Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

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

Authors
Harel, OferChung, HwanMiglioretti, Diana
Issue Date
7월-2013
Publisher
WILEY-BLACKWELL
Keywords
Latent class regression; Missing data; Missing information; Multiple imputation
Citation
BIOMETRICAL JOURNAL, v.55, no.4, pp.541 - 553
Indexed
SCIE
SCOPUS
Journal Title
BIOMETRICAL JOURNAL
Volume
55
Number
4
Start Page
541
End Page
553
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/102931
DOI
10.1002/bimj.201200020
ISSN
0323-3847
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.
Files in This Item
There are no files associated with this item.
Appears in
Collections
College of Political Science & Economics > Department of Statistics > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Altmetrics

Total Views & Downloads

BROWSE