In our data set, the results obtained by multiple imputation appeared to be better and more realistic than those obtained using the net survival rate.
On our basis of prostate cancer data, the results obtained by cause-specific survival after multiple imputation appeared to be better and more realistic than those obtained using net survival.
For our data set, it appeared that multiple imputation lead to a relatively large spread in inclusion frequency, which is a nice property that eases decisions about which variables to include.
First, we used multiple imputation to impute stage.
Multiple imputation is used to impute country of birth from the SIPP into the SCF.
This was imputed using a multivariate multiple imputation procedure with 10 imputations.
Multiple imputation methods have been used previously for missing ALSPAC data but do not appear to alter association patterns (42).
Multiple imputation using chained equations was used to impute missing covariates; five imputations were generated.
Tables 4 and 5 suggest that the multiple imputation and mixed model approaches had comparable power, except in cases with substantial missing data (RCT 9 & RCT 10) where the mixed model approaches appear more powerful.
To reduce the bias implicit in utilizing only complete cases, multiple imputation procedures were implemented using the SPSS Markov Chain Monte Carlo multiple imputation function to produce five imputed datasets.
