Abstract
Models for missing data are necessarily based on untestable assumptions whose effect on the conclusions are usually assessed via sensitivity analysis. To avoid the usual normality assumption and/or hard-to-interpret sensitivity parameters proposed by many authors for such purposes, we consider a simple approach for estimating means, standard deviations and correlations. We do not make distributional assumptions and adopt a pattern-mixture model parameterization which has easily interpreted sensitivity parameters. We use the so-called estimated ignorance and uncertainty intervals to summarize the results and illustrate the proposal with a practical example. We present results for both the univariate and the multivariate cases.
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Communicated by Domingo Morales.
The authors would like to thank the following institutions for financial support: Frederico Z. Poleto and Julio M. Singer, from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Brazil, Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), Brazil, and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Brazil; Geert Molenberghs, from the IAP research Network P6/03 of the Belgian Government (Belgian Science Policy); Carlos Daniel Paulino, from Fundação para a Ciência e Tecnologia (FCT) through the research centre CEAUL-FCUL, Portugal.
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Poleto, F.Z., Molenberghs, G., Paulino, C.D. et al. Sensitivity analysis for incomplete continuous data. TEST 20, 589–606 (2011). https://doi.org/10.1007/s11749-010-0219-x
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DOI: https://doi.org/10.1007/s11749-010-0219-x