Latent class regression: Inference and estimation with two-stage multiple imputation

Ofer Harel, Hwan Chung, Diana L Miglioretti

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Latent class regression (LCR) is a popular method for analyzing multiple categorical outcomes. While nonresponse to the manifest items is a common complication, inferences of LCR can be evaluated using maximum likelihood, multiple imputation, and two-stage multiple imputation. Under similar missing data assumptions, the estimates and variances from all three procedures are quite close. However, multiple imputation and two-stage multiple imputation can provide additional information: estimates for the rates of missing information. The methodology is illustrated using an example from a study on racial and ethnic disparities in breast cancer severity.

Original languageEnglish (US)
Pages (from-to)541-553
Number of pages13
JournalBiometrical Journal
Issue number4
StatePublished - Jul 2013


  • Latent class regression
  • Missing data
  • Missing information
  • Multiple imputation

ASJC Scopus subject areas

  • Statistics and Probability
  • Medicine(all)
  • Statistics, Probability and Uncertainty


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