|
ABSTRACT Qian-Li Xue, Johns Hopkins University Department of Biostatistics This work was motivated by the need to combine outcome information from
a
reference population with risk factor information from a screened
sub-population in a setting where the analytic goal was to study the
association between risk factors and multiple binary outcomes. This talk
consists of two parts: part I introduces a two-stage latent class
procedure that first summarizes the commonalities among outcomes using a
reference population sample, then analyzes the association between
outcomes and risk factors while accounting for the fact that screening may
alter the structure of item associations. It develops an approach to
estimating model parameters that is similar to pseudo maximum likelihood.
Part II presents an alternative approach by formulating the analysis as a
missing covariates problem under the assumption of missing at random. The
performance of the proposed methods are compared through a simulation
study and in an illustrative analysis of data from the Women's Health and
Aging Study, a recent investigation of the causes and course of disability
in older women. Combining information in the proposed ways is found to
improve both accuracy and precision in summarizing multiple categorical
outcomes, which effectively diminishes ambiguity and bias in making risk
factor inferences.
Return to Longitudinal/Survival Working Group List | Return to Home Page |