TY - JOUR
T1 - Modeling Longitudinal-Competing Risks Data With Skew Distribution and Mismeasured Covariate
AU - Lu, Tao
N1 - Publisher Copyright:
© 2017 American Statistical Association.
PY - 2017/1/2
Y1 - 2017/1/2
N2 - The study on relationship between HIV viral load and CD4 counts is critical for AIDS treatment. We study the varying relationship between viral load and CD4 counts by accounting for factors usually encountered in practice: skewed distribution in data, competing risks time-to-event, and mismeasured covariate. We propose a joint modeling approach to take into account all these factors. A Bayesian approach is adopted to make inference on the joint model. The proposed model and method are applied to an AIDS study. To testify the validity of the method, simulation studies are performed.
AB - The study on relationship between HIV viral load and CD4 counts is critical for AIDS treatment. We study the varying relationship between viral load and CD4 counts by accounting for factors usually encountered in practice: skewed distribution in data, competing risks time-to-event, and mismeasured covariate. We propose a joint modeling approach to take into account all these factors. A Bayesian approach is adopted to make inference on the joint model. The proposed model and method are applied to an AIDS study. To testify the validity of the method, simulation studies are performed.
KW - Bayesian inference
KW - Competing risks
KW - Longitudinal data
KW - Measurement error
KW - Mixed-effects models
UR - http://www.scopus.com/inward/record.url?scp=85014635051&partnerID=8YFLogxK
U2 - 10.1080/19466315.2016.1208624
DO - 10.1080/19466315.2016.1208624
M3 - Article
AN - SCOPUS:85014635051
SN - 1946-6315
VL - 9
SP - 73
EP - 84
JO - Statistics in Biopharmaceutical Research
JF - Statistics in Biopharmaceutical Research
IS - 1
ER -