DESCRIPTION (Applicant's abstract): The aims of this proposal are the
development, evaluation and application of methods for the statistical analysis
of longitudinal data, with emphasis on observational follow-up studies in
epidemiologic and other health related research. The specific focus is to
realistically model various departures from the assumptions of commonly applied
models, evaluate the effect, consider new estimation techniques that are robust
to the possible misspecification and develop methods for detecting the
misspecification. Misspecifications to be considered are omitted confounders,
measurement error and informative missing data. Special attention will be paid
to the relationships between these, and a unified framework will be introduced
whenever possible.
The proposed work includes:
- Fitting models with latent random effects geared to capture possible
misspecifications
- Developing simple methods for fitting models for informative missing data
- Adapting semiparametric random effects models for measurement error problems
- Undertaking a comprehensive investigation of the effect of confounding in
generalized linear models, and
- Developing of a pseudo score test to detect misspecification with GEE
The methods will be applied in the analysis of large amounts of longitudinal
data on sleep disorders, diabetes, falls among the elderly, neonatal lung
disease and eye disease in an aging population.
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- The DCCPS Team.