Inverse Propensity Score Weighting with a Latent Class Exposure: Estimating the Causal Effect of Reported Reasons for Alcohol Use on Problem Alcohol Use 16 Years Later
- 607 Downloads
Latent class analysis (LCA) has proven to be a useful tool for identifying qualitatively different population subgroups who may be at varying levels of risk for negative outcomes. Recent methodological work has improved techniques for linking latent class membership to distal outcomes; however, these techniques do not adjust for potential confounding variables that may provide alternative explanations for observed relations. Inverse propensity score weighting provides a way to account for many confounders simultaneously, thereby strengthening causal inference of the effects of predictors on outcomes. Although propensity score weighting has been adapted to LCA with covariates, there has been limited work adapting it to LCA with distal outcomes. The current study proposes a step-by-step approach for using inverse propensity score weighting together with the “Bolck, Croon, and Hagenaars” approach to LCA with distal outcomes (i.e., the BCH approach), in order to estimate the causal effects of reasons for alcohol use latent class membership during the year after high school (at age 19) on later problem alcohol use (at age 35) with data from the longitudinal sample in the Monitoring the Future study. A supplementary appendix provides evidence for the accuracy of the proposed approach via a small-scale simulation study, as well as sample programming code to conduct the step-by-step approach.
KeywordsLatent class analysis Causal inference Propensity scores Alcohol use Motives Reasons for drinking
The authors wish to thank Deborah D. Kloska for help with management of the Monitoring the Future data sets and Donna L. Coffman for early discussions that helped inform our thinking about causal latent class exposures.
This research was conducted at The Pennsylvania State University and The University of Michigan, and was supported by a seed grant from the National Center for Responsible Gaming (NCRG) and awards P50-DA039838, P50-DA010075, and R01-DA037902 from the National Institute on Drug Abuse (NIDA); data collection was supported by awards R01-DA001411 and R01-DA016575 from NIDA.
Compliance with Ethical Standards
Conflict of Interest
The authors declare that they have no conflicts of interest.
All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Where appropriate, informed consent and assent were obtained from all individual participants included in this study.
The content is solely the responsibility of the authors and does not necessarily represent the official views of NCRG, NIDA, or the National Institutes of Health.
- Coffman, D., Patrick, M. E., Palen, L., Rhoades, B. L., & Ventura, A. (2007). Why do high school seniors drink? Implications for a targeted approach. Prevention Science, 8, 241–248.Google Scholar
- Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Hillsdale: Laurence Erlbaum.Google Scholar
- 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
- Evans-Polce, R. J., Patrick, M. E., & Miech, R. (2017). Patterns of reasons for vaping in a national sample of adolescent vapers. Paper presented at the Society for Prevention Research 25th Annual Meeting: “Prevention and Public Systems of Care: Research, Policy and Practice,” Washington.Google Scholar
- Gilreath, T. D., Astor, R. A., Estrada Jr, J. N., Benbenishty, R., & Unger, J. B. (2014). School victimization and substance use among adolescents in California. Prevention Science, 15, 897–906.Google Scholar
- Imbens, G. (1999). The role of the propensity score in estimating dose-response functions (Tech. Work. Paper No. 237). Cambridge: National Bureau of Economic Research. Retreived from https://www.nber.org/papers/t0237.pdf.
- Jiang, L., Beals, J., Zhang, L., Mitchell, C. M., Manson, S. M., Acton, K. J., ... & Special Diabetes Program for Indians Demonstration Projects. (2012). Latent class analysis of stages of change for multiple health behaviors: Results from the Special Diabetes Program for Indians diabetes prevention program. Prevention Science, 13, 449–461.Google Scholar
- Johnston, L. D., O’Malley, P. M., Bachman, J. G., Schulenberg, J. E., & Miech, R. A. (2016). Monitoring the Future national survey results on drug use, 1975–2015: Volume 2, college students and adults ages 19–55. Ann Arbor: Institute for Social Research, The University of Michigan.Google Scholar
- Lanza, S. T., Bray, B. C., & Collins, L. M. (2013a). An introduction to latent class and latent transition analysis. In J. A. Schinka, W. F. Velicer, & I. B. Weiner (Eds.), Handbook of psychology (Vol. 2, 2nd ed., pp. 691–716). Hoboken: Wiley.Google Scholar
- Lanza, S. T., Dziak, J. J., Huang, L., Wagner, A., & Collins, L. M. (2015). PROC LCA & PROC LTA users’ guide (Version 1.3.2). University Park: The Methodology Center, Penn State. Retrieved from http://methodology.psu.edu
- Lanza, S. T., Schuler, M. S., & Bray, B. C. (2016). Latent class analysis with causal inference: The effect of adolescent depression on young adult substance use profile (Chp. 16, pp. 385–404). In W. Wiedermann & A. von Eye (Eds.), Causality and statistics. Hoboken: Wiley.Google Scholar
- Li, F., Lock Morgan, K., & Zaslavsky, A. M. (2016). Balancing covariates via propensity score weighting. Journal of the American Statistical Association. Advance online publication. https://doi.org/10.1080/01621459.2016.1260466.
- Miech, R. A., Johnston, L. D., O’Malley, P. M., Bachman, J. G., Schulenberg, J. E., & Patrick, M. E. (2017). Monitoring the Future national survey results on drug use, 1975–2016: Volume I, secondary school students. Ann Arbor, MI: Institute for Social Research, The University of Michigan.Google Scholar
- Muthén, L.K. and Muthén, B.O. (2015). Mplus User’s guide (7th ed.) Los Angeles, CA: Muthén & Muthén.Google Scholar
- Patrick, M. E., Evans-Polce, R., Kloska, D. D., Maggs, J. L., & Lanza, S. T. (2018). Age-related changes in associations between reasons for alcohol use and high-intensity drinking across young adulthood. Journal of Studies on Alcohol and Drugs.Google Scholar
- R Core Team (2015). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retreived from http://www.R-project.org.
- Schulenberg, J. E., Patrick, M. E., Kloska, D. D., Maslowsky, J., Maggs, J. L., & O’Malley, P. M. (2015). Substance use disorder in early midlife: A national prospective study on health and well-being correlates and long-term predictors. Substance Abuse: Research and Treatment, 9(Suppl 1), 41–57.Google Scholar
- Schulenberg, J. E., Johnston, L. D., O’Malley, P. M., Bachman, J. G., Miech, R. A., & Patrick, M. E. (2017). Monitoring the Future national survey results on drug use, 1975–2016: Volume II, college students and adults ages 19–55. Ann Arbor: Institute for Social Research, The University of Michigan.Google Scholar
- Schuler, M. S. (2013). Estimating the relative treatment effects of natural clusters of adolescent substance abuse treatment services: Combining latent class analysis and propensity score methods. Unpublished doctoral dissertation. Baltimore: Johns Hopkins University Retrieved from https://jscholarship.library.jhu.edu/bitstream/handle/1774.2/36988/SCHULER-DISSERTATION-2014.pdf.Google Scholar
- Vermunt, J. K., & Magidson, J. (2015). Upgrade manual for Latent GOLD 5.1. Belmont, MA: Statistical Innovations.Google Scholar
- Yamaguchi, K. (2015). Extensions of Rubin’s causal model for a latent-class treatment variable: An analysis of the effects of employers’ work-life balance policies on women’s income attainment in Japan. Research Institute of Economy, Trade and Industry Discussion Paper Series (No. 15-E-090). Tokyo, Japan: The Research Institute of Economy, Trade and Industry. Retrieved from http://www.rieti.go.jp/jp/publications/dp/15e090.pdf.
- Zanutto, E. L. (2006). A comparison of propensity score and linear regression analysis of complex survey data. Journal of Data Science, 4, 67–91.Google Scholar