Latent variable mixture modelling and individual treatment prediction

Behaviour Research and Therapy

Volumn 124, Issue , 2020, Pages

  Saunders, Rob ^a   Buckman, Joshua E J ^a   Pilling, Stephen ^a  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

IAPT; Latent profile analysis; Precision medicine; Psychotherapy; Treatment outcomes

Indexed keywords

ADULT; ALGORITHM; ARTICLE; COUNSELING; DETERIORATION; FEMALE; HUMAN; MAJOR CLINICAL STUDY; MALE; PATIENT CODING; PERSONALIZED MEDICINE; PREDICTION; PROTEIN FINGERPRINTING; PSYCHOTHERAPY; REMISSION; VALIDATION STUDY; ANXIETY DISORDER; DEPRESSION; MENTAL HEALTH; MIDDLE AGED; PROGNOSIS; PSYCHOLOGY; THEORETICAL MODEL; TREATMENT OUTCOME; YOUNG ADULT;

ADULT; ANXIETY DISORDERS; DEPRESSIVE DISORDER; FEMALE; HUMANS; MALE; MENTAL HEALTH; MIDDLE AGED; MODELS, THEORETICAL; PRECISION MEDICINE; PROGNOSIS; PSYCHOTHERAPY; TREATMENT OUTCOME; YOUNG ADULT;

EID: 85076238796     PISSN: 00057967     EISSN: 1873622X     Source Type: Journal    
DOI: 10.1016/j.brat.2019.103505     Document Type: Article

References (64)

1. Agrawal, A., Lynskey, M.T., Madden, P.A.F., Bucholz, K.K., Heath, A.C., A latent class analysis of illicit drug abuse/dependence: Results from the national epidemiological survey on alcohol and related conditions. Addiction, 2007, 10.1111/j.1360-0443.2006.01630.x.
2. Aiken, L.S., West, S.G., Multiple regression: Testing and interpreting interactions. 1991, Sage Publications, Newbury Park.
3. Ashley, E., The precision medicine initiative: A new national effort. Journal of the American Medical Association 313:21 (2015), 2119–2120, 10.1001/jamaoncol.2014.216.5.
4. Bayes, T., An essay towards solving a problem in the doctrine of chances. Phil. Trans. R. Soc. 53 (1763), 370–418.
5. Berlin, K.S., Williams, N.A., Parra, G.R., An introduction to latent variable mixture modeling (Part 1): Overview and cross-sectional latent class and latent profile analyses. Journal of Pediatric Psychology, 2014, 10.1093/jpepsy/jst084.
6. Buckman, J., Naismith, I., Saunders, R., Morrison, T., Linke, S., Leibowitz, J., et al. The impact of alcohol use on drop-out and psychological treatment outcomes in improving access to psychological therapies services: An audit. Behavioural and Cognitive Psychotherapy 46:5 (2018), 513–527, 10.1017/S1352465817000819.
7. Buckman, J., Saunders, R., Fearon, P., Leibowitz, J., Pilling, S., Attentional control as a predictor of response to psychological treatment for depression and relapse up to 1 year after treatment: A pilot cohort study. Behavioural and Cognitive Psychotherapy, 1–14, 2018, 10.1017/S1352465818000590.
8. Buckman, J., Underwood, A., Clarke, K., Saunders, R., Hollon, S.D., Fearon, P., et al. Risk factors for relapse and recurrence of depression in adults and how they operate: A four-phase systematic review and meta-synthesis. Clinical Psychology Review, 2018, 10.1016/j.cpr.2018.07.005.
9. Button, K.S., Kounali, D., Thomas, L., et al. Minimal clinically important difference on the Beck Depression Inventory-II according to the patient's perspective. Psychological Medicine 45:15 (2015), 3269–3279, 10.1017/S0033291715001270.
10. Calfee, C.S., Delucchi, K., Parsons, P.E., Thompson, B.T., Ware, L.B., Matthay, M.A., Subphenotypes in acute respiratory distress syndrome: Latent class analysis of data from two randomised controlled trials. The Lancet. Respiratory Medicine 2:8 (2014), 611–620, 10.1016/S2213-2600(14)70097-9.
11. Chekroud, A.M., Koutsouleris, N., The perilous path from publication to practice. Molecular Psychiatry, 2018, 10.1038/mp.2017.227.
12. Chekroud, A.M., Zotti, R.J., Shehzad, Z., Gueorguieva, R., Johnson, M.K., Trivedi, M.H., et al. Cross-trial prediction of treatment outcome in depression: A machine learning approach. Lancet Psychiatr. 3:3 (2016), 243–250, 10.1016/S2215-0366(15)00471-X.
13. Clark, D.M., Realizing the mass public benefit of evidence-based psychological therapies: The iapt program. Annual Review of Clinical Psychology 7 (2018), 159–183, 10.1146/annurev-clinpsy-050817-084833.
14. Clark, D.M., Canvin, L., Green, J., et al. Transparency about the outcomes of mental health services (IAPT approach): an analysis of public data. The Lancet 391 (2018), 679–686, 10.1016/S0140-6736(17)32133-5.
15. Cleynen, I., John, J.M.M., Henckaerts, L., van Moerkercke, W., Rutgeerts, P., van Steen, K., et al. Molecular reclassification of Crohn's disease by cluster analysis of genetic variants. PLoS One, 2010, 10.1371/journal.pone.0012952.
16. Cohen, Z.D., DeRubeis, R.J., Treatment selection in depression. Annual Review of Clinical Psychology, 14, 2018, 10.1146/annurev-clinpsy-050817 15.1-15.28.
17. Cohen, Z.D., Kim, T.T., Van, H.L., Dekker, J.J.M., Driessen, E., A demonstration of a multi-method variable selection approach for treatment selection: Recommending cognitive–behavioral versus psychodynamic therapy for mild to moderate adult depression. Psychotherapy Research, 2019, 10.1080/10503307.2018.1563312.
18. Cohen, Z.D., Wiley, J.F., Lutz, W., Fisher, A.J., Kim, T., Saunders, R., et al. SMART mental health prediction tournament. Retrieved from https://osf.io/wxgzu/, 2018.
19. Conley, S., Symptom cluster research with biomarkers and genetics using latent class analysis. Western Journal of Nursing Research 39:12 (2017), 1639–1653, 10.1177/0193945916679812.
20. Connor, K.M., Davidson, J.R.T., Churchill, L.E., Sherwood, A., Weisler, R.H., Foa, E., Psychometric properties of the social phobia inventory (SPIN). The British Journal of Psychiatry 176:4 (2000), 379–386, 10.1192/bjp.176.4.379.
21. Cox, D.R., Note on grouping. Journal of the American Statistical Association 52:280 (1957), 543–547, 10.1080/01621459.1957.10501411.
22. Delgadillo, J., de Jong, K., Lucock, M., Lutz, W., Rubel, J., Gilbody, S., et al. Feedback-informed treatment versus usual psychological treatment for depression and anxiety: A multisite, open-label, cluster randomised controlled trial. Lancet Psychiatry, 2018, 10.1016/S2215-0366(18)30162-7.
23. Delgadillo, J., Moreea, O., Lutz, W., Different people respond differently to therapy: A demonstration using patient profiling and risk stratification. Behaviour Research and Therapy 79 (2016), 15–22, 10.1016/j.brat.2016.02.003.
24. DeRubeis, R.J., Cohen, Z.D., Forand, N.R., Fournier, J.C., Gelfand, L.A., Lorenzo-Luaces, L., The personalized advantage index: Translating research on prediction into individualized treatment recommendations. A demonstration. PLoS ONE, 9(1), 2014, e83875, 10.1371/journal.pone.0083875.
25. Dwyer, D.B., Falkai, P., Koutsouleris, N., Machine learning approaches for clinical Psychology and psychiatry. 2018, SSRN.
26. Falkenström, F., Ekeblad, A., Holmqvist, R., Improvement of the working alliance in one treatment session predicts improvement of depressive symptoms by the next session. Journal of Consulting and Clinical Psychology, 2016, 10.1037/ccp0000119.
27. Finch, W.H., Bronk, K.C., Conducting confirmatory latent class analysis using Mplus. Structural Equation Modeling, 2011, 10.1080/10705511.2011.532732.
28. Fossati, A., Maffei, C., Battaglia, M., Bagnato, M., Donati, D., Donini, M., et al. Latent class analysis of DSM-IV schizotypal personality disorder criteria in psychiatric patients. Schizophrenia Bulletin, 2001, 10.1093/oxfordjournals.schbul.a006860.
29. Garb, H.N., Clinical judgment and decision making. Annual Review of Clinical Psychology 1:1 (2005), 67–89, 10.1146/annurev.clinpsy.1.102803.143810.
30. Goodman, L., Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika 61:2 (1974), 215–231, 10.2307/2334349.
31. Hagenaars, J., McCutcheon, A., Applied latent class analysis. 2002, Cambridge University Press, Cambridge.
32. Hart, A., Wyatt, J., Evaluating black-boxes as medical decision aids: Issues arising from a study of neural networks. Informatics for Health and Social Care 15:3 (1990), 229–236, 10.3109/14639239009025270.
33. Herzberg, P.Y., Roth, M., Beyond resilients, undercontrollers, and overcontrollers? An extension of personality prototype research. European Journal of Personality 20:1 (2006), 5–28, 10.1002/per.557.
34. Howard, M.C., Hoffman, M.E., Variable-centered, person-centered, and person-specific approaches: Where theory meets the method. Organizational Research Methods 21:4 (2018), 846–876, 10.1177/1094428117744021.
35. Huibers, M.J.H., Cohen, Z.D., Lemmens, L.H.J.M., Arntz, A., Peeters, F.P.M.L., Cuijpers, P., et al. Predicting optimal outcomes in cognitive therapy or interpersonal psychotherapy for depressed individuals using the personalized advantage index approach. PLoS One, 10(11), 2015, e0140771, 10.1371/journal.pone.0140771.
36. IAPT, The IAPT data handbook. Retrieved from http://www.iapt.nhs.uk/silo/files/iapt-data-handbook-appendicies-v2.pdf, 2011.
37. Kennedy, R.D., Quinn, J.E., Mullan, P.B., Johnston, P.G., Harkin, D.P., The role of BRCA1 in the cellular response to chemotherapy. JNCI Journal of the National Cancer Institute 96:22 (2004), 1659–1668, 10.1093/jnci/djh312.
38. Kent, P., Stochkendahl, M.J., Christensen, H.W., Kongsted, A., Could the clinical interpretability of subgroups detected using clustering methods be improved by using a novel two-stage approach?. Chiropractic & Manual Therapies, 2015, 10.1186/s12998-015-0064-9.
39. Khoury, M.J., Galea, S., Will precision medicine improve population health?. Journal of the American Medical Association 316:13 (2016), 1357–1358, 10.1001/jama.2016.12260.
40. Kroenke, K., Spitzer, R.L., Williams, J.B., The PHQ-9: Validity of a brief depression severity measure. Journal of General Internal Medicine 16:9 (2001), 606–613 Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/11556941.
41. Lazarsfield, P.F., Henry, N.W., Latent structure analysis. 1968, Houghton Mifflin, Boston.
42. Leuven, E., Sianesi, B., PSMATCH2: Stata module to perform full mahalanobis and propensity score matching, version 1.2. 1. 2003, Statistical Software Components.
43. Libon, D.J., Drabick, D.A.G., Giovannetti, T., Price, C.C., Bondi, M.W., Eppig, J., et al. Neuropsychological syndromes associated with Alzheimer's/vascular dementia: A latent class analysis. Journal of Alzheimer's Disease, 2014, 10.3233/JAD-132147.
44. Magidson, J., Vermunt, J.K., Latent class models for clustering: A comparison with K-means. Canadian Journal of Marketing Research 20:1 (2002), 37–44 1614-1881.
45. Merz, E.L., Roesch, S.C., A latent profile analysis of the Five Factor Model of personality: Modeling trait interactions. Personality and Individual Differences 51:8 (2011), 915–919, 10.1016/j.paid.2011.07.022.
46. Mundt, J.C., Marks, I.M., Shear, M.K., Greist, J.H., The work and social adjustment scale: A simple measure of impairment in functioning. British Journal of Psychiatry 180 (2002), 461–464, 10.1192/bjp.180.5.461.
47. NHS Digital, Psychological therapies, annual report on the use of IAPT services: England 2015-16., 2016.
48. NHS Digital, Psychological therapies, annual report on the use of., 2017, IAPT services - England.
49. NHS Digital, Psychological Therapies, Annual report on the use of IAPT services 2018-19., 2019.
50. NICE, Depression in adults: Recognition and management. 2009, NICE Clinical Guideline Retrieved from guidance.nice.org.uk/cg90.
51. Ozomaro, U., Wahlestedt, C., Nemeroff, C.B., Pulford, J., Miles, W., Sheridan, J., et al. Personalized medicine in psychiatry: Problems and promises. BMC Medicine, 11(1), 2013, 132, 10.1186/1741-7015-11-132.
52. Pirmohamed, M., Personalized pharmacogenomics: Predicting efficacy and adverse drug reactions. Annual Review of Genomics and Human Genetics 15:1 (2014), 349–370, 10.1146/annurev-genom-090413-025419.
53. Rosellini, A.J., Brown, T.A., Initial interpretation and evaluation of a profile-based classification system for the anxiety and mood disorders: Incremental validity compared to DSM-IV categories. Psychological Assessment 26:4 (2014), 1212–1224, 10.1037/pas0000023.
54. Saunders, R., Buckman, J.E.J., Cape, J., Fearon, P., Leibowitz, J., Pilling, S., Trajectories of depression and anxiety symptom change during psychological therapy. Journal of Affective Disorders 249 (2019), 327–335, 10.1016/J.JAD.2019.02.043.
55. Saunders, R., Cape, J., Fearon, P., Pilling, S., Predicting treatment outcome in psychological treatment services by identifying latent profiles of patients. Journal of Affective Disorders 197 (2016), 107–115, 10.1016/j.jad.2016.03.011.
56. Scheltens, N.M.E., Galindo-Garre, F., Pijnenburg, Y.A.L., Van Der Vlies, A.E., Smits, L.L., Koene, T., et al. The identification of cognitive subtypes in Alzheimer's disease dementia using latent class analysis. Journal of Neurology, Neurosurgery & Psychiatry 87 (2016), 235–243, 10.1136/jnnp-2014-309582.
57. Schreiber, J.B., Pekarik, A.J., Technical note: Using latent class Analysis versus K-means or hierarchical clustering to understand museum visitors. Curator: The Museum Journal 57:1 (2014), 45–59, 10.1111/cura.12050.
58. Schwaederle, M., Zhao, M., Lee, J.J., Lazar, V., Leyland-Jones, B., Schilsky, R.L., et al. Association of biomarker-based treatment strategies with response rates and progression-free survival in refractory malignant neoplasms. JAMA Oncology, 2(11), 2016, 1452, 10.1001/jamaoncol.2016.2129.
59. Spitzer, R.L., Kroenke, K., Williams, J.B.W., Löwe, B., A brief measure for assessing generalized anxiety disorder. Archives of Internal Medicine, 166(10), 2006, 1092, 10.1001/archinte.166.10.1092.
60. StataCorp, Stata statistical software: Release 15. 2017., 2017.
61. Unick, G.J., Snowden, L., Hastings, J., Heterogeneity in comorbidity between major depressive disorder and generalized anxiety disorder and its clinical consequences. The Journal of Nervous and Mental Disease 197:4 (2009), 215–224, 10.1097/NMD.0b013e31819d954f.
62. Vittengl, J.R., Anna Clark, L., Thase, M.E., Jarrett, R.B., Initial Steps to inform selection of continuation cognitive therapy or fluoxetine for higher risk responders to cognitive therapy for recurrent major depressive disorder. Psychiatry Research 253 (2017), 174–181, 10.1016/j.psychres.2017.03.032.
63. Wade, T.D., Crosby, R.D., Martin, N.G., Use of latent profile analysis to identify eating disorder phenotypes in an adult Australian twin cohort. Archives of General Psychiatry 63:12 (2006), 1377–1384, 10.1001/archpsyc.63.12.1377.
64. Webb, C.A., Trivedi, M.H., Cohen, Z.D., Dillon, D.G., Fournier, J.C., Goer, F., et al. Personalized prediction of antidepressant v. placebo response: Evidence from the EMBARC study. Psychological Medicine, 2018, 10.1017/S0033291718001708.