Statistical methodology is to be extended to accommodate unique statistical
problems that arise in several different biomedical studies involving the
estimation of within-cluster effects. Examples of such effects include
differences in response to chemical toxicity among groups of litters within
blocks, within-subject differences in treatment response among tumors in
different organs across time, within-subject differences in risk of
periodontal diseases among different regions of the dentition across time,
and within-subject treatment effects in a cross-over study of
bioequivalence. These examples arise in four areas of application: 1) a
developmental toxicity study of the synergistic effect of combinations of
possible carcinogens ; 2) a comparison of treatment responses of
metastatic tumors in different organs that originated from renal cell
carcinoma; 3) an observational study of temporal changes in the spatial
distribution of periodontal disease ina the dentition; and 4) an
investigation of the bioequivalence of different formulations of a drug
with respect to a discrete response.
These areas of research present a number of unresolved issues regarding
mixed effects discrete response models that are the focus of this proposal.
First, mixed effects models that accommodate multiple endpoints are
addressed. Three cases are to be examined: 1) multiple discrete
endpoints; 2) discrete and continuous endpoints; and 3) multiple discrete
endpoints with a discrete time failure response. For case 1), a hybrid of
random effects and marginal models is proposed. For case 2), an extension
of measurement error mixed effects models is considered. And for case 3),
a mixed effect ordinal response model conditional on a discrete time
failure response is discussed. Additional issues include adapting mixed
effects discrete response models to accommodate random cluster sizes (e.g.,
litter sizes and numbers of tumors for a given subject) and negative
intracluster correlations (e.g., strong litter mates benefiting at the
expense of weak litter mates). Finally, the analysis of bioequivalence
discrete response data has demonstrated differences in confidence-interval-
based inference between population-averaged and mixed effects logistic
regression models. It is proposed that other link functions be
investigated that lead to consistent confidence interval-based inference
for bioequivalence in this context.
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