Journal ArticleDOI
On analyzing ordinal data when responses and covariates are both missing at random.
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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.read more
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Journal ArticleDOI
A Weighted Estimating Equation for Missing Covariate Data with Properties Similar to Maximum Likelihood
TL;DR: In this article, the authors propose a weighted estimating equation that is almost identical to the maximum likelihood estimating equations. But the weighted estimating equations are a special case of those proposed earlier by Robins et al., their EM-type algorithm to solve them is new.
Journal ArticleDOI
Using auxiliary data for parameter estimation with non-ignorably missing outcomes
TL;DR: In this paper, the authors proposed a method for estimating parameters in generalized linear models when the outcome variable is missing for some subjects and the missing data mechanism is non-ignorable, without having to specify a nonignorable model.
Reference EntryDOI
Structural Equation Modeling: Categorical Variables
TL;DR: In the behavioral sciences, response variables are often noncontinuous, common types being dichotomous, ordinal or nominal variables, counts and durations as mentioned in this paper, and conventional structural equation models (SEMs) have thus been generalized to accommodate different kinds of responses.
Journal ArticleDOI
Weighted generalized estimating functions for longitudinal response and covariate data that are missing at random
TL;DR: Inverse probability-weighted generalized estimating equations have been developed for a particular model generating intermittently missing-at-random data in this paper, where the association between the missing data indicators for these two processes is taken into account through joint models.
OtherDOI
Structural Equation Modeling: Categorical Variables
TL;DR: In the behavioral sciences, response variables are often noncontinuous, common types being dichotomous, ordinal or nominal variables, counts and durations as discussed by the authors, and conventional structural equation models have thus been generalized to accommodate different kinds of responses.