Weighted Generalized Estimating Functions for Longitudinal Response and Covariate Data That Are Missing at Random

Chen, Baojiang; Yi, Grace Y.; Cook, Richard J.

doi:10.1198/jasa.2010.tm08551

Cited by 49 publications

(61 citation statements)

References 39 publications

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“…It can be seen that, when the association between the missing data indicators increases, the asymptotic relative bias also increases. This finding is in line with Chen et al 9 The results corresponding to ¼ logð2Þ are presented. In this study, we assess the performance of the proposed method (MCRL) based on Gibbs sampler.…”

Section: Simulation Studysupporting

confidence: 93%

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

Rana

Roy

Das

2013

Stat Methods Med Res

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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.

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Section: Simulation Studysupporting

confidence: 93%

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

Rana

Roy

Das

2013

Stat Methods Med Res

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“…A future research is to extend this method to the missing outcome, missing surrogate process, or missing covariates problem. The idea is that we adopt a modified estimating equation as proposed in Chen, Yi, and Cook [28]. This is still under investigation.…”

Section: Summary and Discussionmentioning

confidence: 99%

A new estimation with minimum trace of asymptotic covariance matrix for incomplete longitudinal data with a surrogate process

Chen

Qin

2013

Statistics in Medicine

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Missing data is a very common problem in medical and social studies, especially when data are collected longitudinally. It is a challenging problem to utilize observed data effectively. Many papers on missing data problems can be found in statistical literature. It is well known that the inverse weighted estimation is neither efficient nor robust. On the other hand, the doubly robust (DR) method can improve the efficiency and robustness. As is known, the DR estimation requires a missing data model (i.e., a model for the probability that data are observed) and a working regression model (i.e., a model for the outcome variable given covariates and surrogate variables). Because the DR estimating function has mean zero for any parameters in the working regression model when the missing data model is correctly specified, in this paper, we derive a formula for the estimator of the parameters of the working regression model that yields the optimally efficient estimator of the marginal mean model (the parameters of interest) when the missing data model is correctly specified. Furthermore, the proposed method also inherits the DR property. Simulation studies demonstrate the greater efficiency of the proposed method compared with the standard DR method. A longitudinal dementia data set is used for illustration.

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“…Sometimes, the response variable and the covariates are missing. A comprehensive development on this problem has been given in Chen et al (2010a) for longitudinal data with marginal mean regression model. This method can be extended to the case of clustered data as demonstrated in Section 6.…”

Section: Discussionmentioning

confidence: 99%

“…As Chen et al (2010a,b) and Chen and Zhou (2011) considered, misspecification of the missing data model normally produces biased estimates for the mean parameters β . Here we focus investigating the impact of model misspecification on estimation of the association parameters.…”

Section: Empirical Studiesmentioning

confidence: 99%

Marginal methods for clustered longitudinal binary data with incomplete covariates

Chen

Cook

et al. 2012

Journal of Statistical Planning and Inference

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Many analyses for incomplete longitudinal data are directed to examining the impact of covariates on the marginal mean responses. We consider the setting in which longitudinal responses are collected from individuals nested within clusters. We discuss methods for assessing covariate effects on the mean and association parameters when covariates are incompletely observed. Weighted first and second order estimating equations are constructed to obtain consistent estimates of mean and association parameters when covariates are missing at random. Empirical studies demonstrate that estimators from the proposed method have negligible finite sample biases in moderate samples. An application to the National Alzheimer’s Coordinating Center (NACC) Uniform Data Set (UDS) demonstrates the utility of the proposed method.

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Weighted Generalized Estimating Functions for Longitudinal Response and Covariate Data That Are Missing at Random

Cited by 49 publications

References 39 publications

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

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

A new estimation with minimum trace of asymptotic covariance matrix for incomplete longitudinal data with a surrogate process

Marginal methods for clustered longitudinal binary data with incomplete covariates

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