The idea of informatively missing data, or informative dropout, is that
the chance an observation is missing is related to its actual value.
Examples of informatively missing data occur throughout Biostatistics,
e.g., in cancer clinical trials with noncompliance, in longitudinal
studies of numerical outcomes where the values of the numerical outcomes
related to cancer and AIDS where the values of the numerical outcomes
influence survival, etc. As the compliance example indicates, the field
of informative missingness is closely linked to issues to causality.
Informative missingness distorts standard analyses, and new approaches
to statistical inference are required.
There are three broad approaches to statistical inference in the
presence of informatively missing data: (a) latent variable models; (b)
pattern mixture models; and (c) selection models. In the pattern
mixture model approach, separate models are fit for each pattern of
missing data, and then through assumptions and/or sensitivity analysis,
the disparate models are combined. In selection models and latent
variable models, the missing data (or selection) mechanism is modeled
directly in terms of the unobservables or in terms of latent variables.
Pattern mixture models, latent variable models and selection models make
assumptions that are not directly verifiable from data, and so
sensitivity analysis is the norm.
The purpose of this conference is to bring together some of the leading
researchers in the area to present the newest statistical techniques.
We plan a limited number of talks over a two-day span, with ample time
for discussion and contrast of the methods.
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