Useful statistical ideas are drawn into biomedical practice from a wide
range of sources, including theoretical research and developments in
other application areas. The long-term purpose of this grant is to
speed the transfer of promising new statistical technology into health-
related applications. Five specific aims are proposed here, all of
which involve a combination of computer-intensive methodology with new
developments in statistical theory. Bootstrap and permutation methods
are suggested for some specific biomedical problems: in the analysis of
accuracy measurements for medical images, and also for chromosomal
ordering via radiation hybrids. More general problems of the types
encountered in biostatistical work are also under investigation: setting
accurate confidence intervals, assessing the accuracy of discrete
estimates such as phylogenetic trees, and making use of Bayesian ideas
in an objective manner. The investigation pursues these topics from
several vantage points, the Stanford statistics department, the medical
school, and the Human Genome project.
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