DESCRIPTION (provided by applicant): The broad objective of this research is to
develop new statistical methods for several important and timely problems that
arise in cancer and HIV/AIDS clinical research. Major efforts will be directed
toward (1) lifetime medical cost analysis with incomplete follow-up data and
(2) covariate measurement error in logistic and Cox regressions. Current
development in these two areas is inadequate and substantial gaps of knowledge
exist. In medical research cost evaluation is becoming an important component
and has been integrated in many studies, as our health care system is
increasingly constrained with limited resources. However, statistical methods
for lifetime medical cost analysis with incomplete follow-up data have largely
been lacking. This work will focus on developing semipararametric tests and
regressions, which accommodate right-censored data. The second area of research
concerns the situation that regression covariates are not accurately
ascertainable, e.g. dietary intakes in cancer prevention studies or CD4
lymphocyte count and viral load in HIV/AIDS research. Parametric- and
nonparametric-correction methods will be developed for widely-applied logistic
and Cox regressions with various scenarios of available data. Despite the
challenges of these long-standing problems, preliminary investigations have
shown considerable promise for elegant and practical solutions. Large-sample
properties of the proposed test statistics and estimators will be rigorously
investigated by making use of marked point process theory, martingale theory,
and modern empirical process theory. Extensive simulation studies will be
performed to validate these proposals under practical sample sizes. The
proposed methods will be applied to a number of cancer and HIV/AIDS clinical
trials. User-friendly computer programs will be developed and made available to
the research community.
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