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Journal ArticleDOI

On analyzing ordinal data when responses and covariates are both missing at random.

01 Aug 2016-Statistical Methods in Medical Research (SAGE Publications)-Vol. 25, Iss: 4, pp 1564-1578
TL;DR: A joint model is developed to take into account simultaneously the association between the ordinal response variable and covariates and also that between the missing data indicators to account for both missing responses and missing covariates.
Abstract: In many occasions, particularly in biomedical studies, data are unavailable for some responses and covariates. This leads to biased inference in the analysis when a substantial proportion of responses or a covariate or both are missing. Except a few situations, methods for missing data have earlier been considered either for missing response or for missing covariates, but comparatively little attention has been directed to account for both missing responses and missing covariates, which is partly attributable to complexity in modeling and computation. This seems to be important as the precise impact of substantial missing data depends on the association between two missing data processes as well. The real difficulty arises when the responses are ordinal by nature. We develop a joint model to take into account simultaneously the association between the ordinal response variable and covariates and also that between the missing data indicators. Such a complex model has been analyzed here by using the Markov chain Monte Carlo approach and also by the Monte Carlo relative likelihood approach. Their performance on estimating the model parameters in finite samples have been looked into. We illustrate the application of these two methods using data from an orthodontic study. Analysis of such data provides some interesting information on human habit.
Topics: Missing data (73%), Imputation (statistics) (67%), Ordinal data (57%), Markov chain Monte Carlo (53%), Covariate (52%)
Citations
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Journal ArticleDOI
01 Mar 1989-The Statistician

3,151 citations



References
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Book
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Journal ArticleDOI
01 Dec 1976-Biometrika
Abstract: Two results are presented concerning inference when data may be missing. First, ignoring the process that causes missing data when making sampling distribution inferences about the parameter of the data, θ, is generally appropriate if and only if the missing data are “missing at random” and the observed data are “observed at random,” and then such inferences are generally conditional on the observed pattern of missing data. Second, ignoring the process that causes missing data when making Bayesian inferences about θ is generally appropriate if and only if the missing data are missing at random and the parameter of the missing data is “independent” of θ. Examples and discussion indicating the implications of these results are included.

7,180 citations


Journal ArticleDOI

5,927 citations


"On analyzing ordinal data when resp..." refers background in this paper

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Journal ArticleDOI
01 Sep 1988-Biometrics

4,003 citations


Performance
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No. of citations received by the Paper in previous years
YearCitations
20201
19891