Using classification trees to identify psychotherapy patients at risk for poor treatment adherence

References

• Aafjes-van Doorn, K., Kamsteeg, C., Bate, J., & Aafjes, M. (2021). A scoping review of machine learning in psychotherapy research. Psychotherapy Research, 31(1), 92–116. https://doi.org/10.1080/10503307.2020.1808729
   PubMed Web of Science ®Google Scholar
• Barrett, M. S., Chua, W.-J., Crits-Christoph, P., Gibbons, M. B., & Thompson, D. (2008). Early withdrawal from mental health treatment: Implications for psychotherapy practice. Psychotherapy: Theory, Research, Practice, Training, 45(2), 247–267. https://doi.org/10.1037/0033-3204.45.2.247
   PubMed Web of Science ®Google Scholar
• Baruch, G., Vrouva, I., & Fearon, P. (2009). A follow-up study of characteristics of young people that dropout and continue psychotherapy: Service implications for a clinic in the community. Child and Adolescent Mental Health, 14(2), 69–75. https://doi.org/10.1111/j.1475-3588.2008.00492.x
   Web of Science ®Google Scholar
• Bennemann, B., Schwartz, B., Giesemann, J., & Lutz, W. (2022). Predicting patients who will drop out of out-patient psychotherapy using machine learning algorithms. The British Journal of Psychiatry, 220(4), 192–201.
   Web of Science ®Google Scholar
• Berghuis, H., Bandell, C. C., & Krueger, R. F. (2021). Predicting dropout using DSM–5 section II personality disorders, and DSM–5 section III personality traits, in a (day)clinical sample of personality disorders.. Personality Disorders: Theory, Research, and Treatment, 12(4), 331–338. https://doi.org/10.1037/per0000429
   Web of Science ®Google Scholar
• Bohart, A. C., & Wade, A. G. (2013). The client in psychotherapy. In M. Lambert (Ed.), Bergin and Garfield’s handbook of psychotherapy and behavior change (Vol. 6, pp. 219–257). John Wiley & Sons, Inc.
   Google Scholar
• Brorson, H. H., Arnevik, E. A., Rand-Hendriksen, K., & Duckert, F. (2013). Drop-out from addiction treatment: A systematic review of risk factors. Clinical Psychology Review, 33(8), 1010–1024. https://doi.org/10.1016/j.cpr.2013.07.007
   PubMed Web of Science ®Google Scholar
• Buizza, C., Ghilardi, A., Olivetti, E., & Costa, A. (2019). Dropouts from a university counselling service: A quantitative and qualitative study. British Journal of Guidance & Counselling, 47(5), 590–602. https://doi.org/10.1080/03069885.2019.1566513
   Web of Science ®Google Scholar
• Cahill, J., Barkham, M., Hardy, G., Rees, A., Shapiro, D. A., Stiles, W. B., & Macaskill, N. (2003). Outcomes of patients completing and not completing cognitive therapy for depression. British Journal of Clinical Psychology, 42(2), 133–143. https://doi.org/10.1348/014466503321903553
   PubMed Web of Science ®Google Scholar
• Dietterich, T. (1995). Overfitting and undercomputing in machine learning. ACM Computing Surveys, 27(3), 326–327. https://doi.org/10.1145/212094.212114
   Web of Science ®Google Scholar
• Farber, B. A., Suzuki, J. Y., & Lynch, D. A. (2018). Positive regard and psychotherapy outcome: A meta-analytic review. Psychotherapy, 55(4), 411–423. https://doi.org/10.1037/pst0000171
   PubMed Web of Science ®Google Scholar
• Fassino, S., Abbate-Daga, G., Pierò, A., Leombruni, P., & Rovera, G. G. (2003). Dropout from brief psychotherapy within a combination treatment in bulimia nervosa: Role of personality and anger. Psychotherapy and Psychosomatics, 72(4), 203–210. https://doi.org/10.1159/000070784
   PubMed Web of Science ®Google Scholar
• Fleming, C. J. E., Kholodkov, T., Dillon, K. H., Belvet, B., & Crawford, E. F. (2018). Actuarial prediction of psychotherapy retention among Iraq–Afghanistan veterans with posttraumatic stress disorder. Psychological Services, 15(2), 172–180. https://doi.org/10.1037/ser0000139
   PubMed Web of Science ®Google Scholar
• Flückiger, C., Del Re, A. C., Wampold, B. E., Symonds, D., & Horvath, A. O. (2012). How central is the alliance in psychotherapy? A multilevel longitudinal meta-analysis. Journal of Counseling Psychology, 59(1), 10–17. https://doi.org/10.1037/a0025749
   PubMed Web of Science ®Google Scholar
• Garcia, H. A., Kelley, L. P., Rentz, T. O., & Lee, S. (2011). Pretreatment predictors of dropout from cognitive behavioral therapy for PTSD in Iraq and Afghanistan war veterans. Psychological Services, 8(1), 1–11. https://doi.org/10.1037/a0022705
   Web of Science ®Google Scholar
• Goode, J., Park, J., Parkin, S., Tompkins, K. A., & Swift, J. K. (2017). A collaborative approach to psychotherapy termination. Psychotherapy, 54(1), 10–14. https://doi.org/10.1037/pst0000085
   PubMed Web of Science ®Google Scholar
• Hampton-Robb, S., Qualls, R. C., & Compton, W. C. (2003). Predicting first-session attendance: The influence of referral source and client income. Psychotherapy Research, 13(2), 223–233. https://doi.org/10.1080/713869641
   Web of Science ®Google Scholar
• Hansen, N., Kershaw, T., Kochman, A., & Sikkema, K. (2007). A classification and regression trees analysis predicting treatment outcome following a group intervention randomized controlled trial for HIV-positive adult survivors of childhood sexual abuse. Psychotherapy Research, 17(4), 404–415. https://doi.org/10.1080/10503300600953512
   Web of Science ®Google Scholar
• Heinonen, E., & Nissen-Lie, H. A. (2020). The professional and personal characteristics of effective psychotherapists: A systematic review. Psychotherapy Research, 30(4), 417–432. https://doi.org/10.1080/10503307.2019.1620366
   PubMed Web of Science ®Google Scholar
• Kattan, M. W., Hess, K. R., & Beck, J. R. (1998). Experiments to determine whether recursive partitioning (CART) or an artificial neural network overcomes theoretical limitations of Cox proportional hazards regression. Computers and Biomedical Research, 31(5), 363–373. https://doi.org/10.1006/cbmr.1998.1488
   PubMedGoogle Scholar
• Kingsford, C., & Salzberg, S. L. (2008). What are decision trees? Nature Biotechnology, 26(9), 1011–1013. https://doi.org/10.1038/nbt0908-1011
   PubMed Web of Science ®Google Scholar
• Lamkin, J., Hundt, N., Ahearn, E. P., Stanley, M., & Smith, T. L. (2019). Patterns of treatment disengagement and personality traits associated with posttraumatic stress disorder in recent-era U.S. veterans receiving cognitive processing therapy. Journal of Traumatic Stress, 32(4), 625–632. https://doi.org/10.1002/jts.22428
   PubMed Web of Science ®Google Scholar
• Lampropoulos, G. K. (2010). Type of counseling termination and trainee therapist–client agreement about change. Counselling Psychology Quarterly, 23(1), 111–120. https://doi.org/10.1080/09515071003721552
   Google Scholar
• Lefforge, N. L., Donohue, B., & Strada, M. J. (2007). Improving session attendance in mental health and substance abuse settings: A review of controlled studies. Behavior Therapy, 38(1), 1–22. https://doi.org/10.1016/j.beth.2006.02.009
   PubMed Web of Science ®Google Scholar
• Lutz, W., Rubel, J. A., Schwartz, B., Schilling, V., & Deisenhofer, A.-K. (2019). Towards integrating personalized feedback research into clinical practice: Development of the trier treatment navigator (TTN). Behaviour Research and Therapy, 120, 103438. https://doi.org/10.1016/j.brat.2019.103438
   PubMed Web of Science ®Google Scholar
• Maples, J. L., & Walker, R. L. (2014). Consolidation rather than termination: Rethinking how psychologists label and conceptualize the final phase of psychological treatment. Professional Psychology: Research and Practice, 45(2), 104–110. https://doi.org/10.1037/a0036250
   Web of Science ®Google Scholar
• Masías, V. H., Krause, M., Valdés, N., Pérez, J. C., & Laengle, S. (2015). Using decision trees to characterize verbal communication during change and stuck episodes in the therapeutic process. Frontiers in Psychology, 6, 379. https://doi.org/10.3389/fpsyg.2015.00379
   PubMed Web of Science ®Google Scholar
• McCredie, M. N., Kurtz, J. E., & Valentine, L. (2018). Prediction of psychotherapy process and outcome with the personality assessment inventory. Psychiatry Research, 269, 455–461. https://doi.org/10.1016/j.psychres.2018.08.110
   PubMed Web of Science ®Google Scholar
• Mitchell, A. J., & Selmes, T. (2007). Why don't patients attend their appointments? Maintaining engagement with psychiatric services. Advances in Psychiatric Treatment, 13(6), 423–434. http://doi.org/10.1192/apt.bp.106.003202
   Google Scholar
• Morey, L. C. (1991). Personality assessment inventory professional manual. Psychological Assessment Resources.
   Google Scholar
• Niles, B. L., Polizzi, C. P., Voelkel, E., Weinstein, E. S., Smidt, K., & Fisher, L. M. (2018). Initiation, dropout, and outcome from evidence-based psychotherapies in a VA PTSD outpatient clinic. Psychological Services, 15(4), 496–502. https://doi.org/10.1037/ser0000175
   PubMed Web of Science ®Google Scholar
• Oldham, M., Kellett, S., Miles, E., & Sheeran, P. (2012). Interventions to increase attendance at psychotherapy: A meta-analysis of randomized controlled trials. Journal of Consulting and Clinical Psychology, 80(5), 928–939. https://doi.org/10.1037/a0029630
   PubMed Web of Science ®Google Scholar
• Olfson, M., Mojtabai, R., Sampson, N. A., Hwang, I., Druss, B., Wang, P. S., Wells, K. B., Pincus, H. A., & Kessler, R. C. (2009). Dropout from outpatient mental health care in the United States. Psychiatric Services, 60(7), 898–907. https://doi.org/10.1176/ps.2009.60.7.898
   PubMed Web of Science ®Google Scholar
• Page, A. C., & Hooke, G. R. (2009). Best practices: Increased attendance in inpatient group psychotherapy improves patient outcomes. Psychiatric Services, 60(4), 426–428. https://doi.org/10.1176/ps.2009.60.4.426
   PubMed Web of Science ®Google Scholar
• Park, S. J., Lee, S. J., Kim, H., Kim, J.-K., Chun, J.-W., Lee, S. J., Lee, H. K., Kim, D.-J., & Choi, I. Y. (2021). Machine learning prediction of dropping out of outpatients with alcohol use disorders. Plos One, 16(8), e0255626. https://doi.org/10.1371/journal.pone.0255626
   PubMed Web of Science ®Google Scholar
• Podgorelec, V., Kokol, P., Stiglic, B., & Rozman, I. (2002). Decision trees: An overview and their use in medicine. Journal of Medical Systems, 26(5), 445–463. https://doi.org/10.1023/A:1016409317640
   PubMedGoogle Scholar
• R Core Team. (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/.
   Google Scholar
• Reardon, M. L., Cukrowicz, K. C., Reeves, M. D., & Joiner, T. E. (2002). Duration and regularity of therapy attendance as predictors of treatment outcome in an adult outpatient population. Psychotherapy Research, 12(3), 273–285. https://doi.org/10.1080/713664390
   Web of Science ®Google Scholar
• Rokach, L., & Maimon, O. (2009). Classification trees. In O. Maimon & L. Rokach (Eds.), Data mining and knowledge discovery handbook (pp. 149–174). Springer.
   Google Scholar
• Schwebel, F. J., Richards, D. K., Pfund, R. A., Joseph, V. W., Pearson, M. R., & Marijuana Outcomes Study Team. (2022). Using decision trees to identify salient predictors of cannabis-related outcomes. Journal of Psychoactive Drugs, 54(4), 419–428. https://doi.org/10.1080/02791072.2021.2014081
   PubMed Web of Science ®Google Scholar
• Shattock, L., Berry, K., Degnan, A., & Edge, D. (2018). Therapeutic alliance in psychological therapy for people with schizophrenia and related psychoses: A systematic review. Clinical Psychology & Psychotherapy, 25(1), e60–e85. https://doi.org/10.1002/cpp.2135
   PubMed Web of Science ®Google Scholar
• Strobl, C., Malley, J., & Tutz, G. (2009). An introduction to recursive partitioning: Rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychological Methods, 14(4), 323–348. https://doi.org/10.1037/a0016973
   PubMed Web of Science ®Google Scholar
• Stulz, N., Lutz, W., Kopta, S. M., Minami, T., & Saunders, S. M. (2013). Dose–effect relationship in routine outpatient psychotherapy: Does treatment duration matter? Journal of Counseling Psychology, 60(4), 593–600. https://doi.org/10.1037/a0033589
   PubMed Web of Science ®Google Scholar
• Swift, J. K., & Greenberg, R. P. (2012). Premature discontinuation in adult psychotherapy: A meta-analysis. Journal of Consulting and Clinical Psychology, 80(4), 547–559. https://doi.org/10.1037/a0028226
   PubMed Web of Science ®Google Scholar
• Therneau, T., Atkinson, B., & Ripley, B. (2019). rpart: Recursive partitioning and regression trees. R package version 4.1-15.
   Google Scholar
• Werbart, A., & Wang, M. (2012). Predictors of not starting and dropping out from psychotherapy in Swedish public service settings. Nordic Psychology, 64(2), 128–146. https://doi.org/10.1080/19012276.2012.726817
   Web of Science ®Google Scholar
• Winhusen, T., Kropp, F., Babcock, D., Hague, D., Erickson, S. J., Renz, C., Rau, L., Lewis, D., Leimberger, J., & Somoza, E. (2008). Motivational enhancement therapy to improve treatment utilization and outcome in pregnant substance users. Journal of Substance Abuse Treatment, 35(2), 161–173. https://doi.org/10.1016/j.jsat.2007.09.006
   PubMed Web of Science ®Google Scholar
• Wnuk, S., McMain, S., Links, P. S., Habinski, L., Murray, J., & Guimond, T. (2013). Factors related to dropout from treatment in two outpatient treatments for borderline personality disorder. Journal of Personality Disorders, 27(6), 716–726. https://doi.org/10.1521/pedi_2013_27_106
   PubMed Web of Science ®Google Scholar