This project addresses statistical problems generated from collaboration with scientists in other program areas and general statistical problems of current interest. This project is a continuing activity of the Section on Mathematical Statistics and other members of the Branch. Papers have been submitted, are in review or were published in FY 1993 on the following statistical subjects: eigenvalue decompositions of data modeled by multiway arrays; validation methods for screening instruments in surveys of low prevalence disease; modeling time series for count data from a relapsing-remitting disease; modeling seasonal change in time series regression relationships; and national prevalence estimates of disease obtained by adjustment and incorporation of estimates from independent community-based surveys. Other work in progress includes: methods to improve coverage in surveys; estimation of time-to-event data with interval censoring; site selection for epidemiologic surveys; analysis of response surface data with spatial and temporal components; modeling of response surfaces with spatially correlated errors; application of splines to estimate model parameters of multiple correlated response surfaces; modeling effect changes of covariates in the presence of spatial correlation; combining information from negatively correlated nonlinear regressions; development of a generalized estimating equation approach for the analysis of spatially dependent binary data; application of bootstrap methods to longitudinal natural history data for the design and analysis of therapeutic trials for relapsing-remitting disease; use of variance component methods to assess the precision of biochemical measurements; using Markov chain model to study three state disease processes; and sampling strategies for spatial point processes with multiple types of clustering.
