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Contemporary Longitudinal Methods for the Study of Trauma and Posttraumatic Stress Disorder

Published online by Cambridge University Press:  07 November 2014

Abstract

Traditional methods for analyzing trends in longitudinal data have typically emphasized average group change over time. In this article, we propose multilevel, regression-based methods for examining inter-individual differences in intra-individual change and apply these methods to research in trauma and posttraumatic stress disorder (PTSD). The outcome or dependent variable of interest is reconceptualized as an index of dynamic change reflecting the trend or trajectory of an individual's PTSD symptom severity scores across time. A basic statistical model is presented, and analyses and findings are demonstrated with an existing database used in previously published studies. The methods offer promise for future study of the natural course of PTSD chronicity or recovery, risk and resilience factors that influence individual growth or decline, and critical timepoints for intervention.

Type
Review Article
Copyright
Copyright © Cambridge University Press 2003

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References

REFERENCES

1.Shalev, AY, Freedman, S, Peri, T, et al.Prospective study of posttraumatic stress disorder and depression following trauma. Am J Psychiatry. 1998;55:630637.CrossRefGoogle ScholarPubMed
2.Shalev, AY, Sahar, T, Freedman, S, et al.A prospective study of heart rate response following trauma and the subsequent development of posttraumatic stress disorder. Arch Gen Psychiatry. 1998;5:553559.CrossRefGoogle Scholar
3.Riggs, DS, Rothbaum, BO, Foa, EB. A prospective examination of symptoms of posttraumatic stress disorder in victims of nonsexual assault. J Interpers Violence. 1995;10:201213.CrossRefGoogle Scholar
4.Rothbaum, BO, Foa, EB, Riggs, DS, Murdock, T, Walsh, W. A prospective examination of post-traumatic stress disorder in rape victims. J Trauma Stress. 1992;5:455475.Google Scholar
5.Bryant, RA. Early interventions following psychological trauma. CNS Spectr. 2002;97:650654.CrossRefGoogle Scholar
6.King, DW, Vogt, DS, King, LA. Risk and resilience factors in the etiology of chronic PTSD. In: Litz, BT, ed. Early Interventions for Trauma and Traumatic Loss in Children and Adults: Evidence-based Directions. New York, NY: Guilford Press; 2003.Google Scholar
7.Lawrence, FR, Hancock, GR. Assessing change over time using latent growth modeling. Measurement and Evaluation in Counseling and Development. 1998;30:211224.CrossRefGoogle Scholar
8.McArdle, JJ, Epstein, D. Latent growth curves within developmental structural equation models. Child Dev. 1987;58:110133.CrossRefGoogle ScholarPubMed
9.McArdle, JJ, Hamagami, F. Latent difference score structural models for linear dynamic analyses with incomplete longitudinal data. In: Collins, L, ed. New Methods for the Analysis of Change: Decade of Behavior. Washington, DC: American Psychological Association; 2001:139175.CrossRefGoogle Scholar
10.McArdle, JJ, Nesselroade, JR. Structuring data to study development and change. In: Cohen, SH, Reese, HW, eds. Life-span Developmental Psychology: Methodological Innovations. Hillsdale, NJ: Erlbaum; 1994:223267.Google Scholar
11.Muthén, B. Latent variable modeling of longitudinal and multilevel data. In: Raftery, AE, ed. Sociological Methodology. Washington, DC: Blackwell; 1997:453480.Google Scholar
12.Ragosa, D. Myths and methods: “Myths about longitudinal research” plus supplemental questions. In: Gottman, JM, ed. The Analysis of Change. Mahwah, NJ: Erlbaum; 1995:366.Google Scholar
13.Raudenbush, SW, Bryk, AS. Hierarchical Linear Models: Application and Data Analysis Methods. Thousand Oaks, Calif: Sage Publications, Inc; 2002.Google Scholar
14.Willett, JB. Questions and answers in the measurement of change. In: Rothkopf, E, ed. Review of Research in Education 1988-89. Washington, DC: American Educational Research Association; 1988:345422.Google Scholar
15.Willett, JB, Singer, JD, Martin, NC. The design and analysis of longitudinal studies of development and psychopathology in context: statistical models and methodological recommendations. Dev Psychopathol. 1998;10:395426.CrossRefGoogle ScholarPubMed
16.Willett, JB, Sayer, AG. Using covariance structure analysis to detect correlates and predictors of individual change over time. Psychol Bull. 1994;116:363381.CrossRefGoogle Scholar
17.Brewin, CR, Andrews, B, Valentine, JD. Meta-analysis of risk factors for posttraumatic stress disorder in trauma-exposed adults. J Consult Clin Psychol. 2000;68:748766.CrossRefGoogle ScholarPubMed
18.Cudeck, R. Mixed-effects models in the study of individual differences with repeated measures data. Multivariate Behav Res. 1996;31:371403.CrossRefGoogle Scholar
19.McArdle, JJ, Paskus, TA, Boker, SM. A multilevel multivariate analysis of academic success in college based on NCAA student-athletes. Multivariate Behav Res. In press.Google Scholar
20.Smith, PL. Splines as a useful and convenient tool. Am Stat. 1979;32:5763.Google Scholar
21.Marsh, L, Cormier, DR. Spline Regression Models. Thousand Oaks, Calif: Sage; 2001.Google Scholar
22.Meredith, W, Tisak, J. Latent curve analysis. Psychometrika. 1990;55:107122.CrossRefGoogle Scholar
23.McArdle, JJ, Bell, RQ. Recent trends in modeling longitudinal data by latent growth curve methods. In: Little, TD, Schnabel, KU, Baumert, J, eds. Modeling Longitudinal and Multiple-group Data: Practical Issues, Applied Approaches, and Scientific Examples. Mahwah, NJ: Erlbaum; 1998.Google Scholar
24.Jöreskog, KG, Sörbom, D. LISREL 8 User's Reference Guide. Lincolnwood, IL: Scientific Software International; 2001.Google Scholar
25.Arbuckle, JL, Wothke, W. AMOS 4.0 User's Guide. Chicago, IL: SmallWaters Corporation; 1999.Google Scholar
26.Bender, PM. EQS: Structural Equations Program Manual. Encino, Calif: Multivariate Software, Inc; 1995.Google Scholar
27.Muthén, LK, Muthén, BO. Mplus User's Guide. Los Angeles, CA: Muthén & Muthén; 2001.Google Scholar
28.Neale, MC, Boker, SM, Xie, G, Maes, HH. Mx Statistical Modeling. 2nd ed. Unpublished program manual, Virginia Institute of Psychiatric and Behavioral Genetics, Medical College of Virginia, Virginia Commonwealth University, Richmond, VA; 1999.Google Scholar
29.Singer, JD. Using SAS PROC MIXED to fit multilevel models, hierarchical models, and individual growth models. J Edu Behav Stat. 1998;24:323355.CrossRefGoogle Scholar
30.McArdle, JJ, Ferrer-Caja, E, Hamagami, F, Woodcock, R. Comparative longitudinal structural analyses of the growth and decline of multiple intellectual abilities over the life span. Dev Psychol. 2002;38:115142.CrossRefGoogle ScholarPubMed
31.Littell, RC, Milliken, GA, Stroup, WW, Wolfinger, RD. SAS System for Mixed Models. Carey, NC: BBU; 1996.Google Scholar
32.SAS. Mixed Models Analyses Using the SAS System. Carey, NC: SAS Publishing; 2003.Google Scholar
33.Raudenbush, S, Bryk, A, Congdon, R. Hierarchical Linear and Nonlinear Modeling 5. Lincolnwood, IL: Scientific Software International. 2001.Google Scholar
34.Shalev, AY, Freedman, S, Peri, T, Brandes, D, Sahar, T. Predicting PTSD in trauma survivors: Prospective evaluation of self-report and clinician-administered instruments. Br J Psychiatry. 1997;170:558564.CrossRefGoogle ScholarPubMed
35.Shalev, AY, Peri, T, Canetti, L, Schreiber, S. Predictors of PTSD in injured trauma survivors: A prospective study. Am J Psychiatry. 1996;153:219225.Google ScholarPubMed
36.Diagnostic and Statistical Manual of Mental Disorders. 4th ed. Washington, DC: American Psychiatric Association; 1994.Google Scholar
37.Horowitz, MJ, Wilner, N, Alvarez, W. Impact of event scale: A measure of subjective stress. Psychosom Med. 1979;4:209218.CrossRefGoogle Scholar
38.Marmar, CR, Weiss, DS, Schlenger, WE, et al.Peritraumatic dissociation and posttraumatic stress in male Vietnam theater veterans. Am J Psychiatry. 1994;151:902907.Google ScholarPubMed
39.Collins, LM, Schafer, JL, Kam, C. A comparison of inclusive and restrictive strategies in modern missing data procedures. Psychol Methods. 2001;6:330351.CrossRefGoogle ScholarPubMed
40.Enders, CK. A primer on maximum likelihood algorithms available for use with missing data. Structural Equation Modeling. 2001;8:128141.CrossRefGoogle Scholar
41.Foy, DW, Carroll, EM, Donahoe, CP. Etiological factors in the development of PTSD in clinical samples of Vietnam combat veterans. J Clin Psychol. 1987;43:1727.3.0.CO;2-Q>CrossRefGoogle ScholarPubMed
42.Fairbank, JA, Keane, TM, Malloy, PF. Some preliminary data on the psychological characteristics of Vietnam veterans with posttraumatic stress disorder. J Consult Clin Psychol. 1983;51:912919.CrossRefGoogle Scholar
43.March, JS. What constitutes a stressor? The “Criterion A” issue. In: Davidson, JRT, Foa, EB, eds. Posttraumatic Stress Disorder: DSM-IV and Beyond. Washington, DC: American Psychiatric Press; 1993:3754.Google Scholar
44.Cohen, J, Cohen, P. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Hillsdale, NJ: Lawrence Erlbaum Associates; 1983.Google Scholar
45.Bremner, JD, Marmar, CR. Eds. Trauma, Memory, and Dissociation. Washington, DC: American Psychological Association; 1998.Google Scholar