Abstract
We propose a mixture model for data with an ordinal outcome and a longitudinal covariate that is subject to missingness. Data from a tailored telephone delivered, smoking cessation intervention for construction laborers are used to illustrate the method, which considers as an outcome a categorical measure of smoking cessation, and evaluates the effectiveness of the motivational telephone interviews on this outcome. We propose two model structures for the longitudinal covariate, for the case when the missing data are missing at random, and when the missing data mechanism is non-ignorable. A generalized EM algorithm is used to obtain maximum likelihood estimates.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1040-1052 |
| Number of pages | 13 |
| Journal | Journal of Applied Statistics |
| Volume | 41 |
| Issue number | 5 |
| DOIs | |
| State | Published - May 2014 |
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Keywords
- longitudinal covariates
- missingness
- ordinal outcomes
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty
Cite this
Analysis of ordinal outcomes with longitudinal covariates subject to missingness. / Goodman, Melody; Li, Yi; Stoddard, Anne M.; Sorensen, Glorian.
In: Journal of Applied Statistics, Vol. 41, No. 5, 05.2014, p. 1040-1052.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Analysis of ordinal outcomes with longitudinal covariates subject to missingness
AU - Goodman, Melody
AU - Li, Yi
AU - Stoddard, Anne M.
AU - Sorensen, Glorian
PY - 2014/5
Y1 - 2014/5
N2 - We propose a mixture model for data with an ordinal outcome and a longitudinal covariate that is subject to missingness. Data from a tailored telephone delivered, smoking cessation intervention for construction laborers are used to illustrate the method, which considers as an outcome a categorical measure of smoking cessation, and evaluates the effectiveness of the motivational telephone interviews on this outcome. We propose two model structures for the longitudinal covariate, for the case when the missing data are missing at random, and when the missing data mechanism is non-ignorable. A generalized EM algorithm is used to obtain maximum likelihood estimates.
AB - We propose a mixture model for data with an ordinal outcome and a longitudinal covariate that is subject to missingness. Data from a tailored telephone delivered, smoking cessation intervention for construction laborers are used to illustrate the method, which considers as an outcome a categorical measure of smoking cessation, and evaluates the effectiveness of the motivational telephone interviews on this outcome. We propose two model structures for the longitudinal covariate, for the case when the missing data are missing at random, and when the missing data mechanism is non-ignorable. A generalized EM algorithm is used to obtain maximum likelihood estimates.
KW - longitudinal covariates
KW - missingness
KW - ordinal outcomes
UR - http://www.scopus.com/inward/record.url?scp=84897628288&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84897628288&partnerID=8YFLogxK
U2 - 10.1080/02664763.2013.859236
DO - 10.1080/02664763.2013.859236
M3 - Article
VL - 41
SP - 1040
EP - 1052
JO - Journal of Applied Statistics
JF - Journal of Applied Statistics
SN - 0266-4763
IS - 5
ER -