Advancing statistical analysis of ambulatory assessment data in the study of addictive behavior: A primer on three person-oriented techniques

Addictive Behaviors

Volumn 83, Issue , 2018, Pages 25-34

  Foster, Katherine T ^a   Beltz, Adriene M ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

Author keywords

Daily diary; Group iterative multiple model estimation; Growth curve; Multilevel modeling; P technique; Time series

Indexed keywords

ACTIMETRY; ADDICTION; ANALYTIC METHOD; ARTICLE; BEHAVIORAL OBSERVATION; CONTROLLED STUDY; FACTOR ANALYSIS; FEMALE; HUMAN; MALE; MULTILEVEL ANALYSIS; PSYCHOLOGICAL ASPECT; SELF REPORT; STATISTICAL ANALYSIS; STRUCTURAL EQUATION MODELING; TREATMENT OUTCOME; AMBULATORY MONITORING; DEVICES; DRUG DEPENDENCE; PROCEDURES; STATISTICAL MODEL;

BEHAVIOR, ADDICTIVE; FACTOR ANALYSIS, STATISTICAL; HUMANS; MODELS, STATISTICAL; MONITORING, AMBULATORY; SUBSTANCE-RELATED DISORDERS;

EID: 85043470127     PISSN: 03064603     EISSN: 18736327     Source Type: Journal    
DOI: 10.1016/j.addbeh.2017.12.018     Document Type: Article

References (58)

1. Beckham, J., Wiley, M., Miller, S., Dennis, M., Wilson, S., McClernon, F.J., Calhoun, P., Ad lib smoking in post-traumatic stress disorder: An electronic diary study. Nicotine & Tobacco Research 10:7 (2008), 1149–1157, 10.1080/14622200802123302.
2. Beltz, A. M., & Gates, K. M. Network mapping with GIMME. Multivariate Behavioral Research. (in press).
3. Beltz, A.M., Gates, K.M., Engels, A.S., Molenaar, P.C.M., Pulido, C., Turrisi, R., Wilson, S.J., Changes in alcohol-related brain networks across the first year of college: A prospective pilot study using fMRI effective connectivity mapping. Addictive Behaviors 38:4 (2013), 2052–2059, 10.1016/j.addbeh.2012.12.023.
4. Beltz, A.M., Wright, A.G.C., Sprague, B.N., Molenaar, P.C.M., Bridging nomothetic and idiographic approaches to the analysis of clinical data. Assessment 23:4 (2016), 447–458.
5. Beltz, A.M., Molenaar, P.C.M., A posteriori model validation for the temporal order of directed functional connectivity maps. Frontiers in neuroscience, 9, 2015, 304.
6. Birkhoff, G.D., Proof of the ergodic theorem. Proceedings of the National Academy of Sciences 17:12 (1931), 656–660, 10.1073/PNAS.17.2.656.
7. Bolger, N., Laurenceau, J.-P., Intensive longitudinal methods. 2013, Vasa Retrieved from http://medcontent.metapress.com/index/A65RM03P4874243N.pdf.
8. Borkenau, P., Ostendorf, F., The big five as states: How useful is the five-factor model to describe intraindividual variations over time?. Journal of Research in Personality 32 (1998), 202–221.
9. Brown, T.A., Confirmatory factor analysis for applied research. 2014, Guilford Publications, Methodology in the Social Sciences.
10. Brown, T.A., Confirmatory factor analysis for applied research. Second Edition, 2015, Guilford Publications.
11. Browne, M., Zhang, G., Developments in the factor analysis of individual time series. Cudeck, R., MacCallum, R., (eds.) Factor analysis at 100: Historical developments and future directions, 2007, Erlbaum, Mahwah, NJ.
12. Browne, M., Zhang, G., User's guide to DyFA: Dynamic factor analysis of lagged correlation matrices, version 3.00 [Computer program and manual]. 2010.
13. Cattell, R.B., The description of personality: Basic traits resolved into clusters. The Journal of Abnormal and Social Psychology 38:4 (1943), 476–506, 10.1037/h0054116.
14. Chow, S.-M., Hamaker, E.L., Fujita, F., Boker, S.M., Representing time-varying cyclic dynamics using multiple-subject state-space models. British Journal of Mathematical and Statistical Psychology 62:3 (2009), 683–716, 10.1348/000711008X384080.
15. Chow, S.-M., Witkiewitz, K., Grasman, R., Hutton, R.S., Maisto, S.A., A regime-switching longitudinal model of alcohol lapse-relapse. Handbook of Developmental Systems Theory and Methodology, 2014, 397–422.
16. Collins, L.M., Lanza, S.T., Latent class and latent transition analysis with applications in the social, behavioral, and health sciences. 2010, John Wiley & Sons, 10.1002/9780470567333.
17. Cutler, R.B., Fishbain, D.A., Are alcoholism treatments effective? The project MATCH data. BMC Public Health, 5(1), 2005, 75, 10.1186/1471-2458-5-75.
18. Delfino, R.J., Jamner, L.D., Whalen, C.K., Temporal analysis of the relationship of smoking behavior and urges to mood states in men versus women. Nicotine & Tobacco Research 3 (2001), 235–248, 10.1080/14622200.
19. Duckworth, A.L., Tsukayama, E., May, H., Establishing causality using longitudinal hierarchical linear modeling: An illustration predicting achievement from self-control. Social Psychological and Personality Science 1:4 (2010), 311–317 http://doi.org/doi https://doi.org/10.1177/1948550609359707.
20. Epstein, D.H., Willner-Reid, J., Vahabzadeh, M., Mezghanni, M., Lin, J.-L., Preston, K.L., Real-time electronic diary reports of cue exposure and mood in the hours before cocaine and heroin craving and use. Archives of General Psychiatry, 66(1), 2009, 88, 10.1001/archgenpsychiatry.2008.509.
21. Foster, K.T., Hicks, B., Iacono, W., McGue, M., Gender differences in the structure of risk for alcohol use disorder in adolescence and young adulthood. Psychological Medicine, 2015, 1–12, 10.1017/S0033291715001014.
22. Gates, K.M., Molenaar, P.C.M., Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples. NeuroImage 63:1 (2012), 310–319, 10.1016/j.neuroimage.2012.06.026.
23. Gates, K.M., Molenaar, P.C.M., Hillary, F.G., Ram, N., Rovine, M.J., Automatic search for fMRI connectivity mapping: An alternative to Granger causality testing using formal equivalences among SEM path modeling, VAR, and unified SEM. NeuroImage 50:3 (2010), 1118–1125, 10.1016/j.neuroimage.2009.12.117.
24. Granger, C.W.J., Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 1969, 424, 10.2307/1912791.
25. Hamdi, N.R., Iacono, W.G., Lifetime prevalence and co-morbidity of externalizing disorders and depression in prospective assessment. Psychological Medicine 44:2 (2014), 315–324, 10.1017/S0033291713000627.
26. Hussong, A.M., Hicks, R.E., Levy, S.A., Curran, P.J., Specifying the relations between affect and heavy alcohol use among young adults. Journal of Abnormal Psychology 110:3 (2001), 449–461, 10.1037/0021-843X.110.3.449.
27. Jones, C.J., Nesselroade, J.R., Multivariate, replicated, single-subject, repeated measures designs and p-technique factor analysis: A review of intraindividual change studies. Experimental Aging Research 16:4 (1990), 171–183, 10.1080/03610739008253874.
28. Kim, J., Zhu, W., Chang, L., Bentler, P.M., Ernst, T., Unified structural equation modeling approach for the analysis of multisubject, multivariate functional MRI data. Human Brain Mapping 28:2 (2007), 85–93, 10.1002/hbm.20259.
29. Lane, S., Gates, K.M., Automated selection of robust individual-level structural equation models for time series data. Structural Equation Modeling: A Multidisciplinary Journal 24 (2017), 768–782.
30. Lane, S., Gates, K.M., Molenaar, P.C.M., Hallquist, M., Pike, H., Gimme: Group Iterative Multiple Model Estimation. Retrieved from https://cran.r-project.org/package=gimme, 2017.
31. Lee, I.A., Little, T.D., P-technique factor analysis. Handbook of Developmental Research Methods, 2012, 350–363.
32. Lütkepohl, H., New introduction to multiple time series analysis. Book, 2005, 10.1007/978-3-540-27752-1.
33. Molenaar, P.C.M., A dynamic factor model for the analysis of multivariate time series. Psychometrika 50:2 (1985), 181–202, 10.1007/BF02294246.
34. Molenaar, P.C.M., A manifesto on psychology as idiographic science: Bringing the person back into scientific psychology, this time forever. Measurement: Interdisciplinary Research & Perspective 2:4 (2004), 201–218, 10.1207/s15366359mea0204_1.
35. Molenaar, P.C.M., Nesselroade, J.R., The recoverability of p-technique factor analysis. Multivariate Behavioral Research 44:1 (2009), 130–141, 10.1080/00273170802620204.
36. Molenaar, P.C.M., Campbell, C.G., The new person-specific paradigm in psychology. Current Directions in Psychological Science 18 (2009), 112–117.
37. Moos, R.H., Moos, B.S., Rates and predictors of relapse after natural and treated remission from alcohol use disorders. Addiction 101:2 (2006), 212–222, 10.1111/j.1360-0443.2006.01310.x.
38. Muthen, B., Muthen, L.K., Integrating person-centered and variable-centered analyses: Growth mixture modeling with latent trajectory classes. Alcoholism: Clinical and Experimental Research 24:6 (2000), 882–891, 10.1111/j.1530-0277.2000.tb02070.x.
39. Piasecki, T.M., Relapse to smoking. Clinical Psychology Review, 2006, 10.1016/j.cpr.2005.11.007.
40. Preacher, K.J., Curran, P.J., Bauer, D.J., Computational tools for probing interactions in multiple linear regression, multilevel modeling, and latent curve analysis. Journal of Educational and Behavioral Statistics 31:4 (2006), 437–448, 10.3102/10769986031004437.
41. Preacher, K.J., Zyphur, M., Zhang, Z., A general multilevel SEM framework for assessing multilevel mediation. Psychological Methods 15:3 (2010), 209–233, 10.1037/a0020141.
42. Ram, N., Grimm, K.J., Growth mixture modeling: A method for identifying differences in longitudinal change among unobserved groups. International Journal of Behavioural Development 33:6 (2009), 565–576, 10.1177/0165025409343765.Growth.
43. Raudenbush, S.W., Bryk, A.S., Hierarchical linear models: Applications and data analysis methods. 2002, SAGE Publications, Los Angeles, CA.
44. Russell, R.L., Jones, M.E., Miller, S.A., Core process components in psychotherapy: A synthetic review of p-technique studies. Psychotherapy Research 17:3 (2007), 271–288, 10.1080/10503300500529388.
45. Schneider, S., Stone, A.A., Ambulatory and diary methods can facilitate the measurement of patient-reported outcomes. Quality of Life Research 25:3 (2016), 497–506, 10.1007/s11136-015-1054-z.
46. Shapiro, D., Jamner, L.D., Davydov, D.M., James, P., Situations and moods associated with smoking in everyday life. Psychology of Addictive Behaviors 16:4 (2002), 342–345, 10.1037//0893-164X.16.4.342.
47. Shiffman, S., How many cigarettes did you smoke? Assessing cigarette consumption by global report, time-line follow-back, and ecological momentary assessment. Health Psychology 28:5 (2009), 519–526, 10.1037/a0015197.
48. Shiffman, S., Gwaltney, C.J., Balabanis, M.H., Liu, K.S., Paty, J.A., Kassel, J.D., Gnys, M., Immediate antecedents of cigarette smoking: An analysis from ecological momentary assessment. Journal of Abnormal Psychology 111:4 (2002), 531–545, 10.1037//0021-843X.111.4.531.
49. Shiffman, S., Waters, A.J., Negative affect and smoking lapses: A prospective analysis. Journal of Consulting and Clinical Psychology 72:2 (2004), 192–201, 10.1037/0022-006X.72.2.192.
50. Singer, J.D., Willett, J.B., Applied longitudinal data analysis: modeling change and event occurrence. Applied longitudinal data analysis: Modeling change and event occurrence, 2003, Oxford University Press, 10.1093/acprof:oso/9780195152968.001.0001.
51. Solhan, M.B., Trull, T.J., Jahng, S., Wood, P.K., Clinical assessment of affective instability: Comparing EMA indices, questionnaire reports, and retrospective recall. Psychological Assessment 21:3 (2009), 425–436, 10.1037/a0016869.
52. Stephenson, W., The foundations of psychometry: Four factor systems. Psychometrika 1:3 (1936), 195–209, 10.1007/BF02288366.
53. Treloar, H., Piasecki, T.M., McCarthy, D.M., Sher, K.J., Heath, A.C., Ecological evidence that affect and perceptions of drink effects depend on alcohol expectancies. Addiction (Abingdon, England) 110:9 (2015), 1432–1442, 10.1111/add.12982.
54. Vrieze, S.I., Iacono, W.G., McGue, M., Confluence of genes, environment, development, and behavior in a post Genome-Wide Association Study world. Development and Psychopathology 24:4 (2012), 1195–1214, 10.1017/S0954579412000648.
55. Weinstock, J., Farney, M.R., Elrod, N.M., Henderson, C.E., Weiss, E.P., Exercise as an adjunctive treatment for substance use disorders: Rationale and intervention description. Journal of Substance Abuse Treatment 72 (2017), 40–47, 10.1016/j.jsat.2016.09.002.
56. West, B.T., Welch, K.B., Galecki, A.T., Linear mixed models: A practical guide using statistical software. Linear Mixed Models, Second Edition, Vol. 30, 2014, 10.1201/b17198-2.
57. Wong, C.C.Y., Schumann, G., Review. Genetics of addictions: Strategies for addressing heterogeneity and polygenicity of substance use disorders. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 363:1507 (2008), 3213–3222, 10.1098/rstb.2008.0104.
58. Wood, P., Brown, D., The study of intraindividual differences by means of dynamic factor models: Rationale, implementation, and interpretation. Psychological Bulletin 116:1 (1994), 166–186, 10.1037/0033-2909.116.1.166.