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Prevention Science

, Volume 14, Issue 2, pp 157–168 | Cite as

Latent Class Analysis: An Alternative Perspective on Subgroup Analysis in Prevention and Treatment

  • Stephanie T. Lanza
  • Brittany L. Rhoades
Article

Abstract

The overall goal of this study is to introduce latent class analysis (LCA) as an alternative approach to latent subgroup analysis. Traditionally, subgroup analysis aims to determine whether individuals respond differently to a treatment based on one or more measured characteristics. LCA provides a way to identify a small set of underlying subgroups characterized by multiple dimensions which could, in turn, be used to examine differential treatment effects. This approach can help to address methodological challenges that arise in subgroup analysis, including a high Type I error rate, low statistical power, and limitations in examining higher-order interactions. An empirical example draws on N = 1,900 adolescents from the National Longitudinal Survey of Adolescent Health. Six characteristics (household poverty, single-parent status, peer cigarette use, peer alcohol use, neighborhood unemployment, and neighborhood poverty) are used to identify five latent subgroups: Low Risk, Peer Risk, Economic Risk, Household & Peer Risk, and Multi-Contextual Risk. Two approaches for examining differential treatment effects are demonstrated using a simulated outcome: 1) a classify-analyze approach and, 2) a model-based approach based on a reparameterization of the LCA with covariates model. Such approaches can facilitate targeting future intervention resources to subgroups that promise to show the maximum treatment response.

Keywords

Latent class analysis Subgroup analysis Differential treatment effects Adolescents Multiple risks 

References

  1. Agrawal, A., Lynskey, M. T., Madden, P. A., Bucholz, K. K., & Heath, A. C. (2007). A latent class analysis of illicit drug abuse/dependence: Results from the National Epidemiological Survey on Alcohol and Related Conditions. Addiction, 102, 94–104.PubMedCrossRefGoogle Scholar
  2. Agresti, A. (2002). Categorical data analysis (2nd ed.). New York: Wiley.CrossRefGoogle Scholar
  3. Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Thousand Oaks, CA: Sage.Google Scholar
  4. Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 716–723.CrossRefGoogle Scholar
  5. Anderson, K. G., Ramo, D. E., Cummins, K. M., & Brown, S. A. (2010). Alcohol and drug involvement after adolescent treatment and functioning during emerging adulthood. Drug and Alcohol Dependence, 107, 171–181.PubMedCrossRefGoogle Scholar
  6. Arthur, M. W., Hawkins, J. D., Pollard, J., Catalano, R. F., & Baglioni, A. J. (2002). Measuring risk and protective factors for substance use, delinquency, and other adolescent problem behaviors: The Communities that Care Youth Survey. Evaluation Review, 26, 575–601.PubMedGoogle Scholar
  7. Baucom, B. R., Atkins, D. C., Simpson, L. E., & Christensen, A. (2009). Prediction of response to treatment in a randomized clinical trial of couple therapy: A 2-year follow-up. Journal of Consulting and Clinical Psychology, 77, 160–173.PubMedCrossRefGoogle Scholar
  8. Bergman, L. R., & Magnusson, D. (1997). A person-oriented approach in research on developmental psychopathology. Development and Psychopathology, 9, 291–319.PubMedCrossRefGoogle Scholar
  9. Bozdogan, H. (1987). Model selection and Akaike’s Information Criterion (AIC): The general theory and its analytical extensions. Psychometrika, 52, 345–370.CrossRefGoogle Scholar
  10. Brooks-Gunn, J., Duncan, G. J., & Maritato, N. (1997). Poor families, poor outcomes: The well-being of children and youth. In G. J. Duncan & J. Brooks-Gunn (Eds.), Consequences of growing up poor (pp. 1–17). New York: Russell Sage.Google Scholar
  11. Catalano, R. F., & Hawkins, J. D. (1996). The social development model: A theory of anti-social behavior. In J. D. Hawkins (Ed.), Delinquency and crime: Current theories (pp. 149–197). New York: Cambridge University Press.Google Scholar
  12. Chassin, L., Pitts, S. C., & Prost, J. (2002). Binge drinking trajectories from adolescence to emerging adulthood in a high-risk sample: Predictors and substance abuse outcomes. Journal of Consulting and Clinical Psychology, 70, 67–78.PubMedCrossRefGoogle Scholar
  13. Chung, T., Maisto, S. A., Cornelius, J. R., & Marti, C. S. (2004). Adolescents’ alcohol and drug use trajectories in the year following treatment. Journal of Studies on Alcohol, 65, 105–114.PubMedGoogle Scholar
  14. Cicchetti, D., & Rogosch, F. A. (1996). Equifinality and multifinality in developmental psychopathology. Development and Psychopathology, 8, 597–600.CrossRefGoogle Scholar
  15. Coffman, D. L., Patrick, M. E., Palen, L. A., Rhoades, B. L., & Ventura, A. K. (2007). Why do high school seniors drink? Implications for a targeted approach to intervention. Prevention Science, 8, 241–248.PubMedCrossRefGoogle Scholar
  16. Coie, J. D., Watt, N. F., West, S. G., Hawkins, J. D., Asarnow, J. R., Markman, H. J., et al. (1993). The science of prevention: A conceptual framework and some directions for a national research program. American Psychologist, 48, 1013–1022.PubMedCrossRefGoogle Scholar
  17. Colder, C. R., Campbell, R. T., Ruel, E., Richardson, J. L., & Flay, B. R. (2002). A finite mixture model of growth trajectories of adolescent alcohol use: Predictors and consequences. Journal of Consulting and Clinical Psychology, 70, 976–985.PubMedCrossRefGoogle Scholar
  18. Collins, L. M., & Lanza, S. T. (2010). Latent class and latent transition analysis: With applications in the social, behavioral, and health sciences. New York: Wiley.Google Scholar
  19. Collins, L. M., Fidler, P. L., Wugalter, S. E., & Long, J. D. (1993). Goodness-of-fit testing for latent class models. Multivariate Behavioral Research, 28, 375–389.CrossRefGoogle Scholar
  20. Collins, L. M., Graham, J. W., Long, J. D., & Hansen, W. B. (1994). Crossvalidation of latent class models of early substance use onset. Multivariate Behavioral Research, 29, 165–183.CrossRefGoogle Scholar
  21. Collins, L. M., Murphy, S. A., & Bierman, K. (2004). A conceptual framework for adaptive preventive interventions. Prevention Science, 3, 185–196.CrossRefGoogle Scholar
  22. Conduct Problems Prevention Research Group. (1992). A developmental and clinical model for the prevention of conduct disorders: The FAST Track Program. Development and Psychopathology, 4, 509–527.CrossRefGoogle Scholar
  23. Dayton, C. M., & Macready, G. B. (1988). Concomitant variable latent class models. Journal of the American Statistical Association, 83, 173–178.CrossRefGoogle Scholar
  24. Elkin, I., Gibbons, R. D., Shea, M. T., Sotsky, S. M., Watkins, J. T., Pilkonis, P. A., et al. (1995). Initial severity and differential treatment outcome in the National Institute of Mental Health Treatment of Depression Collaborative Research Program. Journal of Consulting and Clinical Psychology, 63, 841–847.PubMedCrossRefGoogle Scholar
  25. Everitt, B. S., & Hand, D. J. (1981). Finite mixture distributions. London: Chapman and Hall.CrossRefGoogle Scholar
  26. Fairchild, A. J., & MacKinnon, D. P. (2009). A general model for testing mediation and moderation effects. Prevention Science, 10, 87–99.PubMedCrossRefGoogle Scholar
  27. Gerard, J. M., & Buehler, C. (2004). Cumulative environmental risk and youth maladjustment: The role of youth attributes. Child Development, 75, 1832–1849.PubMedCrossRefGoogle Scholar
  28. Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61, 215–231.CrossRefGoogle Scholar
  29. 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 [WWW document]. URL: http://www.cpc.unc.edu/projects/addhealth/design.
  30. Hawkins, J. D., Catalano, R. F., & Miller, J. Y. (1992). Risk and protective factors for alcohol and other drug problems in adolescence and early adulthood: Implications for substance abuse prevention. Psychological Bulletin, 112, 64–105.PubMedCrossRefGoogle Scholar
  31. Kessler, R. C., Chiu, W. T., Demler, O., & Walters, E. E. (2005). Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Archives of General Psychiatry, 62, 617–627.PubMedCrossRefGoogle Scholar
  32. Komro, K. A., Tobler, A. L., Maldonado-Molina, M. M., & Perry, C. L. (2010). Effects of alcohol use initiation patterns on high-risk behaviors among urban, low-income, young adolescents. Prevention Science, 11, 14–23.PubMedCrossRefGoogle Scholar
  33. Langeheine, R., Pannekoek, J., & van de Pol, F. (1996). Bootstrapping goodness-of-fit measures in categorical data analysis. Sociological Methods & Research, 24, 492–516.CrossRefGoogle Scholar
  34. Lanza, S. T., Collins, L. M., Schafer, J. L., & Flaherty, B. P. (2005). Using data augmentation to obtain standard errors and conduct hypothesis tests in latent class and latent transition analysis. Psychological Methods, 10, 84–100.PubMedCrossRefGoogle Scholar
  35. Lanza, S. T., Collins, L. M., Lemmon, D. R., & Schafer, J. L. (2007). PROC LCA: A SAS procedure for latent class analysis. Structural Equation Modeling, 14, 671–694. PMCID: PMC2785099.PubMedCrossRefGoogle Scholar
  36. Lanza, S. T., Lemmon, D. R., Dziak, J. J., Huang, L., Schafer, J. L., & Collins, L. M. (2010). PROC LCA & PROC LTA user’s guide version 1.2.5 beta. University Park, PA: The Methodology Center, Penn State.Google Scholar
  37. Lanza, S. T., Rhoades, B. L., Nix, R. L., Greenberg, M. T., & the Conduct Problems Prevention Research Group. (2010). Modeling the interplay of multilevel risk factors for future academic and behavior problems: A person-centered approach. Development and Psycholopathology, 22, 313–335.CrossRefGoogle Scholar
  38. Laska, M. N., Pasch, K. E., Lust, K., Story, M., & Ehlinger, E. (2009). Latent class analysis of lifestyle characteristics and health risk behaviors among college youth. Prevention Science, 10, 376–386.PubMedCrossRefGoogle Scholar
  39. Lazarsfeld, P. F., & Henry, N. W. (1968). Latent structure analysis. Boston, MA: Houghton Mifflin.Google Scholar
  40. Loeber, R. (1990). Development and risk factors of juvenile antisocial behavior and delinquency. Clinical Psychology Review, 10, 1–41.CrossRefGoogle Scholar
  41. Luthar, S. S. (1993). Annotation: Methodological and conceptual issues in research on childhood resilience. Journal of Child Psychology and Psychiatry & Allied Disciplines, 34, 441–453.CrossRefGoogle Scholar
  42. MacKinnon, D. P. (2009). Introduction to statistical mediation analysis. New York: Lawrence Erlbaum Associates.Google Scholar
  43. McLachlan, G., & Peel, D. (2000). Finite mixture models. New York: Wiley.CrossRefGoogle Scholar
  44. Muthén, L. K., & Muthén, B. O. (1998–2007). Mplus user’s guide (5th ed.). Los Angeles, CA: Authors.Google Scholar
  45. 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.CrossRefGoogle Scholar
  46. Ondersma, S. J., Winhusen, T., Erickson, S. J., Stine, S. M., & Wang, Y. (2009). Motivation enahancement therapy with pregnant substance-abusing women: Does baseline motivation moderate efficacy? Drug and Alcohol Dependence, 101, 74–79.PubMedCrossRefGoogle Scholar
  47. Oxford, M. L., Gilchrist, L. D., Morrison, D. M., Gillmore, M. R., Lohr, M. J., & Lewis, S. M. (2003). Alcohol use among adolescent mothers: Heterogeneity in growth curves, predictors, and outcomes of alcohol use over time. Prevention Science, 4, 15–26.PubMedCrossRefGoogle Scholar
  48. Rutter, M. (1979). Protective factors in children’s responses to stress and disadvantage. In M. W. Kent & J. E. Rolf (Eds.), Primary prevention of psychopathology: Vol 3. Social competence in children (pp. 49–74). Hanover, NH: University of New England Press.Google Scholar
  49. Sameroff, A. J., Seifer, R., Baldwin, C. P., & Baldwin, A. (1993). Stability of intelligence from preschool to adolescence: The influence of social and family risk factors. Child Development, 64, 80–97.PubMedCrossRefGoogle Scholar
  50. Scheier, L. M., Abdallah, A. B., Inciardi, J. A., Copeland, J., & Cottler, L. B. (2008). Tri-city study of Ecstasy use problems: A latent class analysis. Drug and Alcohol Dependence, 98, 249–263.PubMedCrossRefGoogle Scholar
  51. Schochet, P. Z. (2008). Technical methods report: Guidelines for multiple testing in impact evaluations (NCEE 2008-4018). Washington, DC: National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education.Google Scholar
  52. Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6, 461–464.CrossRefGoogle Scholar
  53. Sclove, S. L. (1987). Application of model-selection criteria to some problems in multivariate analysis. Psychometrika, 52, 333–343.CrossRefGoogle Scholar
  54. Shin, S. H., Hong, H. G., & Hazen, A. L. (2010). Childhood sexual abuse and adolescent substance use: A latent class analysis. Drug and Alcohol Dependence, 109, 226–235.PubMedCrossRefGoogle Scholar
  55. Syvertsen, A. K., Cleveland, M. J., Gayles, J. G., Tibbits, M. K., & Faulk, M. T. (2010). Profiles of protection from substance use among adolescents. Prevention Science, 11, 185–196.PubMedCrossRefGoogle Scholar
  56. Titterington, D., Smith, A., & Makov, U. (1985). Statistical analysis of finite mixture distributions. Chichester, UK: Wiley.Google Scholar
  57. Van de Pol, F., Langeheine, R., & De Jong, W. (1996). User’s manual: A latent class program. The Netherlands: Voorburg.Google Scholar
  58. Vermunt, J. K., & Magidson, J. (2005). Latent GOLD 4.0 user’s guide. Belmont, MA: Statistical Innovations Inc.Google Scholar
  59. Von Eye, A., & Bergman, L. R. (2003). Research strategies in developmental psychopathology: Dimensional identity an the person-oriented approach. Development and Psychopathology, 15, 553–580.Google Scholar
  60. Wang, C.-P., Brown, C. H., & Bandeen-Roche, K. (2005). Residual diagnostics for growth mixture models: Examining the impact of a preventive intervention on multiple trajectories of aggressive behavior. Journal of the American Statistical Association, 100, 1054–1076.CrossRefGoogle Scholar

Copyright information

© Society for Prevention Research 2011

Authors and Affiliations

  1. 1.The Methodology CenterThe Pennsylvania State UniversityState CollegeUSA
  2. 2.Prevention Research CenterThe Pennsylvania State UniversityUniversity ParkUSA

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