A discrete-time multiple event process survival mixture (MEPSUM) model

Psychological Methods

Volumn 19, Issue 2, 2014, Pages 251-264

  Dean, Danielle O ^a   Bauer, Daniel J ^a   Shanahan, Michael J ^b  

^a University of North Carolina at Chapel Hill   (United States)

^b UNIVERSITY OF NORTH CAROLINA   (United States)

Author keywords

Event history; Latent class analysis; Mixture modeling; Multiple events; Survival analysis

Indexed keywords

ALGORITHM; HUMAN; LIFE EVENT; STATISTICAL MODEL; SURVIVAL;

ALGORITHMS; HUMANS; LIFE CHANGE EVENTS; MODELS, STATISTICAL; SURVIVAL ANALYSIS;

EID: 84902468820     PISSN: 1082989X     EISSN: None     Source Type: Journal    
DOI: 10.1037/a0034281     Document Type: Article

References (50)

1. Abar, B.,&Loken, E. (2012). Consequences of fitting nonidentified latent class models. Structural Equation Modeling, 19, 1-15. doi:10.1080/10705511.2012.634701
2. Akaike, H. (1974). A new look at statistical model identification. IEEE Transactions on automatic Control, AU-19, 719-722.
3. Allison, P. D. (1999). Logistic regression using the SAS system: Theory and application. Cary, NC: SAS Institute.
4. Bandeen-Roche, K., Miglioretti, D. L., Zeger, S. L.,&Rathouz, P. J. (1997). Latent variable regression for multiple discrete outcomes. Journal of the American Statistical Association, 92, 1375-1386. doi:10.1080/01621459.1997.10473658
5. Bauer, D. J.,&Curran, P. J. (2003). Distributional assumptions of growth mixture models: Implications for overextraction of latent trajectory classes. Psychological Methods, 8, 338-363. doi:10.1037/1082-989X.8.3.338
6. Bowers, A. J. (2010). Grades and graduation: A longitudinal risk perspective to identify student dropouts. The Journal of Educational Research, 103, 191-207. doi:10.1080/00220670903382970
7. Bozdogan, H. (1987). Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions. Psychometrika, 52, 345-370. doi:10.1007/BF02294361
8. Brame, R., Nagin, D. S.,&Wasserman, L. (2006). Exploring some analytical characteristics of finite mixture models. Journal of Quantitative Criminology, 22, 31-59. doi:10.1007/s10940-005-9001-8
9. Bray, B. C., Lanza, S. T.,&Collins, L. M. (2010). Modeling relations among discrete developmental processes: A general approach to associative latent transition analysis. Structural Equation Modeling, 17, 541-569. doi:10.1080/10705511.2010.510043
10. Collins, L. M.,&Lanza, S. T. (2010). Latent class and latent transition analysis. Hoboken, NJ: Wiley.
11. Corning, A. F.,&Malofeeva, E. V. (2004). The application of survival analysis to the study of psychotherapy termination. Journal of Counseling Psychology, 51, 354-367. doi:10.1037/0022-0167.51.3.354
12. Elder, G. H., Jr. (1985). Life course dynamics. Ithaca, NY: Cornell University Press.
13. Fraley, C.,&Raftery, A. E. (1998). How many clusters? Which clustering method? Answers via model-based cluster analysis (Technical report No. 329). Seattle, WA: Department of Statistics, University of Washington.
14. Gabadinho, A., Ritschard, G., Müller, N. S.,&Studer, M. (2011). Analyzing and visualizing state sequences in R with TraMineR. Journal of Statistical Software, 40, 1-37.
15. Ha, J. C., Kimpo, C. L.,&Sackett, G. P. (1997). Multiple-spell, discretetime survival analysis of developmental data: Object concept in pigtailed macaques. Developmental Psychology, 33, 1054-1059. doi:10.1037/0012-1649.33.6.1054
16. Harris, K. M., Halpern, C. T., Whitsel, E., Hussey, J., Tabor, J., Entzel, P., & Udry, J. R. (2009). The National Longitudinal Study of Adolescent Health: Research design. Retrieved from http://www.cpc.unc.edu/projects/addhealth/design
17. Heckman, J.,&Singer, B. (1984). A method for minimizing the impact of distributional assumptions in econometric models for duration data. Econometrica, 52, 271-320. doi:10.2307/1911491
18. Hipp, J. R.,&Bauer, D. J. (2006). Local solutions in the estimation of growth mixture models. Psychological Methods, 11, 36-53. doi: 10.1037/1082-989X.11.1.36
19. Hougaard, P. (2000). Analysis of multivariate survival data. New York, NY: Springer. doi:10.1007/978-1-4612-1304-8
20. Kish, L. (1965). Survey sampling. New York, NY: Wiley.
21. Lee, E. T.,&Wang, J. W. (2003). Statistical methods for survival data analysis. New York, NY: Wiley. doi:10.1002/0471458546
22. Lo, Y., Mendell, N. R.,&Rubin, D. B. (2001). Testing the number of components in a normal mixture. Biometrika, 88, 767-778. doi:10.1093/biomet/88.3.767
23. Lohr, S. (2009). Sampling: Design and analysis. Pacific Grove, CA: Duxbury Press.
24. Macmillan, R.,&Eliason, S. R. (2003). Characterizing the life course as role configurations and pathways: A latent structure approach. In J. T. Mortimer&M. J. Shanahan (Eds.), Handbook of the life course (pp. 529-554). Hingham, MA: Kluwer Academic. doi:10.1007/978-0-306-48247-2_24
25. Mahaffy, K. A. (2003). Gender, race, class, and the transition to adulthood: A critical review of the literature. Sociological Studies of Children and Youth, 9, 15-47. doi:10.1016/S1537-4661(03)09003-2
26. Malone, P. S., Lamis, D. A., Masyn, K. E.,&Northrup, T. F. (2010). A dual-process discrete-time survival analysis model: Application to the gateway drug hypothesis. Multivariate Behavioral Research, 45, 790-805. doi:10.1080/00273171.2010.519277
27. Mare, R. D. (1994). Discrete-time bivariate hazards with unobserved heterogeneity: A partially observed contingency table approach. In P. V. Marsden (Ed.), Sociological methodology (pp. 341-385). Oxford, England: Basil Blackwell. doi:10.2307/270987
28. Marini, M. (1984). Women's educational attainment and the timing of entry into parenthood. American Sociological Review, 49, 491-511. doi:10.2307/2095464
29. Marsh, H. W., Lüdtke, O., Trautwein, U.,&Morin, A. (2009). Classical latent profile analysis of academic self-concept dimensions: Synergy of person- and variable-centered approaches to theoretical models of selfconcept. Structural Equation Modeling, 16, 191-225. doi:10.1080/10705510902751010
30. McLachlan, G.,&Peel, D. (2000). Finite mixture models. New York, NY: Wiley. doi:10.1002/0471721182
31. Muthén, B.,&Asparouhov, T. (2008). Growth mixture modeling: Analysis with non-Gaussian random effects. In F. G. Davidian, M. G. Verbeke, & G. Mohlenberghs (Eds.), Longitudinal data analysis (pp. 143-165). Boca Raton, FL: Chapman&Hall/CRC Press.
32. Muthén, B.,&Masyn, K. (2005). Discrete-time survival mixture analysis. Journal of Educational and Behavioral Statistics, 30, 27-58. doi: 10.3102/10769986030001027
33. Muthén, L. K.,&Muthén, B. O. (1998-2010). Mplus user's guide (6th ed.). Los Angeles, CA: Muthén&Muthén.
34. Nagin, D. S. (1999). Analyzing developmental trajectories: A semiparametric group-based approach. Psychological Methods, 4, 139-157. doi: 10.1037/1082-989X.4.2.139
35. Nagin, D. S. (2005). Group-based modeling of development. Cambridge, MA: Harvard University Press.
36. Nagin, D. S.,&Land, K. C. (1993). Age, criminal careers, and population heterogeneity: Specification and estimation of a nonparametric mixed Poisson model. Criminology, 31, 327-362. doi:10.1111/j.1745-9125.1993.tb01133.x
37. Nagin, D. S.,&Odgers, C. L. (2010). Group-based trajectory modeling in clinical research. Annual Review of Clinical Psychology, 6, 109-138. doi:10.1146/annurev.clinpsy.121208.131413
38. Nylund, K. L., Asparouhov, T.,&Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling, 14, 535-569. doi:10.1080/10705510701575396
39. Petras, H.,&Masyn, K. (2010). General growth mixture analysis with antecedents and consequences of change. In A. Piquero&D. Weisburd (Eds.), Handbook of quantitative criminology (pp. 69-100). New York, NY: Springer. doi:10.1007/978-0-387-77650-7_5
40. Schwartz, S. J., Phelps, E., Lerner, J. V., Shi, H., Brown, C., Lewin-Bizan, S., . . . Lerner, R. M. (2010). Promotion as prevention: Positive youth development as protective against tobacco, alcohol, illicit drug, and sex initiation. Applied Developmental Science, 14, 197-211. doi:10.1080/10888691.2010.516186
41. Schwarz, G. (1978). Estimating the dimensions of a model. Annals of Statistics, 6, 461-464. doi:10.1214/aos/1176344136
42. Scott, K. M., Wells, J. E., Angermeyer, M. M., Brugha, T. S., Bromet, E. E., Demyttenaere, K. K.,&Kessler, R. C. (2010). Gender and the relationship between marital status and first onset of mood, anxiety and substance use disorders. Psychological Medicine: A Journal of Research in Psychiatry and the Allied Sciences, 40, 1495-1505.
43. Shanahan, M. (2000). Pathways to adulthood in changing societies: Variability and mechanisms in life course perspective. Annual Review of Sociology, 26, 667-692. doi:10.1146/annurev.soc.26.1.667
44. Shanahan, M., Miech, R.,&Elder, G. (1998). Changing pathways to attainment in men's lives, historical patterns of school, work, and social class. Social Forces, 77, 231-256.
45. Singer, J. D.,&Willett, J. B. (1993). It's about time: Using discrete-time survival analysis to study duration and the timing of events. Journal of Educational Statistics, 18, 155-195.
46. Singer, J. D.,&Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York, NY: Oxford University Press. doi:10.1093/acprof:oso/9780195152968.001.0001
47. Sterba, S. K., Baldasaro, R. E.,&Bauer, D. J. (2012). Factors affecting the adequacy and preferability of semiparametric groups-based approximations of continuous growth trajectories. Multivariate Behavioral Research, 47, 590-634. doi:10.1080/00273171.2012.692639
48. Vermunt, J. K. (1997). Log-linear models for event histories. Thousand Oaks, CA: Sage.
49. Vermunt, J. K.,&Magidson, J. (2002). Latent class cluster analysis. In J. Hagenaars&A. McCutcheon (Eds.), Applied latent class analysis (pp. 89-106). Cambridge, England: Cambridge University Press.
50. Vermunt, J. K.,&Magidson, J. (2005). Latent GOLD 4.0 choice user's guide. Belmont, MA: Statistical Innovations.