In a clinical or observational study for HIV-related diseases, the response variable (e.g., the time to a certain clinical or lab based event, the level of a specific viral or immunological marker et al.) may be censored or truncated. We propose to investigate the following three general, closely related censored regression problems, which we encounter frequently in HIV/AIDS research. 1. Inference procedures for nonproportional hazards models for right censored data. We will develop new inference procedures for semi-parametric accelerated failure time (AFT) and quantile models. We will also study the general AFT and quantile regression with the Box-Cox-type of transformation for the failure time. 2. Model checking and evaluation for censored failure time data. We will develop model diagnostic tools for AFT, quantile and linear transformation models with censored data based on martingale residuals for checking various aspects of modeling assumptions. 3. Censored data regression with high-dimensional covariates. We will study methods for reducing mean squared error of estimation when fitting exploratory regression models with right censored data when there are large numbers of covariates, when the covariates are highly correlated, or the number of failures is smaller than commonly thought prudent. Our methods will generalize principal component regression to the proportional hazards model and partial least squares and principal components regression to AFT models for fight censored data.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Research Project (R01)
Project #
5R01AI052817-02
Application #
6666971
Study Section
AIDS and Related Research 8 (AARR)
Program Officer
Gezmu, Misrak
Project Start
2002-09-30
Project End
2005-03-31
Budget Start
2003-04-01
Budget End
2004-03-31
Support Year
2
Fiscal Year
2003
Total Cost
$283,500
Indirect Cost
Name
Harvard University
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
149617367
City
Boston
State
MA
Country
United States
Zip Code
02115
Claggett, Brian; Tian, Lu; Castagno, Davide et al. (2015) Treatment selections using risk-benefit profiles based on data from comparative randomized clinical trials with multiple endpoints. Biostatistics 16:60-72
Shen, Yuanyuan; Liao, Katherine P; Cai, Tianxi (2015) Sparse kernel machine regression for ordinal outcomes. Biometrics 71:63-70
Tian, Lu; Zhao, Lihui; Wei, L J (2014) Predicting the restricted mean event time with the subject's baseline covariates in survival analysis. Biostatistics 15:222-33
Uno, Hajime; Claggett, Brian; Tian, Lu et al. (2014) Moving beyond the hazard ratio in quantifying the between-group difference in survival analysis. J Clin Oncol 32:2380-5
Matsouaka, Roland A; Li, Junlong; Cai, Tianxi (2014) Evaluating marker-guided treatment selection strategies. Biometrics 70:489-499
Parast, Layla; Tian, Lu; Cai, Tianxi (2014) Landmark Estimation of Survival and Treatment Effect in a Randomized Clinical Trial. J Am Stat Assoc 109:384-394
Zhao, Lihui; Tian, Lu; Cai, Tianxi et al. (2013) EFFECTIVELY SELECTING A TARGET POPULATION FOR A FUTURE COMPARATIVE STUDY. J Am Stat Assoc 108:527-539
Zhao, Lihui; Hu, X Joan (2013) Estimation with Right-Censored Observations Under A Semi-Markov Model. Can J Stat 41:237-256
Zhou, Qian M; Zheng, Yingye; Cai, Tianxi (2013) Subgroup specific incremental value of new markers for risk prediction. Lifetime Data Anal 19:142-69
Parast, Layla; Cai, Tianxi (2013) Landmark risk prediction of residual life for breast cancer survival. Stat Med 32:3459-71

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