Statistical Modelling 1 (2001), 3148
Sensitivity Analysis for Incomplete Categorical Data
Michael G. Kenward
London School of Hygiene and Tropical Medicine,
UK,
eMail: mike.kenward@lshtm.ac.uk
Els J.T. Goetghebeur
Department of Applied Mathematics and Information
Sciences, Universiteit Gent, Ghent, Belgium;
eMail:
els.goetghebeur@rug.ac.be
Geert Molenberghs
Biostatistics, Limburgs Universitair Centrum, B3590
Diepenbeek,
eMail:
geert.molenberghs@luc.ac.be
Abstract:
Classical inferential
procedures induce conclusions from a set of data to a
population of interest, accounting for the imprecision resulting from the
stochastic component of the model. This is usually done by means of precision
or interval estimates. Less attention is devoted to the uncertainty arising
from (unplanned) incompleteness in the data, even though the majority of
clinical studies suffer from incomplete follow-up. Through the choice of an
identifiable model for non-ignorable non-response, one narrows the possible
data generating mechanisms to the point where inference only suffers from
imprecision. Some proposals have been made for assessment of sensitivity to
these modelling assumptions; many are based on fitting several plausible but
competing models. We propose a formal approach which identifies and
incorporates both sources of uncertainty in inference:
imprecision due to finite sampling and ignorance due to
incompleteness. The developments focus on contingency tables, and are
illustrated using data from a HIV prevalence study and data from a psychiatric
study.
Keywords:
Contingency Table; Missing At Random; Overspecified Model; Saturated Model
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