stat. 70:129-33
175. Watkins E, Newbold A, Tester-Jones M, Javaid M, Cadman J, et al. 2016. Implementing multifactorial psychotherapy research in online virtual environments (IMPROVE-2): study protocol for a phase III trial of the MOST randomized component selection method for internet cognitive-behavioural therapy for depression. BMC Psychiatry 16:345
176. Weisz JR, Krumholz LS, Santucci L, Thomassin K, Ng MY. 2015. Shrinking the gap between research and practice: tailoring and testing youth psychotherapies in clinical care contexts. Annu. Rev. Clin. Psychol. 11:139-63
177. Weitz ES, Hollon SD, Twisk J, van Straten A, Huibers MJ, et al. 2015. Baseline depression severity as moderator of depression outcomes between cognitive behavioral therapy versus pharmacotherapy: an individual patient data meta-analysis. JAMA Psychiatry 72:1102-9
178. Wellek S. 1997. Testing for absence of qualitative interactions between risk factors and treatment effects. Biometrical J. 39:809-21
179. Westover AN, Kashner TM, Winhusen TM, Golden RM, Nakonezny PA, et al. 2015. A systematic approach to subgroup analyses in a smoking cessation trial. Am. J. Drug Alcohol Abuse 41:498-507
180. Widaman KF, Helm JL, Castro-Schilo L, Pluess M, Stallings MC, Belsky J. 2012. Distinguishing ordinal and disordinal interactions. Psychol. Methods 17:615-22
181. Williams LM, Rush AJ, Koslow SH, Wisniewski SR, Cooper NJ, et al. 2011. International Study to Predict Optimized Treatment for Depression (iSPOT-D), a randomized clinical trial: rationale and protocol. Trials 12:4
182. Winter SE, Barber JP. 2013. Should treatment for depression be based more on patient preference? Patient Pref. Adher. 7:1047-57
183. World Health Organization. 2017. Depression and other common mental disorders: global health estimates. Rep. CC BY-NC-SA 3.0 IGO, World Health Org., Geneva
184. Yakovlev AY, Goot RE, Osipova TT. 1994. The choice of cancer treatment based on covariate information. Stat. Med. 13:1575-81
185. Yarkoni T, Westfall J. 2017. Choosing prediction over explanation in psychology: Lessons from machine learning. Perspect. Psychol. Sci. 12:1100-22
186. Zilcha-Mano S, Keefe JR, Chui H, Rubin A, Barrett MS, Barber JP. 2016. Reducing dropout in treatment for depression: translating dropout predictors into individualized treatment recommendations. J. Clin. Psychiatry 77:e1584-e90
187. Zimmerman M, ClarkHL, Multach MD,Walsh E, Rosenstein LK, Gazarian D. 2015. Have treatment studies of depression become even less generalizable? A review of the inclusion and exclusion criteria used in placebo-controlled antidepressant efficacy trials published during the past 20 years. Mayo Clin. Proc. 90:1180-86
188. Zimmerman M, Clark HL, Multach MD, Walsh E, Rosenstein LK, Gazarian D. 2016. Symptom severity and the generalizability of antidepressant efficacy trials: changes during the past 20 years. J. Clin. Psychopharmacol. 36:153-56
189. ZouH,HastieT. 2005. Regularization and variable selection via the elastic net. J. R. Stat. Soc.: Ser. B 67:301-20