An index of local sensitivity to non-ignorability for multivariate longitudinal mixed data with potential non-random dropout

Statistics in Medicine
S Eftekhari Mahabadi, M Ganjali

Abstract

Multivariate longitudinal data with mixed continuous and discrete responses with the possibility of non-ignorable missingness are often common in follow-up medical studies and their analysis needs to be developed. Standard methods of analysis based on the strong and the unverifiable assumption of missing at random (MAR) mechanism could be highly misleading. A way out of this problem is to start with methods that simultaneously allow modelling non-ignorable mechanism, which includes somehow troubling computations that are often time consuming, then we can use a sensitivity analysis, in which one estimates models under a range of assumptions about non-ignorability parameters to study the impact of these parameters on key inferences. A general index of sensitivity to non-ignorability (ISNI) to measure sensitivity of key inferences in a neighborhood of MAR model without fitting a complicated not MAR (NMAR) model for univariate generalized linear models and for models used for univariate longitudinal normal and non-Gaussian data with potentially NMAR dropout are well presented in the literature. In this paper we extend ISNI methodology to analyze multivariate longitudinal mixed data subject to non-ignorable dropout in which the non-...Continue Reading

References

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May 24, 2005·Statistics in Medicine·Guoguang MaDaniel F Heitjan
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Citations

Jun 7, 2018·Statistics in Medicine·Samaneh Eftekhari Mahabadi

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