MethodsMethods to Account for Uncertainty in Latent Class Assignments When Using Latent Classes as Predictors in Regression Models, with Application to Acculturation Strategy MeasuresElliott, Michael R.a,b; Zhao, Zhangchena; Mukherjee, Bhramara; Kanaya, Alkac; Needham, Belinda L.dAuthor Information From the aDepartment of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI bSurvey Methodology Program, Institute for Social Research, University of Michigan, Ann Arbor, MI cDepartment of Medicine, University of California - San Francisco, San Francisco, CA dDepartment of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI. Submitted September 25, 2018; accepted November 6, 2019. Supported by NIH Grant No. 1R01 HL093009. The authors report no conflicts of interest. Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com). Correspondence: Michael R. Elliott, Department of Biostatistics, School of Public Health, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109. E-mail: [email protected]. Epidemiology: March 2020 - Volume 31 - Issue 2 - p 194-204 doi: 10.1097/EDE.0000000000001139 Buy SDC Metrics Abstract Latent class models have become a popular means of summarizing survey questionnaires and other large sets of categorical variables. Often these classes are of primary interest to better understand complex patterns in data. Increasingly, these latent classes are reified into predictors of other outcomes of interests, treating the most likely class as the true class to which an individual belongs even though there is uncertainty in class membership. This uncertainty can be viewed as a form of measurement error in predictors, leading to bias in the estimates of the regression parameters associated with the latent classes. Despite this fact, there is very limited literature treating latent class predictors as measurement error models. Most applications ignore this issue and fit a two-stage model that treats the modal class prediction as truth. Here, we develop two approaches—one likelihood-based, the other Bayesian—to implement a joint model for latent class analysis and outcome prediction. We apply these methods to an analysis of how acculturation behaviors predict depression in South Asian immigrants to the United States. A simulation study gives guidance for when a two-stage model can be safely implemented and when the joint model may be required. Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.