Thursday, July 17, 2025 7/17/2025

EFFICIENT ESTIMATION METHODS FOR CENSORED SURVIVAL DATA

Award Number: R15CA103845
ORGANIZATION: NATIONAL CANCER INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 04/01/2004
PERIOD OF PERFORMANCE END DATE: 03/31/2007

Group Awards By:

View Award Description

EFFICIENT ESTIMATION METHODS FOR CENSORED SURVIVAL DATA - DESCRIPTION (provided by applicant): The broad objectives of this research are the development of efficient methods of estimation in non- and semi parametric models for the analysis of censored survival data commonly present in biomedical studies. Although considerable progress has been made toward fitting such models to censored data, satisfactory solutions to important issues are still lacking. For example, a simple procedure is possible for estimating a survival function in the missing censoring indicator (MCI) model of random censorship, which has not been investigated and implemented so far. Furthermore some recently proposed semiparametric median and survival-rate regression models have not been explored for analyzing doubly censored data. In fact the utility of such regression models for the analysis of the relatively simpler right-censored data has been studied only for the special discrete covariate setting or for the special cases when the censoring or the observation error is independent of the covariate. The research objectives of this proposal are (A) Develop a new, simple-to-calculate, and asymptotically efficient nonparametric estimator of a survival function in the MCI model. (B) Initiate a methodology for fitting median regression models to analyze doubly censored data. (C) Develop for the case of continuous covariates the missing information principle (MIP) and the inverse censoring weighted (ICW) approaches of fitting right-censored (i) median regression models and (ii) survival-rate regression models. The research will focus on flexible and robust estimation methods, resulting in estimators that will be asymptotically efficient or will perform as well as or better than currently existing ones. Large sample properties of the estimators will be studied using nonparametric kernel estimation techniques, U-statistic theory, and other related large sample theory used in statistics and probability. Optimal bandwidths for computing the various estimators will be obtained by employing smoothed and other bootstrap ideas proposed in the literature. Preliminary theoretical and numerical studies justify the promise of the proposed approaches to have definite efficiency and robustness advantages over competing estimators. More extensive simulation studies will be carried out to study the operating characteristics of the proposed estimators. The project will illustrate the practical usefulness of the procedures by analyzing publicly available real data sets obtained from cancer and other health studies. The proposed research will extend significantly the current state of knowledge in the fitting of non- and semiparametric models to censored data. The software developed for the project will be made available for public use on a web site created specially for this purpose.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2004 ( Subtotal = $218,150 )
2004	2004	UNIVERSITY OF MAINE, PORTLAND-GORHAM	96 FALMOUTH STREET	PORTLAND	ME	04103	CUMBERLAND	USA	Cancer Treatment Research	000	1	3/19/2004	NEW	$218,150
														Subtotal = $218,150

Grand Total All Awards = $218,150

Top

All Categories

About

Search

Reports

Data Submission

Award Information

EFFICIENT ESTIMATION METHODS FOR CENSORED SURVIVAL DATA

Award Number: R15CA103845

ORGANIZATION: NATIONAL CANCER INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 04/01/2004

PERIOD OF PERFORMANCE END DATE: 03/31/2007

Federal Websites

Department of Health & Human Services

HHS Operating Divisions

HHS Staff Divisions

Download A Document Viewer