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Following the seminal work of Rubin (1976, see also; Little and Rubin 2002; Schafer 1997; Enders 2010) let R be a missing data indicator, taking the value of 1 if the data are observed, and 0 if the data are missing.
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MI is a missing data technique that imputes plausible values for the missing values.
Ultimately, verification bias is a missing data problem and we hypothesize that no correction method will be able to overcome a cell count of zero.
Before the PCB variable is included, a missing data analysis is performed to investigate the appropriateness of the underlying assumption of the previous complete case analysis that data are missing completely at random.
If the impact is great, a missing data strategy will be put in place using mean horizontal imputation and last value carried forward.
The inference of diplotypes for a particular genotype is statistically a missing data problem that can be formulated by a finite mixture model.
A neutral response (answer 3 on the Likert scale) was considered a missing data point.
Those that fall between the identified populations of the bimodal distribution, or are completely missing, were treated as a missing data point.
If the patient is lost to follow-up, the remission is considered as a missing data.
Ambiguous results in all cases were recorded as a missing data.
However, in epidemiological data sets this multivariate approach is likely to introduce a missing data problem.
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