||5R44CA083512-03 Interpret this number
||Statistical Software for Data with Measurement Error
DESCRIPTION (provided by applicant): The ultimate objective of this research
application is the development of S+ME: a next generation software for handling
covariate measurement error. The developed methodologies are also applicable to
data with missing covariates. To achieve this, much of our research effort will
be devoted towards combinging algorithms in regression calibration, SIMEX and
iterative imputation methods into a software package. These methodologies will
be matured by incorporating diagnostic techniques, sensitivity analyses, and
graphical methods into the software package. In the Phase I feasibility study,
algorithms have been developed for univariate Gaussian and logistic regression.
These methods will be extended to covariate measurement error in modeling
correlated responses such as in longitudinal or spatial data.
The result of this research will make fundamental contributions to the conduct
of public health studies by developing mature methodology and user-friendly
software for handling missing or mismeasured covariates. Currently, there is
little or no commercial software in this area. The aim of the proposed research
is to overcome this deficiency and bring the benefits of measurement error
models to a wide audience of biomedical analysts and practitioners. The S+ME
module will be implemented as an object-oriented software in the S-Plus
language. A comprehensive case study guidebook will be developed involving real
problems in exposure and risk factors which inherently contain measurement
PROPOSED COMMERCIAL APPLICATION: NOT AVAILABLE
Structured correlation in models for clustered data.
Statistics in medicine, 2006-07-30; 25(14), p. 2450-68.