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In this study, multiple imputation gives insight into the magnitude of selection bias on the prevalence estimates.
It has been shown that a properly performed multiple imputation gives less biased results compared to traditional complete case analysis [ 34].
Multiple imputation gives unbiased results providing the data are missing at random, that is, assuming that the missingness is not related to the value of the observation, conditional on the variables included in the imputation model.
Similar(57)
We argue that the relevant psychometric literature on PVs, as well as the more general statistical literature on multiple imputations, gives reason for optimism.
For these missing data, it was not feasible to perform multiple imputations, given that our analyses were computationally expensive (see below).
We deferred using multiple imputation methods given the complex survey design and the number of records (millions) requiring computation power exceeding the capability of our research team.
The multiple imputation analysis gave similar results.
In such cases multiple imputation may give misleading results.
Therefore the multiple imputation should give a good correction for non-response bias, resulting in a reliable outcome.
One referred to a paper on multiple imputation but gave no further details, the other stated that "1000 imputation sequences" were used.
Where complete cases and multiple imputation analyses give different results, the analyst should attempt to understand why, and this should be reported in publications.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com