This project will develop and investigate new methodology for analyzing data from clinical studies of cancer and other chronic diseases. The specific areas of concentration are as follows. l. Methodology for Analysis with Missing Data. In clinical studies it is common to have cases with incomplete data, both on covariates and on outcome measures. Standard complete case methods of analysis applied to such data are often not valid. Several specific areas will be investigated: (a) Joint estimation of the covariate distribution and the regression model in a full likelihood analysis with missing covariate data, with an emphasis on """"""""nonpararametric"""""""" models for the covariate distribution. (b) Extending methods for handling missing covariate data from model fitting to methods for exploratory analysis and data smoothing. (c) Examining parametric methods for inference with non-ignorable missing outcome data. 2. Time- Varying Covariates. Clinical studies with failure time endpoints often include collecting data on marker process which vary over time. Methods for exploratory analysis through nonparametric estimation of the relationship between such timevarying covariates and failure time will be developed. 3. Transformed Linear Survival Models. Transformed linear survival models provide a useful alternative to the proportional hazards model for regression analysis of failure time data. New methods will be developed for estimating optimal weight functions for the rank based estimating equations for these models. New methods for flexible estimation of regression functions including generalized additive modeling, will also be developed.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA057253-06
Application #
2458078
Study Section
Special Emphasis Panel (ZRG7-SSS-1 (19))
Program Officer
Patel, Appasaheb1 R
Project Start
1992-07-01
Project End
1999-07-31
Budget Start
1997-08-01
Budget End
1999-07-31
Support Year
6
Fiscal Year
1997
Total Cost
Indirect Cost
Name
Dana-Farber Cancer Institute
Department
Type
DUNS #
149617367
City
Boston
State
MA
Country
United States
Zip Code
02215
Gray, Robert J (2003) Weighted estimating equations for linear regression analysis of clustered failure time data. Lifetime Data Anal 9:123-38
Li, Yi; Betensky, Rebecca A; Louis, David N et al. (2002) The use of frailty hazard models for unrecognized heterogeneity that interacts with treatment: considerations of efficiency and power. Biometrics 58:232-6
Gray, Robert J; Li, Yi (2002) Optimal weight functions for marginal proportional hazards analysis of clustered failure time data. Lifetime Data Anal 8:5-19
Li, Yi; Ryan, Louise (2002) Modeling spatial survival data using semiparametric frailty models. Biometrics 58:287-97
Gray, R J (2000) Estimation of regression parameters and the hazard function in transformed linear survival models. Biometrics 56:571-6
Lipsitz, S R; Molenberghs, G; Fitzmaurice, G M et al. (2000) GEE with Gaussian estimation of the correlations when data are incomplete. Biometrics 56:528-36
Parzen, M; Lipsitz, S R (1999) A global goodness-of-fit statistic for Cox regression models. Biometrics 55:580-4
Lipsitz, S R; Ibrahim, J G; Fitzmaurice, G M (1999) Likelihood methods for incomplete longitudinal binary responses with incomplete categorical covariates. Biometrics 55:214-23
Lipsitz, S R; Dear, K B; Laird, N M et al. (1998) Tests for homogeneity of the risk difference when data are sparse. Biometrics 54:148-60
Gray, R J (1998) On tests for group variation with a small to moderate number of groups. Lifetime Data Anal 4:139-48

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