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Because complete case analysis is liable to bias due to selective missing of data however, we repeated the analyses after missing data had been multiple imputed.
Missing of data resulted in fewer observations in multivariate models which may increase risk of type II error; further, if missing data did not occur at random the potential for bias in ORs exists.
If the probability of data being missing is associated with variables measured in the study and is not influenced by unobserved external factors, multiple imputation is an appropriate method to avoid bias that may occur in complete case analysis due to selective missing of data.
Similar(57)
A missing day of data was also treated as if no pills were taken.
This will increase the risk of missed linkages of data on single individuals.
Four samples were excluded because of missing data of either survival time or MT expression.
All other frailty variables had a proportion of missing data of <5% (table 1).
A final restriction is the uncertainty about the amount of missing data, as increasing numbers of missing data reduce power.
Because of the relatively small amount of missing data casewise exclusion of missing cases was performed.
Missing data Missing data The amount of missing data will be reported for each randomised arm.
No variable had missing data of more than 5%%.
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CEO of Professional Science Editing for Scientists @ prosciediting.com