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We modelled missing data for each categorical variable as a dummy term to avoid the loss of data and to permit direct comparison of each model using the deviance statistic.
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This method of modeling missing data assumes that the data are missing at random and permits inclusion of all available cases, although it is not as efficient as multiple imputation procedures.
Other methods include for example modelling missing data, scenario analysis or sensitivity analysis [ 21].
In order to model missing data, information about three of the variables listed is needed.
The program has the ability to model missing data in the dependent variable under certain conditions.
The common solution – modeling missing data, or extrapolating data of one country to another country – yields epistemic uncertainty.
E-M algorithm is an efficient and flexible tool for modeling missing data in the studied setting.
To avoid excluding patients with missing factors in regression models, missing data were imputed using chained equation methods with 20 imputations[19].
Our results contribute to the literature on non-classical measurement error models, missing data and treatment effects.
processes, simultaneous inference, efficient estimation for semiparametric models, missing data, counting processes, image analysis, and spatial point processes.
Subsequently, mixed modeling has become a major area of statistical research, including work on the computation of maximum likelihood estimates, nonlinear mixed effect models, missing data in mixed effects models, and Bayesian estimation of mixed effects models (West et al. 2007).
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