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We use unimputed data and assumed all missing variables were missing completely at random (MCAR) and confirmed this assumption by doing multiple imputations (using sequential regression multivariate imputation (SRMI) method) which produced similar results.
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For the validation in the TARN dataset, we did multiple imputations to substitute the missing values of the predictors included in the prognostic model by using the procedure of imputation by chained equations in Stata Release 11.
Before we did multiple imputations, we checked the distribution of every variable.
We did multiple imputation using the Markov chain Monte Carlo function (SPSS).
We handled missing data with mixed linear models and did not perform multiple imputations.
In HLM, the analysis of plausible values is done by multiple imputations.
Imputations were done by multiple imputation using the predictive mean matching method in SPSS 19.
This was done by multiple imputation of missing values with the ICE routine in Stata 11.1.
The imputation was done using multiple imputations by chained equations (MICE), an approach that uses all the variables in the models to impute the missing values [ 25].
The MICE (Multivariate Imputation via Chained Equations) package in R has been utilized to do the multiple imputations [59].
By doing so and performing multiple imputations for missing values, we tried to provide more solid data while avoiding a confirmatory statement.
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