Cardiovascular Diseases Clinical Trial
To develop a new statistical index that measures sensitivity to non-ignorability (index of sensitivity to nonignorability, or ISNI) for model-based inferences.
BACKGROUND:
Despite a considerable number of recent developments, missing data and associated
methodology continues to be an important topic of research in biostatistics, medicine and
public health. As investigators begin to understand the limitations of model-based
inferences under the assumption of non-ignorable missingness, recent attention has turned to
the formulation and implementation of sensitivity analyses. Having a general-purpose index
to assess sensitivity to departures from ignorability would be extremely useful to
researchers in a variety of fields in the health sciences. This is especially true if the
index is relatively easy to compute and interpret.
DESIGN NARRATIVE:
It would be useful to have a general, easily computed diagnostic that characterizes data
sets with respect to their potential for sensitivity to nonignorability. The investigators
have developed a diagnostic that measures the effect of small perturbations from
ignorability on coefficient estimates in the univariate linear model with missing
observations.They will extend their analysis in a number of directions: i) They will develop
a general class of diagnostics for Bayes and direct- likelihood inferences, and demonstrate
its application to a number of important special cases. ii) They will develop an analogous
theory for sensitivity to nonignorability in frequentist estimation and testing. iii) They
will develop a general form of the diagnostic for the coarse-date model, a generalization of
missing data that includes censoring and rounding as special cases. iv) They will analyze a
number of real- world data sets that represent important cases where nonignorability is of
interest, including dropout in longitudinal data, censored survival data, and cross-over in
clinical trials.
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N/A
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