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The most commonly missing covariate was income.
In the regression model the data item of the partly missing covariate functioned as the dependent variable and the corresponding binary data item (which indicated when the partly missing covariate was observed and when it was not) served as a missing dependent variable data item.
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Another form of missing covariate is when some individuals have a shared exposure that is unmeasured.
The bias and precision of the population parameter estimates, estimated using the different methods for handling the missing covariate, are presented in Tables I (RBias) and II (RSD).
Potential selection bias due to exclusion from final models of children with missing outcome data or missing covariates was addressed by comparing our results to GEE models that included stabilized inverse probability weights (Hernán et al. 2004).
The generalized semiparametric regression models for the cumulative incidence functions with missing covariates are investigated.
Sporadic missing covariates were replaced by the population mean (<5%).
These missing covariates are related to the study outcomes.
Patients with missing covariates were removed from the analysis.
These missing covariates were imputed with a multiple imputation model.
However, the number of missing covariates is a limitation as sample sizes for individual analyses were substantially reduced.
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CEO of Professional Science Editing for Scientists @ prosciediting.com