Citation

Bray BC, Dziak JJ, Patrick ME, Lanza ST. Prev. Sci. 2019; 20(3): 394-406.

Affiliation

Department of Biobehavioral Health, Penn State, 219 Biobehavioral Health Bldg., University Park, PA, 16802, USA.

Copyright

(Copyright © 2019, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s11121-018-0883-8

PMID

29542004

Abstract

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.

Language: en

Keywords

Alcohol use; Causal inference; Latent class analysis; Motives; Propensity scores; Reasons for drinking