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In the base case analysis missing data were imputed by multiple imputation using chained equations (see web appendix) [ 11].
The 7% of data on baseline characteristics, resource use and quality of life that were missing were imputed by multiple imputation using the ice command 36 38 (V.1.3.0) within Stata V.11.0, thereby avoiding the bias and inefficiency associated with complete case analysis.
We filled missing survival times in these patients by multiple imputation, using estimates of mortality from studies that traced patients lost to follow-up.
Imputations were done by multiple imputation using the predictive mean matching method in SPSS 19.
Missing data will be handled by multiple imputation using regression analysis with the Markov-chain Monte Carlo (MCMC) estimation method.
Analysis on the data sets we generated by multiple imputation using chained equations shows persistent positive associations between negative behaviours and poor mental health.
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Missing data for the primary endpoint were treated by multiple imputations, using a regression-switching approach (chained equation with m = 20 imputations obtained with the R statistical software version 3.03).
Remaining missing values were replaced by multiple imputations using the expected maximum function in PRELIS.
In secondary analyses, missing values will be replaced by multiple imputations using Markoff Chain Monte Carlo methods [ 78].
Missing ages when aid walking or wheelchair use where needed were estimated by multiple imputations using the Markov Chain Monte Carlo method.
For covariates with missing values, we obtained values by multiple imputations using the MICE package as recommended for the Cox proportional hazard model analysis [ 27].
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