The aims of this proposal are to develop flexible, semiparametric statistical models, methods, and inferences for longitudinal data, and to apply these models and methods to analyze AIDS data. The proposal consists of two aims. The first specific aim is to develop flexible models, methods, and inference for longitudinal data, which will involve (a) applying the penalized spline methods to longitudinal data analysis, comparing the methods with local kernel, regression spline methods to determine which is best in practice, developing statistical inference methods for penalized spline with longitudinal data; (b) developing flexible and efficient methods for time-varying coefficient mixed-effects models with longitudinal data, including investigation of local kernel regression and the penalized spline methods; and developing flexible methods for generalized varying-coefficient mixed-effects models with longitudinal data; (c) developing flexible methods for general two-stage semiparametric nonlinear mixed-effects models; (d) developing computer packages to implement the proposed methods.
The second aim i s to apply the proposed models and methods developed to study HIV dynamics by using data from AIDS clinical trials run by the AIDS Clinical Trial Group (ACTG) and data from AIDS clinical trials conducted at St. Jude Children's Research Hospital. We will focus on (a) characterizing long-term HIV/T-cell dynamics in HIV-l-infected patients treated with highly active antiretroviral therapy by using flexible, nonparametric or semiparametric methods for longitudinal data; and studying the relation between long-term HIV dynamics and T-lymphocyte kinetics during long-term antiretroviral drug exposure; (b) investigating which clinical and specific factors affect HIV dynamics; (c) investigating the relation between clinical endpoints and HIV dynamics; and (d) comparing the effects of different antiviral agents, and presenting statistical evidence to justify which of several treatments is the most effective. The proposed research is expected to benefit studies of the immune system in HIV-infected patients (one of the main focuses of future AIDS research).

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Research Project (R01)
Project #
1R01AI062247-01
Application #
6841774
Study Section
AIDS Clinical Studies and Epidemiology Study Section (ACE)
Program Officer
Williams, Carolyn F
Project Start
2004-07-01
Project End
2005-07-31
Budget Start
2004-07-01
Budget End
2005-07-31
Support Year
1
Fiscal Year
2004
Total Cost
$187,500
Indirect Cost
Name
St. Jude Children's Research Hospital
Department
Type
DUNS #
067717892
City
Memphis
State
TN
Country
United States
Zip Code
38105
Liang, Hua; Miao, Hongyu; Wu, Hulin (2010) ESTIMATION OF CONSTANT AND TIME-VARYING DYNAMIC PARAMETERS OF HIV INFECTION IN A NONLINEAR DIFFERENTIAL EQUATION MODEL. Ann Appl Stat 4:460-483
Liang, Hua; Su, Haiyan; Thurston, Sally W et al. (2009) Empirical Likelihood based Inference for Additive Partial Linear Measurement Error Models. Stat Interface 36:433-443
Liang, Hua; Song, Weixing (2009) Improved Estimation in Multiple Linear Regression Models with Measurement Error and General Constraint. J Multivar Anal 100:726-741
Su, Haiyan; Qin, Yongsong; Liang, Hua (2009) Empirical Likelihood-Based Confidence Interval of ROC Curves. Stat Biopharm Res 1:407-414
Liang, Hua (2009) GENERALIZED PARTIALLY LINEAR MIXED-EFFECTS MODELS INCORPORATING MISMEASURED COVARIATES. Ann Inst Stat Math 61:27-46
Wang, Jiexun; Liang, Hua; Zou, Guohua (2009) Optimal 2-stage design with given power in association studies. Biostatistics 10:324-6
Yi, Grace Y; He, Wenqing; Liang, Hua (2009) Analysis of Correlated Binary Data under Partially Linear Single-Index Logistic Models. J Multivar Anal 100:278-290
Liang, Hua; Li, Runze (2009) Variable Selection for Partially Linear Models with Measurement Errors. J Am Stat Assoc 104:234-248
Yi, Grace Y; He, Wenqing; Liang, Hua (2009) SEMIPARAMETRIC MARGINAL AND ASSOCIATION REGRESSION METHODS FOR CLUSTERED BINARY DATA. Ann Inst Stat Math 100:278-290
Xue, Lan; Liang, Hua (2009) Polynomial Spline Estimation for A Generalized Additive Coefficient Model. Scand Stat Theory Appl 37:26-46

Showing the most recent 10 out of 30 publications