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Abstract

Mental health researchers and clinicians have long sought answers to the question “What works for whom?” The goal of precision medicine is to provide evidence-based answers to this question. Treatment selection in depression aims to help each individual receive the treatment, among the available options, that is most likely to lead to a positive outcome for them. Although patient variables that are predictive of response to treatment have been identified, this knowledge has not yet translated into real-world treatment recommendations. The Personalized Advantage Index (PAI) and related approaches combine information obtained prior to the initiation of treatment into multivariable prediction models that can generate individualized predictions to help clinicians and patients select the right treatment. With increasing availability of advanced statistical modeling approaches, as well as novel predictive variables and big data, treatment selection models promise to contribute to improved outcomes in depression.

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2018-05-07
2024-03-28
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Literature Cited

  1. Allen J, Mattson M, Miller W, Tonigan J, Connors G. et al. 1997. Matching alcoholism treatments to client heterogeneity. J. Stud. Alcohol 58:7–29 [Google Scholar]
  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/practice_guidelines/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 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  7. Austin PC, Tu JV. 2004. Bootstrap methods for developing predictive models. Am. Stat. 58:131–37 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  11. Beck AT, Rush AJ, Shaw BF, Emery G. 1979. Cognitive Therapy of Depression New York: Guilford Press
  12. Beutler LE, Engle D, Mohr D, Daldrup RJ, Bergan J. et al. 1991. Predictors of differential response to cognitive, experiential, and self-directed psychotherapeutic procedures. J. Consult. Clin. Psychol. 59:333–40 [Google Scholar]
  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 [Google Scholar]
  14. Bossuyt PM, Parvin T. 2015. Evaluating biomarkers for guiding treatment decisions. EJIFCC 26:63–70 [Google Scholar]
  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. São Paulo Med. J. 133:252–63 [Google Scholar]
  16. Bursac Z, Gauss CH, Williams DK, Hosmer DW. 2008. Purposeful selection of variables in logistic regression. Source Code Biol. Med. 3:17 [Google Scholar]
  17. Byar DP. 1985. Assessing apparent treatment—covariate interactions in randomized clinical trials. Stat. Med. 4:255–63 [Google Scholar]
  18. Byar DP, Corle DK. 1977. Selecting optimal treatment in clinical trials using covariate information. J. Chronic Diseas. 30:445–59An early example of a prescriptive multivariable treatment selection model in medicine. [Google Scholar]
  19. Byrne SE, Rothschild AJ. 1998. Loss of antidepressant efficacy during maintenance therapy: possible mechanisms and treatments. J. Clin. Psychiatry 59:279–88 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  23. Chekroud AM, Gueorguieva R, Krumholz HM, Trivedi MH, Krystal JH, McCarthy G. 2017. Reevaluating the efficacy and predictability of antidepressant treatments: a symptom clustering approach. JAMA Psychiatry 74:370–78 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  26. Cohen Z, Kim T, Van R, Dekker J, Driessen E. 2017. Individual treatment recommendations of cognitive-behavioral 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, Treanor M, Dour HJ. 2016. Treatment for anhedonia: a neuroscience driven approach. Depress. Anxiety 33:927–38 [Google Scholar]
  28. Cronbach LJ. 1957. The two disciplines of scientific psychology. Am. Psychol. 12:671–84 [Google Scholar]
  29. Cuijpers P, Huibers MJ, Furukawa TA. 2017. The need for research on treatments of chronic depression. JAMA Psychiatry 74:242–43 [Google Scholar]
  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 [Google Scholar]
  31. Dawes RM. 1979. The robust beauty of improper linear models in decision making. Am. Psychol. 34:571–82Early article reviewing the superiority of and resistance to statistical decision making. [Google Scholar]
  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 [Google Scholar]
  33. Dawes RM, Faust D, Meehl PE. 1989. Clinical versus actuarial judgment. Science 243:1668–74 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  36. DeRubeis RJ, Cohen ZD, Forand NR, Fournier JC, Gelfand LA, Lorenzo-Luaces L. 2014.a The Personalized Advantage Index: translating research on prediction into individualized treatment recommendations. A demonstration. PLOS ONE 9:e83875The first published presentation of the Personalized Advantage Index (PAI) approach to treatment selection. [Google Scholar]
  37. DeRubeis RJ, Gelfand LA, German RE, Fournier JC, Forand NR. 2014.b Understanding processes of change: how some patients reveal more than others–and some groups of therapists less–about what matters in psychotherapy. Psychother. Res. 24:419–28A simulation study demonstrating the importance of patient types in mental health treatment research. [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  42. Dunlop BW, Binder EB, Cubells JF, Goodman MM, Kelley ME. et al. 2012.a Predictors of remission in depression to individual and combined treatments (PReDICT): study protocol for a randomized controlled trial. Trials 13:106 [Google Scholar]
  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 [Google Scholar]
  44. Dunlop BW, Kelley ME, Mletzko TC, Velasquez CM, Craighead WE, Mayberg HS. 2012.b Depression beliefs, treatment preference, and outcomes in a randomized trial for major depressive disorder. J. Psychiatr. Res. 46:375–81 [Google Scholar]
  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 [Google Scholar]
  46. Fernandez KC, Fisher AJ, Chi C. 2017. Development and initial implementation of the Dynamic Assessment Treatment Algorithm (DATA). PLOS ONE 12:e0178806 [Google Scholar]
  47. Fineberg NA, Brown A, Reghunandanan S, Pampaloni I. 2012. Evidence-based pharmacotherapy of obsessive-compulsive disorder. Int. J. Neuropsychopharmacol. 15:1173–91 [Google Scholar]
  48. Fisher AJ, Boswell JF. 2016. Enhancing the personalization of psychotherapy with dynamic assessment and modeling. Assessment 23:496–506 [Google Scholar]
  49. Forand NR, Huibers MJ, 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 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  53. Gabrieli JD, Ghosh SS, Whitfield-Gabrieli S. 2015. Prediction as a humanitarian and pragmatic contribution from human cognitive neuroscience. Neuron 85:11–26 [Google Scholar]
  54. Gail M, Simon R. 1985. Testing for qualitative interactions between treatment effects and patient subsets. Biometrics 41:361–72 [Google Scholar]
  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 [Google Scholar]
  56. Gillan CM, Daw ND. 2016. Taking psychiatry research online. Neuron 91:19–23 [Google Scholar]
  57. Gillan CM, Whelan R. 2017. What big data can do for treatment in psychiatry. Curr. Opin. Behav. Sci. 18:34–42Review of the potential for prediction, big data, and machine learning to advance psychiatry. [Google Scholar]
  58. Gordon E, Rush AJ, Palmer DM, Braund TA, Rekshan W. 2015. Toward an online cognitive and emotional battery to predict treatment remission in depression. Neuropsychiatr. Dis. Treat. 11:517–31 [Google Scholar]
  59. Green KC, Armstrong JS. 2015. Simple versus complex forecasting: the evidence. J. Bus. Res. 68:1678–85 [Google Scholar]
  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 [Google Scholar]
  61. Grove WM, Zald DH, Lebow BS, Snitz BE, Nelson C. 2000. Clinical versus mechanical prediction: a meta-analysis. Psychol. Assess. 12:19–30Meta-analysis finding that actuarial/mechanical/statistical prediction is consistently superior to clinical prediction. [Google Scholar]
  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 [Google Scholar]
  63. Gunter L, Zhu J, Murphy S. 2011.a Variable selection for qualitative interactions. Stat. Methodol. 8:42–55 [Google Scholar]
  64. Gunter L, Zhu J, Murphy S. 2011.b Variable selection for qualitative interactions in personalized medicine while controlling the family-wise error rate. J. Biopharm. Stat. 21:1063–78 [Google Scholar]
  65. Hamburg MA, Collins FS. 2010. The path to personalized medicine. N. Engl. J. Med. 2010:301–4 [Google Scholar]
  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 [Google Scholar]
  68. Hollon SD, Areán PA, Craske MG, Crawford KA, Kivlahan DR. et al. 2014. Development of clinical practice guidelines. Annu. Rev. Clin. Psychol. 10:213–41 [Google Scholar]
  69. Hollon SD, Thase ME, Markowitz JC. 2002. Treatment and prevention of depression. Psychol. Sci. Public Interest 3:39–77 [Google Scholar]
  70. Holmes EA, Craske MG, Graybiel AM. 2014. A call for mental-health science. Nature 511:287–89 [Google Scholar]
  71. Howland RH. 2014. Pharmacogenetic testing in psychiatry: not (quite) ready for primetime. J. Psychosoc. Nurs. Ment. Health Serv. 52:13–16 [Google Scholar]
  72. Huang Y, Gilbert PB, Janes H. 2012. Assessing treatment‐selection markers using a potential outcomes framework. Biometrics 68:687–96 [Google Scholar]
  73. Huang Y, Laber EB, Janes H. 2015. Characterizing expected benefits of biomarkers in treatment selection. Biostatistics 16:383–99 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  76. Imai K, Ratkovic M. 2013. Estimating treatment effect heterogeneity in randomized program evaluation. Ann. Appl. Stat. 7:443–70 [Google Scholar]
  77. Iniesta R, Malki K, Maier W, Rietschel M, Mors O. et al. 2016.a Combining clinical variables to optimize prediction of antidepressant treatment outcomes. J. Psychiatr. Res. 78:94–102 [Google Scholar]
  78. Iniesta R, Stahl D, McGuffin P. 2016.b Machine learning, statistical learning and the future of biological research in psychiatry. Psychol. Med. 46:2455–65 [Google Scholar]
  79. Ioannidis JP. 2005. Why most published research findings are false. PLOS Med 2:e124 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  84. Kapelner A, Bleich J. 2016. bartMachine: A powerful tool for machine learning. J. Stat. Softw. 70:1–40 [Google Scholar]
  85. Katsnelson A. 2013. Momentum grows to make “personalized” medicine more “precise.”. Nat. Med. 19:249 [Google Scholar]
  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
  87. Kessler RC. 2018. The potential of predictive analytics to provide clinical decision support in depression treatment planning. Curr. Opin. Psychiatry 31:32–39 [Google Scholar]
  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 [Google Scholar]
  89. Kessler RC, van Loo HM, 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 [Google Scholar]
  90. Khan A, Leventhal RM, Khan SR, Brown WA. 2002. Severity of depression and response to antidepressants and placebo: an analysis of the Food and Drug Administration database. J. Clin. Psychopharmacol. 22:40–45 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  98. Kuhn M, Johnson K. 2013. Applied Predictive Modeling New York: SpringerExcellent resource for those interested in building statistical prediction models.
  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 [Google Scholar]
  100. Layard R, Clark D, Knapp M, Mayraz G. 2007. Cost-benefit analysis of psychological therapy. Natl. Inst. Econ. Rev. 202:90–98 [Google Scholar]
  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 [Google Scholar]
  102. Leykin Y, Amsterdam JD, DeRubeis RJ, Gallop R, Shelton RC, Hollon SD. 2007.a 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 [Google Scholar]
  103. Leykin Y, DeRubeis RJ, Gallop R, Amsterdam JD, Shelton RC, Hollon SD. 2007.b The relation of patients’ treatment preferences to outcome in a randomized clinical trial. Behavior. Ther. 38:209–17 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  107. Luedtke AR, van der Laan MJ. 2016. Super-learning of an optimal dynamic treatment rule. Int. J. Biostat. 12:305–32 [Google Scholar]
  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 [Google Scholar]
  109. Lutz W, Saunders SM, Leon SC, Martinovich Z, Kosfelder J. et al. 2006. Empirically and clinically useful decision making in psychotherapy: differential predictions with treatment response models. Psychol. Assess. 18:133–41 [Google Scholar]
  110. Lutz W, Zimmermann D, Müller 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 [Google Scholar]
  111. Ma J, Stingo FC, Hobbs BP. 2016. Bayesian predictive modeling for genomic based personalized treatment selection. Biometrics 72:575–83 [Google Scholar]
  112. MacKinnon DP, Fairchild AJ, Fritz MS. 2007. Mediation analysis. Annu. Rev. Psychol. 58:593–614 [Google Scholar]
  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 [Google Scholar]
  114. McCullough JP Jr. 2003. Treatment for chronic depression: cognitive behavioral analysis system of psychotherapy (CBASP). J. Pschother. Integr. 13:241–63 [Google Scholar]
  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 of major depressive episodes. Psychol. Med. 45:693–704 [Google Scholar]
  116. McGrath CL, Kelley ME, Holtzheimer PE, Dunlop BW, Craighead WE. et al. 2013. Toward a neuroimaging treatment selection biomarker for major depressive disorder. JAMA Psychiatry 70:821–29 [Google Scholar]
  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 [Google Scholar]
  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 Seminal monograph of Paul Meehl's original lectures in which he championed statistical decision making.
  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 [Google Scholar]
  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 [Google Scholar]
  121. Mickey RM, Greenland S. 1989. The impact of confounder selection criteria on effect estimation. Am. J. Epidemiol. 129:125–37 [Google Scholar]
  122. National Health Service 2016. Psychological Therapies, Annual Report on the Use of IAPT Services: England 201516 London, UK: Health Soc. Care Inf. Cent [Google Scholar]
  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. PressReport summarizing recent progress in precision medicine, including compelling examples of treatment selection in cancer.
  125. Nemeroff CB, Heim CM, Thase ME, 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 [Google Scholar]
  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 [Google Scholar]
  127. Niles AN, Loerinc AG, Krull JL, Roy-Byrne P, Sullivan G. et al. 2017.a 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 [Google Scholar]
  128. Niles AN, Wolitzky-Taylor KB, Arch JJ, Craske MG. 2017.b 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 [Google Scholar]
  129. Nuzzo R. 2014. Scientific method: statistical errors. Nature 506:150–52 [Google Scholar]
  130. Open Science Collaboration 2015. Estimating the reproducibility of psychological science. Science 349:aac4716 [Google Scholar]
  131. Paez JG, Jänne 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 [Google Scholar]
  132. Pao W, 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 [Google Scholar]
  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 [Google Scholar]
  136. Pauker SG, Kassirer JP. 1980. The threshold approach to clinical decision making. N. Engl. J. Med. 302:1109–17 [Google Scholar]
  137. Paul GL. 1967. Strategy of outcome research in psychotherapy. J. Consult. Psychol. 31:109–18 [Google Scholar]
  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 [Google Scholar]
  139. Perlis RH. 2013. A clinical risk stratification tool for predicting treatment resistance in major depressive disorder. Biol. Psychiatry 74:7–14 [Google Scholar]
  140. Perlis RH. 2016. Abandoning personalization to get to precision in the pharmacotherapy of depression. World Psychiatry 15:228–35 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  144. Pizzagalli DA. 2011. Frontocingulate dysfunction in depression: toward biomarkers of treatment response. Neuropsychopharmacology 36:183–206 [Google Scholar]
  145. Preference Collaborative Review Group 2008. Patients’ preferences within randomised trials: systematic review and patient level meta-analysis. BMJ 337:a1864 [Google Scholar]
  146. Raza GT, Holohan DR. 2015. Clinical treatment selection for posttraumatic stress disorder: suggestions for researchers and clinical trainers. Psychol. Trauma 7:547–54 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  149. Rubenstein L, Rayburn N, 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 [Google Scholar]
  150. Rush AJ, Trivedi MH, Stewart JW, Nierenberg AA, Fava M. et al. 2011. Combining Medications to Enhance Depression Outcomes (CO-MED): acute and long-term outcomes of a single-blind randomized study. Am. J. Psychiatry 168:689–701 [Google Scholar]
  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 [Google Scholar]
  152. Schleidgen S, Klingler C, Bertram T, Rogowski WH, Marckmann G. 2013. What is personalized medicine: sharpening a vague term based on a systematic literature review. BMC Med. Ethics 14:55 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  155. Simon GE, Perlis RH. 2010. Personalized medicine for depression: can we match patients with treatments. ? Am. J. Psychiatry 167:1445–55 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  158. Steyerberg E. 2008. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating New York: SpringerComprehensive resource for building and evaluating prediction models for clinical applications.
  159. Swift JK, Callahan JL. 2009. The impact of client treatment preferences on outcome: a meta‐analysis. J. Clin. Psychol. 65:368–81 [Google Scholar]
  160. Swift JK, Callahan JL, Vollmer BM. 2011. Preferences. J. Clin. Psychol. 67:2155–65 [Google Scholar]
  161. Tibshirani R. 1996. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B 58:267–88 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  165. Tversky A, Kahneman D. 1983. Extensional versus intuitive reasoning: the conjunction fallacy in probability judgment. Psychol. Rev. 90:293–315 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  170. Vittengl JR, Clark LA, Thase ME, 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 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  173. Wang R, Ware JH. 2013. Detecting moderator effects using subgroup analyses. Prev. Sci. 14:111–20 [Google Scholar]
  174. Wasserstein RL, Lazar NA. 2016. The ASA's statement on p-values: context, process, and purpose. Am. Stat. 70:129–33 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  178. Wellek S. 1997. Testing for absence of qualitative interactions between risk factors and treatment effects. Biometrical J 39:809–21 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  182. Winter SE, Barber JP. 2013. Should treatment for depression be based more on patient preference. ? Patient Pref. Adher. 7:1047–57 [Google Scholar]
  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 [Google Scholar]
  185. Yarkoni T, Westfall J. 2017. Choosing prediction over explanation in psychology: Lessons from machine learning. Perspect. Psychol. Sci. 12:1100–22 [Google Scholar]
  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 [Google Scholar]
  187. Zimmerman M, Clark HL, 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 [Google Scholar]
  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 [Google Scholar]
  189. Zou H, Hastie T. 2005. Regularization and variable selection via the elastic net. J. R. Stat. Soc.: Ser. B 67:301–20 [Google Scholar]
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