Skip to main content

COVID-19 Resources

What people with cancer should know:

Guidance for cancer researchers:

Get the latest public health information from CDC:

Get the latest research information from NIH:

Grant Details

Grant Number: 5R01CA084079-04 Interpret this number
Primary Investigator: Feng, Ziding
Organization: Fred Hutchinson Can Res Ctr
Project Title: Statistical Methods for Community Intervention Trials
Fiscal Year: 2003


DESCRIPTION (Applicant's abstract): Group Randomized Trials (GRTs), in which groups rather than individuals are randomized into treatment conditions, are of central importance to community-based cancer prevention research, evidenced by the large number of GRTs conducted in the past decade. The overall goal of this proposal is to develop improved statistical evaluation methods for GRTS. The research carried out under this proposal consists of development and evaluation of analytical methods in GRTs including: 1) methods to make randomization-based inference more powerful for GRTS; 2) methods to evaluate trial results in the matched pair GRTs regardless of whether matching is effective or not; and 3) methods to make Generalized Linear Mixed Models more suitable for GRTS. The project will involve both theoretical and empirical work, drawing on data sources and collaborative opportunities provided by a large number of completed, and ongoing studies. 1). Theoretical work on randomzation-based inference will use a weighted permutation test and examine its properties, in particular the statistical power to detect an intervention effect. A weighted permutation-based confidence interval will be developed allowing for individual covariate adjustment. 2). Theoretical work on matched pair analysis will develop a test conditional on the observed correlation between matched conununities. The properties of this new method will be compared with traditional unconditional methods both analytically and via simulation. Such a test will allow investigators carrying out GRTs to use matching or blocking to control for factors potentially related to outcomes and yet recapture the power at the time of analysis if the matching or blocking is not effective. 3). Theoretical work on Generalized Linear Mixed Models will develop a new analysis method which is properly suited for GRTs where the number of survey subjects per community is usually large while the number of communities is usually small. The bias and efficiency of this method compared to competing procedures will be examined analytically and via simulation.


Re: Lung cancer risk among female textile workers exposed to endotoxin.
Authors: Astrakianakis G. , Seixas N.S. , Ray R. , Camp J.E. , Gao D.L. , Feng Z. , Li W. , Wernli K.J. , Fitzgibbons E.D. , Thomas D.B. , et al. .
Source: Journal of the National Cancer Institute, 2010-06-16; 102(12), p. 913-4.
EPub date: 2010-05-05.
PMID: 20445162
Related Citations

Reliability, effect size, and responsiveness of health status measures in the design of randomized and cluster-randomized trials.
Authors: Diehr P. , Chen L. , Patrick D. , Feng Z. , Yasui Y. .
Source: Contemporary clinical trials, 2005 Feb; 26(1), p. 45-58.
EPub date: 2005-01-27.
PMID: 15837452
Related Citations

Evaluation of community-intervention trials via generalized linear mixed models.
Authors: Yasui Y. , Feng Z. , Diehr P. , McLerran D. , Beresford S.A. , McCulloch C.E. .
Source: Biometrics, 2004 Dec; 60(4), p. 1043-52.
PMID: 15606425
Related Citations

Back to Top