TY - JOUR
T1 - Partially linear mixed-effects joint models for skewed and missing longitudinal competing risks outcomes
AU - Lu, Tao
AU - Lu, Minggen
AU - Wang, Min
AU - Zhang, Jun
AU - Dong, Guang Hui
AU - Xu, Yong
N1 - Publisher Copyright:
© 2017, © 2017 Taylor & Francis.
PY - 2019/11/2
Y1 - 2019/11/2
N2 - Longitudinal competing risks data frequently arise in clinical studies. Skewness and missingness are commonly observed for these data in practice. However, most joint models do not account for these data features. In this article, we propose partially linear mixed-effects joint models to analyze skew longitudinal competing risks data with missingness. In particular, to account for skewness, we replace the commonly assumed symmetric distributions by asymmetric distribution for model errors. To deal with missingness, we employ an informative missing data model. The joint models that couple the partially linear mixed-effects model for the longitudinal process, the cause-specific proportional hazard model for competing risks process and missing data process are developed. To estimate the parameters in the joint models, we propose a fully Bayesian approach based on the joint likelihood. To illustrate the proposed model and method, we implement them to an AIDS clinical study. Some interesting findings are reported. We also conduct simulation studies to validate the proposed method.
AB - Longitudinal competing risks data frequently arise in clinical studies. Skewness and missingness are commonly observed for these data in practice. However, most joint models do not account for these data features. In this article, we propose partially linear mixed-effects joint models to analyze skew longitudinal competing risks data with missingness. In particular, to account for skewness, we replace the commonly assumed symmetric distributions by asymmetric distribution for model errors. To deal with missingness, we employ an informative missing data model. The joint models that couple the partially linear mixed-effects model for the longitudinal process, the cause-specific proportional hazard model for competing risks process and missing data process are developed. To estimate the parameters in the joint models, we propose a fully Bayesian approach based on the joint likelihood. To illustrate the proposed model and method, we implement them to an AIDS clinical study. Some interesting findings are reported. We also conduct simulation studies to validate the proposed method.
KW - Bayesian inference
KW - competing risks
KW - longitudinal survival data
KW - partially linear mixed-effects models
KW - proportional hazard models
UR - http://www.scopus.com/inward/record.url?scp=85039154010&partnerID=8YFLogxK
U2 - 10.1080/10543406.2017.1378663
DO - 10.1080/10543406.2017.1378663
M3 - 文章
C2 - 29252088
AN - SCOPUS:85039154010
SN - 1054-3406
VL - 29
SP - 971
EP - 989
JO - Journal of Biopharmaceutical Statistics
JF - Journal of Biopharmaceutical Statistics
IS - 6
ER -