Sentence examples for missing covariates and from inspiring English sources

I include indicators for missing covariates and assign missing observations the sample mean (continuous) or mode (binary) in the regression models.

This is a special issue, as it implies imputation of the missing covariates and outcomes.

We used the Markov Chain Monte Carlo method to impute missing covariates and the predictive mean matching method to impute missing values in the EQ-5D index [ 29].

Another less intuitive approach is to treat the outcome as a missing variable along with the missing covariates and simultaneously impute.

Although we expect missing values to be minimal, we will report them and perform appropriate multiple imputation and censoring if we find missing covariates and outcome values.

It is thus apparent that no method of dealing with missing data is appropriate under all conditions, and the best approach will depend on the degree of data missingness and the structure of the underlying missing data – correlations of missing covariate and outcome data.

Missing covariate and outcome data will be examined.

We used multiple imputations by chained equations to deal with missing covariate and biomarker values.

The main sample for un-weighted analysis comprised 1 634 853 observations (21.5% with physical disability) from 8380 practices (146 124 eligible observations excluded due to missing covariate and outcome data).

Because missing data would cause a risk of selection bias as well as loss of power, missing covariate and outcome data were imputed using chained multiple imputation, generating 100 complete data sets (Donders et al. 2006; Sterne et al. 2009).

It is possible to extend the MI method presented in this paper by adding extra regression models, one for each partly missing covariate, and include all the established relationships in the imputation model.