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We only present results based on imputed datasets.
Since results of analyses based on imputed datasets were comparable to the results found in complete case analyses, we only show results based on imputed data.
This indicated that missing data did not fit with an assumption of missing completely at random, emphasising the importance of an intention to treat analysis based on imputed datasets.
Regarding the general problem of missing values in registry variables, our results were robust to the missingness assumption, as the results based on imputed datasets were similar to complete case analyses.
CACE model estimates and ITT estimates based on imputed datasets show a small reduction in the magnitude of the treatment difference at 3, 6 and 12 months with the exception of the Roland Morris questionnaire at 3 months.
Only results based on imputed datasets are presented here as complete case analysis is thought to suffer from more chance variation, and multiple random imputation is assumed to correct any bias.
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Analyses will be performed on available data, and on imputed datasets based on the method of multiple imputation [ 47].
Clark and Altman [ 38] also developed their ovarian cancer prognostic model based on ten imputed datasets, and Heymans et al. [ 39] found that similar models were selected using ten versus one hundred imputations.
Generally the complete case analysis showed similar results to the results based on the imputed datasets.
Proportions for ethnicity and religion are based on the imputed datasets.
We performed single instead of multiple imputation due to the relatively low percentage of missing values and the high computational effort that is associated with analysing additive models based on multiply imputed datasets.
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