Sensitivity analysis of informatively coarsened data using pattern mixture models.
about
Statistical analysis with missing exposure data measured by proxy respondents: a misclassification problem within a missing-data problem.Methods for using clinical laboratory test results as baseline confounders in multi-site observational database studies when missing data are expected.Sensitivity analysis for nonignorable missingness and outcome misclassification from proxy reports.
P2860
Sensitivity analysis of informatively coarsened data using pattern mixture models.
description
2009 nî lūn-bûn
@nan
2009 թուականի Նոյեմբերին հրատարակուած գիտական յօդուած
@hyw
2009 թվականի նոյեմբերին հրատարակված գիտական հոդված
@hy
2009年の論文
@ja
2009年論文
@yue
2009年論文
@zh-hant
2009年論文
@zh-hk
2009年論文
@zh-mo
2009年論文
@zh-tw
2009年论文
@wuu
name
Sensitivity analysis of informatively coarsened data using pattern mixture models.
@ast
Sensitivity analysis of informatively coarsened data using pattern mixture models.
@en
Sensitivity analysis of informatively coarsened data using pattern mixture models.
@nl
type
label
Sensitivity analysis of informatively coarsened data using pattern mixture models.
@ast
Sensitivity analysis of informatively coarsened data using pattern mixture models.
@en
Sensitivity analysis of informatively coarsened data using pattern mixture models.
@nl
prefLabel
Sensitivity analysis of informatively coarsened data using pattern mixture models.
@ast
Sensitivity analysis of informatively coarsened data using pattern mixture models.
@en
Sensitivity analysis of informatively coarsened data using pattern mixture models.
@nl
P2860
P1476
Sensitivity analysis of informatively coarsened data using pattern mixture models.
@en
P2093
Michelle Shardell
Samer S El-Kamary
P2860
P304
P356
10.1080/10543400903242779
P577
2009-11-01T00:00:00Z