DESCRIPTION: (Adapted from Investigator's Abstract) This renewal
application is requesting support to extend the findings of the first
funding cycle where the investigators described the impact of model
misspecification in linear regression to the second cycle where they will
focus on analyses with ordinal and categorical outcomes. Specifically, the
investigators propose to assess the impact of omitted (confounding)
covariates, the impact of measurement error and selection bias on parameters
estimated by logistic regression, GEE, and other categorical approaches.
With respect to the assessment of confounding, the investigators propose to
generalize the confounding structure of their earlier models and to extend
the linear results to GEE estimation. Topics to be addressed in measurement
error include extending the latent variables approaches used in the social
sciences to biostatistics, to assess the impact of misclassification in
binary and ordinal outcome measures, and to examine the impact of
distributional assumptions concerning confounders. Topics associated with
selection bias to be addressed include assessing the impact of informative
censoring on selected models and adjustments of modeling to sampling
approaches (such as oversampling). Finally, the procedures for detecting
model lack of fit will be developed. The application implicitly suggests
that the techniques developed will be applied to existing data sets.
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- The DCCPS Team.