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4 results on '"Ivy Jansen"'

1. A flexible marginal modelling strategy for non-monotone missing data

Author

Ivy Jansen and Geert Molenberghs

Subjects
Statistics and Probability, Contingency table, Economics and Econometrics, Multivariate statistics, Multivariate analysis, Computer science, contingency table, global influence, Multivariate Dale model, non-randommissingness, Context (language use), Missing data, Domain (software engineering), Econometrics, Sensitivity (control systems), Statistics, Probability and Uncertainty, Marginal distribution, Social Sciences (miscellaneous)
Abstract

Much research has been devoted to modelling strategies for longitudinal data with missingness, recently especially within the missingness not at random context. In this paper, the relatively unexplored but practically highly relevant domain of non-monotone missingness with multivariate ordinal responses is broached. For this, a dedicated version of the multivariate Dale model is formulated. Furthermore, we also assess the sensitivity of these models to their assumptions, by using the technique of global influence. Ivy Jansen and Geert Molenberghs gratefully acknowledge support from Fonds Wetenschappelijk Onderzoek-Vlaanderen research project G.0002.98 ‘Sensitivity analysis for incomplete and coarse data’ and from Belgian Interuniversity Attraction Pole network ‘Statistical techniquesand modeling for complex substantive questions with complex data.

Published
2008

2. The nature of sensitivity in monotone missing not at random models

Author

Geert Molenberghs, Niel Hens, Geert Verbeke, Ivy Jansen, Michael G. Kenward, and Marc Aerts

Subjects
Statistics and Probability, Mixed model, Applied Mathematics, ignorability, likelihood ratio test, linear mixed model, local influence, missing at random, missing not at random, sensitivity analysis, Missing data, Ignorability, Computational Mathematics, Computational Theory and Mathematics, Likelihood-ratio test, Statistics, Econometrics, Test statistic, Truncation (statistics), Categorical variable, Statistic, Mathematics
Abstract

Models for incomplete longitudinal data under missingness not at random have gained some popularity. At the same time, cautionary remarks have been issued regarding their sensitivity to often unverifiable modeling assumptions. Consequently, there is evidence for a shift towards using ignorable methodology, supplemented with sensitivity analyses to explore the impact of potential deviations of this assumption in the direction of missingness at random. One such tool is local influence. It is shown that local influence tends to pick up a lot of different anomalies in the data at hand, not just deviations in the MNAR mechanism. This particular behavior is described and insight offered in terms of the non-standard behavior of the likelihood ratio test statistic for MAR missingness versus MNAR missingness within a model of the Diggle and Kenward type. (c) 2004 Elsevier B.V. All rights reserved. vy Jansen, Niel Hens, Geert Molenberghs and Marc Aerts gratefully acknowledge support from Fonds Wetenschappelijk Onderzoek-Vlaanderen Research Project G.0002.98 “Sensitivity Analysis for Incomplete and Coarse Data” and from Belgian IUAP/PAI network “Statistical Techniques and Modeling for Complex Substantive Questions with Complex Data”.

Published
2006

3. Missing data

Author

Geert Molenberghs, Caroline Beunckens, Ivy Jansen, Herbert Thijs, Geert Verbeke, and Michael G. Kenward

Published
2004

4. Marginalizing pattern-mixture models for categorical data subject to monotone missingness.

Author

Caroline Beunckens, Geert Molenberghs, Ivy Jansen, and Geert Verbeke

Subjects
ELECTRONIC circuit design, STATISTICS, MATHEMATICAL models, REASONING, ESTIMATION theory
Abstract

Abstract  Many models to analyze incomplete data that allow the missingness to be non-random have been developed. Since such models necessarily rely on unverifiable assumptions, considerable research nowadays is devoted to assess the sensitivity of resulting inferences. A popular sensitivity route, next to local influence (Cook in J Roy Stat Soc Ser B 2:133–169, 1986; Jansen et al. in Biometrics 59:410–419, 2003) and so-called intervals of ignorance (Molenberghs et al. in Appl Stat 50:15–29, 2001), is based on contrasting more conventional selection models with members from the pattern-mixture model family. In the first family, the outcome of interest is modeled directly, while in the second family the natural parameter describes the measurement process, conditional on the missingness pattern. This implies that a direct comparison ought not to be done in terms of parameter estimates, but rather should pass by marginalizing the pattern-mixture model over the patterns. While this is relatively straightforward for linear models, the picture is less clear for the nevertheless important setting of categorical outcomes, since models ordinarily exhibit a certain amount of non-linearity. Following ideas laid out in Jansen and Molenberghs (Pattern-mixture models for categorical outcomes with non-monotone missingness. Submitted for publication, 2007), we offer ways to marginalize pattern-mixture-model-based parameter estimates, and supplement these with asymptotic variance formulas. The modeling context is provided by the multivariate Dale model. The performance of the method and its usefulness for sensitivity analysis is scrutinized using simulations. [ABSTRACT FROM AUTHOR]

Published
2009

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