Abstract

The main theme of this project is the investigation of methodology for the censored or missing data that commonly arises in clinical trials and cohort studies in cancer research. More specifically, we will investigate: 1. Methods for analyzing the cost or resource utilization in cancer studies with incomplete follow-up. 2. Adaptive inference based on families of either regression models or rank tests when a most efficient model cannot be specified in advance. 3. Models that adjust for missing data or partial information in either the cause of failure in right censored data or in longitudinally measured response data, such as in quality of life scores. 4. Group sequential designs for monitoring survival probabilities, as opposed to hazard ratios, in cancer clinical trials.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
2R01CA039929-13
Application #
2007507
Study Section
Special Emphasis Panel (ZRG7-STA (01))
Program Officer
Erickson, Burdette (BUD) W
Project Start
1984-09-01
Project End
2000-12-31
Budget Start
1997-01-08
Budget End
1997-12-31
Support Year
13
Fiscal Year
1997
Total Cost
Indirect Cost

  Institution

Name
Dana-Farber Cancer Institute
Department
Type
DUNS #
149617367
City
Boston
State
MA
Country
United States
Zip Code
02215

  Publications

Huang, Jie; Harrington, David (2005) Iterative partial least squares with right-censored data analysis: a comparison to other dimension reduction techniques. Biometrics 61:17-24
Peckova, Monika; Fleming, Thomas R (2003) Adaptive test for testing the difference in survival distributions. Lifetime Data Anal 9:223-38
Huang, Jie; Harrington, David (2002) Penalized partial likelihood regression for right-censored data with bootstrap selection of the penalty parameter. Biometrics 58:781-91
Xu, R; Harrington, D P (2001) A semiparametric estimate of treatment effects with censored data. Biometrics 57:875-85
Jones, C L; Harrington, D P (2001) Omnibus tests of the martingale assumption in the analysis of recurrent failure time data. Lifetime Data Anal 7:157-71
Xu, R; Adak, S (2001) Survival analysis with time-varying relative risks: a tree-based approach. Methods Inf Med 40:141-7
Bernardo, M V; Harrington, D P (2001) Sample size calculations for the two-sample problem using the multiplicative intensity model. Stat Med 20:557-79
Patricia Bernardo , M V; Ibrahim, J G (2000) Group sequential designs for cure rate models with early stopping in favour of the null hypothesis. Stat Med 19:3023-35
Lin, D Y; Psaty, B M; Kronmal, R A (1998) Assessing the sensitivity of regression results to unmeasured confounders in observational studies. Biometrics 54:948-63
Gray, R J (1998) On tests for group variation with a small to moderate number of groups. Lifetime Data Anal 4:139-48

Showing the most recent 10 out of 21 publications