In statistics, the missing data sometimes contribute much more information to the data. So, we cannot ignore the missing data from our analysis.
Thus, how to overcome the problems that exist in my data below? thanks for help.
Gabor Borgulya
- Bayesian survival analysis and/or
- Multiple imputation with chained equations
could both help.
In any case it is important to think over why the missing data may be missing, and if not at random then performing sensitivity analyses to see how big their impacts on the results can be.
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