||5R01CA084079-04 Interpret this number
||Fred Hutchinson Can Res Ctr
||Statistical Methods for Community Intervention Trials
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.
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