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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/4720

Title: Sensitivity analysis of incomplete categorical data
Authors: Kenward, Michael G.
Goetghebeur, Els J.T.
Molenberghs, Geert
Issue Date: 2001
Publisher: ARNOLD
Citation: Statistical modelling, 1(1). p. 31-48
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 ude to finite sampling and inference 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.
URI: http://hdl.handle.net/1942/4720
Link to publication: https://pdflegend.com/download/sensitivity-analysis-for-incomplete-categorical-data-_59f9dfaad64ab23ff07e88a7_pdf
DOI: 10.1177/1471082X0100100104
ISSN: 1471-082X
Category: A1
Type: Journal Contribution
Appears in Collections: Research publications

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