title: Latent class regression: inference and estimation with two-stage multiple imputation.
abstract: 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.
authors.affiliation: Department of Statistics, University of Connecticut, 215 Glenbrook Road, Unit 4120, Storrs, CT 06269-4120, USA. ofer.harel@uconn.edu
authors.forename: Ofer
authors.initials: O
authors.lastname: Harel
authors.forename: Hwan
authors.initials: H
authors.lastname: Chung
authors.forename: Diana
authors.initials: D
authors.lastname: Miglioretti
mesh_terms: D000465:Algorithms; D001699:Biometry / Q000379:methods*; D001943:Breast Neoplasms / Q000453:epidemiology / Q000208:ethnology / Q000473:pathology; D015331:Cohort Studies; D006801:Humans; D060787:Neoplasm Grading; D009367:Neoplasm Staging; D011446:Prospective Studies; D012044:Regression Analysis; D013223:Statistics as Topic / Q000379:methods*; D035501:Uncertainty
keywords: Latent class regression; Missing data; Missing information; Multiple imputation