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

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초록

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.

키워드

Latent class regressionMissing dataMissing informationMultiple imputationCLASS MODELS
제목
Latent class regression: Inference and estimation with two-stage multiple imputation
저자
Harel, OferChung, HwanMiglioretti, Diana
DOI
10.1002/bimj.201200020
발행일
2013-07
유형
Article
저널명
Biometrical Journal
55
4
페이지
541 ~ 553