Methods to Account for Uncertainty in Latent Class Assignments When Using Latent Classes as Predictors in Regression Models, with Application to Acculturation Strategy Measures

Details

• Personal Author:
  Elliott MR ; Kanaya A ; Mukherjee B ; Needham BL ; Zhao Z
• Description:
  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. [Description provided by NIOSH]
• Subjects:
  Epidemiology Occupational Health Safety Surveys And Questionnaires
• Keywords:
  Author Keywords: Bayesian Models Depression Latent Class Analysis Measurement Error Models Questionnaires Statistical Analysis Statistical Quality Control
• ISSN:
  1044-3983
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  California ; Michigan ; OSHA Region 5 ; OSHA Region 9
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  194-204
• Volume:
  31
• Issue:
  2
• NIOSHTIC Number:
  nn:20059191
• Citation:
  Epidemiology 2020 Mar; 31(2):194-204
• Contact Point Address:
  Michael R. Elliott, Department of Biostatistics, School of Public Health, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109
• Email:
  mrelliot@umich.edu
• Federal Fiscal Year:
  2020
• Performing Organization:
  University of Michigan, Ann Arbor
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  Epidemiology
• End Date:
  20280630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:639116554a5a91680fdd32af9317d4b7552043f31552a53d7a0485943f41ffbabcc3b5135aa584b787e7b87f9b21247eca05d2506a35114af1deaecc7c9e41b7
• Download URL:
  https://stacks.cdc.gov/view/cdc/223485/cdc_223485_DS1.pdf
• File Type:
  [PDF - 553.09 KB ]