||7R03CA094750-03 Interpret this number
||University Of Arizona
||Development of a Model for Causal Latent Factors
DESCRIPTION (provided by applicant):
This R03 application proposes development of a novel method of causal analysis
for cancer research studies. A particular structural description is posed for
data outcomes that fall into a 2 exponent n table, of which a gene micro-array
is an example. Although this model captures causal pathways, the problem is
that it is not obvious how to compute model probabilities from the structural
parameters. The first aim of this project is to discover an efficient,
scalable algorithm for doing this computation. This will make it possible to
perform maximum likelihood inference for the latent causal factor parameters.
The second aim is to produce programs that will perform this analysis. The
third aim is to apply the method to two existing data sets, and to other data
sets that will be acquired. The existing data sets include a unique
cytogenetic series of chromosome abnormalities in ovarian, breast, and
melanoma tumors, with follow-up data on mortality. The second is from a
successful 5-a-Day project to increase intake of fruits and vegetables, with
extensive data on personal and environmental characteristics. Both of these
were NCI-funded studies. Part of the third aim will be to identify and acquire
cancer epidemiology data sets in order to re-analyze them from the perspective
of the causal latent factor model, using the software developed in the second
This project will potentially advance cancer epidemiology by developing and
popularizing a model for statistical analysis that respects the structure of
causal pathways that are thought to underlie many processes in carcinogenesis.
If successful it would also provide a new method for data-mining huge data
sets to search for latent causal factors.
Linksets of tumor chromosome breakpoints related to survival in ovarian adenocarcinoma.
, Taetle R.M.
Cancer genetics and cytogenetics, 2006-04-01; 166(1), p. 22-6.