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With a data-driven approach missing data is a difficult problem to tackle, except with the obvious method of capturing more data.
For the ITT approach, missing data were imputed via multiple imputation using chained equations; 20 imputed datasets were computed on guidance that the number needed should approximate the percentage of incomplete cases.
aAt the suggestion of one of the reviewers, the primary analyses were re-conducted using an intention-to-treat approach (missing data at follow-up replaced with the respective baseline values).
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This multiple imputation approach will be compared to a last observation carried forward approach, mixed models approach, or worst-case approach to missing data.
We shall adopt a consistent approach to missing data relating to effectiveness and cost-effectiveness except where individual outcome measures require variation in that approach.
This approach to missing data used estimates of allele frequencies without allowing for their uncertainty, a deficiency later corrected in the approach of Chung et al. [ 25].
Challenges include sampling methods, instrument development and statistical approach to missing data.
It is observed that the classification accuracy is improved or at least maintained by using our approach for missing data imputation.
In order to show the relationships between actuator constraints, unreliable communication link and mixed delay neural networks, by using Lyapunov functional approach, a missing data probability dependent exponential synchronization criterion is given.
Our primary approach to missing data was to restrict our analysis to complete cases (listwise deletion).
In addition, a post hoc sensitivity analysis was performed using a different approach to missing data imputation.
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