DESCRIPTION: (Adapted from the applicant's abstract) The past thirty
years have been a period of intense methodological development in
statistics, with ideas such as proportional likelihood, robust
estimation, jackknife/bootstrap methods, empirical Bayes techniques,
longitudinal estimation, and EM algorithm becoming important. The long
term purpose of this application is to shorten the transfer time
between promising theoretical/methodological innovations and their
biostatistical applications. This usually means linking the new methods
to established statistical theory, and making clear their data-
analytic advantages. the applicants' research is pursued from both
the Stanford statistics department and medical school. Four areas
are proposed here as focal points for forthcoming research: the use
of complicance data in the analysis of clinical trials; bootstrap
methods particularly as applied to bias-correction, metaanalysis,
prediction, and the construction of confidence ellipsoids; Monte Carlo
Markov chain estimators for the analysis of the type of contingency
tables arising form genetic marker data; and practical formulas for
simultaneous hypothesis testing, based on Hotelling's geometric
arguments. Some of this work will be carried out in collaboration
with biomedical researchers working on arthritis, AIDS,
cardiovascular disease, genetics research, and other diseases in which
compliance measurements are particularly useful.
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