Decomposing the heterogeneity of depression at the person-, symptom-, and time-level: latent variable models versus multimode principal component analysis

BMC Medical Research Methodology

Peter de Jonge¹  Ernst C. Wit²  Elisabeth H. Bos¹  Klaas J. Wardenaar¹  Stijn de Vos¹ 

[1] University of Groningen, University Medical Center Groningen, Interdisciplinary Center Psychopathology and Emotion regulation (ICPE), (internal mail CC-72), Groningen, 9700 RB, The Netherlands;University of Groningen, Johann Bernoulli Institute of Mathematics and Computer Science, Groningen, The Netherlands

关键词: Depression;    Multimodal data;    Heterogeneity;    MPCA;    3PCA;    Latent variable models;    Data cube;   

Others  :  1230341
DOI  :  10.1186/s12874-015-0080-4

 received in 2014-10-27, accepted in 2015-10-05,  发布年份 2015

【 摘 要 】

Background

Heterogeneity of psychopathological concepts such as depression hampers progress in research and clinical practice. Latent Variable Models (LVMs) have been widely used to reduce this problem by identification of more homogeneous factors or subgroups. However, heterogeneity exists at multiple levels (persons, symptoms, time) and LVMs cannot capture all these levels and their interactions simultaneously, which leads to incomplete models. Our objective is to briefly review the most widely used LVMs in depression research, illustrating their use and incompatibility in real data, and to consider an alternative, statistical approach, namely multimode principal component analysis (MPCA).

Methods

We applied LVMs to data from 147 patients, who filled out the Quick Inventory of Depressive Symptomatology (QIDS) at 9 time points. Compatibility of the results and suitability of the LVMs to capture the heterogeneity of the data were evaluated. Alternatively, MPCA was used to simultaneously decompose depression on the person-, symptom- and time-level and to investigate the interactions between these levels.

Results

QIDS-data could be decomposed on the person-level (2 classes), symptom-level (2 factors) and time-level (2 trajectory-classes). However, these results could not be integrated into a single model. Instead, MPCA allowed for decomposition of the data at the person- (3 components), symptom- (2 components) and time-level (2 components) and for the investigation of these components’ interactions.

Conclusions

Traditional LVMs have limited use when trying to define an integrated model of depression heterogeneity at the person, symptom and time level. More integrative statistical techniques such as MPCA can be used to address these relatively complex data patterns and could be used in future attempts to identify empirically-based subtypes/phenotypes of depression.

【 授权许可】

   
2015 de Vos et al.

【 图 表 】

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【 参考文献 】

• [1]Mathers CD, Loncar D. Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med. 2006; 3: Article ID e442
• [2]Shorter E, Tyrer P. Separation of anxiety and depressive disorders: blind alley in psychopharmacology and classification of disease. Br Med J. 2003; 327:158-60.
• [3]Kendell R. Clinical validity. Psychol Med. 1989; 19:45-55.
• [4]Widiger TA, Clark LA. Toward DSM—V and the classification of psychopathology. Psychol Bull. 2000; 126:946.
• [5]Kendell R, Jablensky A. Distinguishing between the validity and utility of psychiatric diagnoses. Am J Psychiatry. 2003; 160:4-12.
• [6]Widiger TA, Samuel DB. Diagnostic categories or dimensions? a question for the diagnostic and statistical manual of mental disorders—fifth edition. J Abnorm Psychol. 2005; 114:494-504.
• [7]Kupfer DJ, Frank E, Phillips ML. Major depressive disorder: new clinical, neurobiological, and treatment perspectives. The Lancet. 2012; 379:1045-55.
• [8]Heim C, Newport DJ, Mletzko T, Miller AH, Nemeroff CB. The link between childhood trauma and depression: insights from HPA axis studies in humans. Psychoneuroendocrinology. 2008; 33:693-710.
• [9]Heim C, Nemeroff CB. The role of childhood trauma in the neurobiology of mood and anxiety disorders: preclinical and clinical studies. Biol Psychiatry. 2001; 49:1023-39.
• [10]Chen L, Eaton WW, Gallo JJ, Nestadt G. Understanding the heterogeneity of depression through the triad of symptoms, course and risk factors: a longitudinal, population-based study. J Affect Disord. 2000; 59:1-11.
• [11]Cattell RB. The data box: its ordering of total resources in terms of possible relational systems. In: Handbook of multivariate experimental psychology. Cattell RB, editor. Rand McNally, Chicago; 1966: p.67-128.
• [12]Wardenaar KJ, de Jonge P. Diagnostic heterogeneity in psychiatry: towards an empirical solution. BMC Med. 2013; 11:201. BioMed Central Full Text
• [13]Skrondal A, Rabe‐Hesketh S. Latent variable modelling: a survey. Scand J Stat. 2007; 34:712-45.
• [14]Tibshirani R, Walther G, Hastie T. Estimating the number of clusters in a data set via the gap statistic. J R Stat Soc Series B Stat Methodol. 2001; 63:411-23.
• [15]Kendler KS, Eaves LJ, Walters EE, Neale MC, Heath AC, Kessler RC. The identification and validation of distinct depressive syndromes in a population-based sample of female twins. Arch Gen Psychiatry. 1996; 53:391-9.
• [16]Sullivan PF, Neale MC, Kendler KS. Genetic epidemiology of major depression: review and meta-analysis. Am J Psychiatry. 2000; 157:1552-62.
• [17]Sullivan PF, Prescott CA, Kendler KS. The subtypes of major depression in a twin registry. J Affect Disord. 2002; 68:273-84.
• [18]Lamers F, de Jonge P, Nolen WA, Smit JH, Zitman FG, Beekman AT et al.. Identifying depressive subtypes in a large cohort study: results from the Netherlands Study of Depression and Anxiety (NESDA). J Clin Psychiatry. 2010; 71:1582-9.
• [19]Lamers F, Vogelzangs N, Merikangas KR, de Jonge P, Beekman AT, Penninx BW. Evidence for a differential role of HPA-axis function, inflammation and metabolic syndrome in melancholic versus atypical depression. Mol Psychiatry. 2013; 18:692-9.
• [20]van Loo HM, de Jonge P, Romeijn JW, Kessler RC, Schoevers RA. Data-driven subtypes of major depressive disorder: a systematic review. BMC Med. 2012; 10:156. BioMed Central Full Text
• [21]Mora PA, Beamon T, Preuitt L, DiBonaventura M, Leventhal EA, Leventhal H. Heterogeneity in depression symptoms and health status among older adults. J Aging Health. 2012; 24:879-96.
• [22]Shafer AB. Meta‐analysis of the factor structures of four depression questionnaires. Beck, CES‐D, Hamilton, and Zung. J Clin Psychol. 2006; 62:123-46.
• [23]Fabrigar LR, Wegener DT, MacCallum RC, Strahan EJ. Evaluating the use of exploratory factor analysis in psychological research. Psychol Methods. 1999; 4:272.
• [24]Brown TA. Confirmatory factor analysis for applied research. Guilford Press, New York; 2012.
• [25]Wardenaar KJ, van Veen T, Giltay EJ, den Hollander-Gijsman M, Penninx BW, Zitman FG. The structure and dimensionality of the Inventory of Depressive Symptomatology Self Report (IDS-SR) in patients with depressive disorders and healthy controls. J Affect Disord. 2010; 125:146-54.
• [26]Wardenaar KJ, van Veen T, Giltay EJ, Zitman FG, Penninx BW. The use of symptom dimensions to investigate the longitudinal effects of life events on depressive and anxiety symptomatology. J Affect Disord. 2014; 156:126-33.
• [27]Stoolmiller M, Kim HK, Capaldi DM. The course of depressive symptoms in men from early adolescence to young adulthood: identifying latent trajectories and early predictors. J Abnorm Psychol. 2005; 114:331.
• [28]Mora PA, DiBonaventura MD, Idler E, Leventhal EA, Leventhal H. Psychological factors influencing self-assessments of health: toward an understanding of the mechanisms underlying how people rate their own health. Ann Behav Med. 2008; 36:292-303.
• [29]Murphy BM, Elliott PC, Higgins RO, Le Grande MR, Worcester MU, Goble AJ et al.. Anxiety and depression after coronary artery bypass graft surgery: most get better, some get worse. Eur J Cardiovasc Prev Rehabil. 2008; 15:434-40.
• [30]Colman I, Ploubidis GB, Wadsworth ME, Jones PB, Croudace TJ. A longitudinal typology of symptoms of depression and anxiety over the life course. Biol Psychiatry. 2007; 62:1265-71.
• [31]Muthén B. Latent variable analysis. The Sage handbook of quantitative methodology for the social sciences. Sage Publications, Thousand Oaks; 2004.
• [32]Kroonenberg PM. Applied multiway data analysis. John Wiley & Sons, Hoboken NJ; 2008.
• [33]Rush AJ, Trivedi MH, Ibrahim HM, Carmody TJ, Arnow B, Klein DN et al.. The 16-Item Quick Inventory of Depressive Symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): a psychometric evaluation in patients with chronic major depression. Biol Psychiatry. 2003; 54:573-83.
• [34]Muthén LK, Muthén BO. Mplus user’s guide. 6th ed. Muthén & Muthén, Los Angeles CA; 2011.
• [35]Giordani P, Kiers H, Del Ferraro M. Three-way component analysis using the R package ThreeWay. 2012. J Stat Softw. 2014; 57:7.
• [36]Monden, R, Wardenaar, KJ, Stegeman, A, Conradi, HJ, de Jonge, P. Simultaneous Decomposition of Depression Heterogeneity on the Person-, Symptom-and Time-Level: The Use of Three-Mode Principal Component Analysis. PLoS One. 2015; DOI:. 10. 1371/journal.pone.0132765 webcite