We propose to study the following statistical problems arising in cancer studies. The results from this research will be useful in analyzing survival data. Median and Quantile Regression Models for Censored Data We will develop efficient numerical aIgorithms to implement the methods proposed by Ying, Jung and Wei (1993) for the median and quantile regression models with censored data; study the case when censoring variables depend on covariates; generalize these methods to multivariate failure time data; explore quantile regression methods for highly clustered failure time observations; and study model diagnostic procedures for the quantile regression model with censored data. B. Semi-parametric Methods for the Accelerated Failure Time Model We will study a class of new estimation procedures which can be obtained through standard numerical methods; carefully examine their performance and compare them to the rank estimators proposed by Wei, Ying and Lin (1990); derive confidence bands for the survival function under the accelerated failure time (AFT) model for future patients with a specific set of covariates; and study model diagnostic methods for the AFT model. C. Estimating the Difference Between Two Survival Curves We will study nonparametric methods for constructing simultaneous confidence intervals of the difference between two survival functions and explore appropriate transformations of the Kaplan-Meier estimates to obtain accurate bands for small sample sizes. We will study nonparametric methods which provide a single statistic for estimating and summarizing the difference between two survival curves.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA056844-05
Application #
2097630
Study Section
Special Emphasis Panel (ZRG7-SSS-1 (01))
Project Start
1991-08-15
Project End
1997-07-31
Budget Start
1995-08-01
Budget End
1996-07-31
Support Year
5
Fiscal Year
1995
Total Cost
Indirect Cost
Name
Harvard University
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
082359691
City
Boston
State
MA
Country
United States
Zip Code
02115
Chuang, S K; Tian, L; Wei, L J et al. (2002) Predicting dental implant survival by use of the marginal approach of the semi-parametric survival methods for clustered observations. J Dent Res 81:851-5
Chuang, S K; Wei, L J; Douglass, C W et al. (2002) Risk factors for dental implant failure: a strategy for the analysis of clustered failure-time observations. J Dent Res 81:572-7
Gilbert, Peter B; Wei, L J; Kosorok, Michael R et al. (2002) Simultaneous inferences on the contrast of two hazard functions with censored observations. Biometrics 58:773-80
Chuang, S K; Tian, L; Wei, L J et al. (2001) Kaplan-Meier analysis of dental implant survival: a strategy for estimating survival with clustered observations. J Dent Res 80:2016-20
Xu, X; Palmer, L J; Horvath, S et al. (2001) Combining multiple phenotypic traits optimally for detecting linkage with sib-pair observations. Genet Epidemiol 21 Suppl 1:S479-83
Cheng, S C; Fine, J P; Wei, L J (1998) Prediction of cumulative incidence function under the proportional hazards model. Biometrics 54:219-28
Betensky, R A (1997) Conditional power calculations for early acceptance of H0 embedded in sequential tests. Stat Med 16:465-77
Betensky, R A (1997) Early stopping to accept H(o) based on conditional power: approximations and comparisons. Biometrics 53:794-806
Zackin, R; Wei, L J (1997) Analysis of repeated virological measurements based on cell dilution assays. Stat Med 16:571-82
Yao, Q; Wei, L J (1996) Play the winner for phase II/III clinical trials. Stat Med 15:2413-23;discussion 2455-8

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