Abstract
We propose Bayesian parametric and semiparametric partially linear regression methods to analyze the outcome-dependent follow-up data when the random time of a follow-up measurement of an individual depends on the history of both observed longitudinal outcomes and previous measurement times. We begin with the investigation of the simplifying assumptions of Lipsitz, Fitzmaurice, Ibrahim, Gelber, and Lipshultz, and present a new model for analyzing such data by allowing subject-specific correlations for the longitudinal response and by introducing a subject-specific latent variable to accommodate the association between the longitudinal measurements and the follow-up times. An extensive simulation study shows that our Bayesian partially linear regression method facilitates accurate estimation of the true regression line and the regression parameters. We illustrate our new methodology using data from a longitudinal observational study.
| Original language | English |
|---|---|
| Pages (from-to) | 952-961 |
| Number of pages | 10 |
| Journal | Journal of the American Statistical Association |
| Volume | 102 |
| Issue number | 479 |
| DOIs | |
| State | Published - Sep 1 2007 |
Fingerprint
Keywords
- Bayesian cubic smoothing spline
- Latent variable
- Partially linear model
ASJC Scopus subject areas
- Mathematics(all)
- Statistics and Probability
Cite this
Longitudinal studies with outcome-dependent follow-up : Models and bayesian regression. / Ryu, Duchwan; Sinha, Debajyoti; Mallick, Bani; Lipsitz, Stuart R.; Lipshultz, Steven E.
In: Journal of the American Statistical Association, Vol. 102, No. 479, 01.09.2007, p. 952-961.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Longitudinal studies with outcome-dependent follow-up
T2 - Models and bayesian regression
AU - Ryu, Duchwan
AU - Sinha, Debajyoti
AU - Mallick, Bani
AU - Lipsitz, Stuart R.
AU - Lipshultz, Steven E
PY - 2007/9/1
Y1 - 2007/9/1
N2 - We propose Bayesian parametric and semiparametric partially linear regression methods to analyze the outcome-dependent follow-up data when the random time of a follow-up measurement of an individual depends on the history of both observed longitudinal outcomes and previous measurement times. We begin with the investigation of the simplifying assumptions of Lipsitz, Fitzmaurice, Ibrahim, Gelber, and Lipshultz, and present a new model for analyzing such data by allowing subject-specific correlations for the longitudinal response and by introducing a subject-specific latent variable to accommodate the association between the longitudinal measurements and the follow-up times. An extensive simulation study shows that our Bayesian partially linear regression method facilitates accurate estimation of the true regression line and the regression parameters. We illustrate our new methodology using data from a longitudinal observational study.
AB - We propose Bayesian parametric and semiparametric partially linear regression methods to analyze the outcome-dependent follow-up data when the random time of a follow-up measurement of an individual depends on the history of both observed longitudinal outcomes and previous measurement times. We begin with the investigation of the simplifying assumptions of Lipsitz, Fitzmaurice, Ibrahim, Gelber, and Lipshultz, and present a new model for analyzing such data by allowing subject-specific correlations for the longitudinal response and by introducing a subject-specific latent variable to accommodate the association between the longitudinal measurements and the follow-up times. An extensive simulation study shows that our Bayesian partially linear regression method facilitates accurate estimation of the true regression line and the regression parameters. We illustrate our new methodology using data from a longitudinal observational study.
KW - Bayesian cubic smoothing spline
KW - Latent variable
KW - Partially linear model
UR - http://www.scopus.com/inward/record.url?scp=35348870652&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=35348870652&partnerID=8YFLogxK
U2 - 10.1198/016214507000000248
DO - 10.1198/016214507000000248
M3 - Article
VL - 102
SP - 952
EP - 961
JO - Journal of the American Statistical Association
JF - Journal of the American Statistical Association
SN - 0162-1459
IS - 479
ER -