The goal of this research project is to carry out an extensive
investigation of several areas of statistical methodology for the design
and analysis of biomedical studies, planned or observational, applicable
to various areas of health research including cancer, toxicology,
environmental health and epidemiology. The objective is to provide more
efficient statistical methods to achieve valid conclusions at less cost in
terms of time and sample size.
The research falls into six main categories: (a) Interim monitoring
of clinical trials: (b) Design and analysis of long-term animal
tumorigenicity bioassays; (c) Detection and surveillance of disease
clusters; (d) Performance measures for diagnostic tests; (e) Quality
control procedures for laboratory analyses; (f) Modeling and analysis of
multitype recurrent events in longitudinal studies.
Specific projects include the use of group sequential designs and
repeated confidence intervals in clinical trials with particular emphasis
on multiple endpoints and longitudinal data. An important advantage of
this approach is that its flexibility allows inferences to be drawn
independent from any stopping rule. It is planned to investigate the
design of efficient and robust interim sacrificing schedules in long-term
animal studies. Statistical methodology for the detection of clusters of
disease will be extended to account for heterogeneous populations and the
existence of multiple putative sources of hazard. Applications will be
made to geocoded cancer and suicide data. Nonparametric estimators of
performance measures of health or nutritional status will be studied using
asymptotic theory and applied to diabetes and anemia data from the
NHANES-II survey. Optimal use of controls, standards and blind replicate
samples will be investigated as part of a quality control system for
laboratory, analyses.
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