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Biased estimates from missing data occur when the data are not missing at random [ 42].
Errors in patient identifiers, and missing data, occur even in well-validated data sets such as PICANet and have implications for analysis.
In the event that missing data occur, we will attempt to contact participants and obtain the data or to find out why the surveys or items are missing.
It should be noted that such an analysis assumes that missing data occur completely at random which might not be plausible, in which case appropriate imputation techniques should be applied [ 18].
In the event that missing data occur, we will attempt to contact participants and obtain the data or to find out why the questionnaires or items are missing, and document the reasons for missing data.
When missing data occur only in an outcome variable that is measured once in each individual, then such analyses will not be biased, provided that all variables associated with the outcome being missing can be included as covariates (under a missing at random assumption).
Similar(53)
The approach is to use information common to both surveys to impute plausible values of the missing data occurring in both surveys.
Monotone missing data occurs frequently in longitudinal studies.
The majority of missing data occurred for two items – informal care and medications.
The problem of missing data occurs in almost all retrospective studies using routine health databases.
Longitudinal data of this kind benefit from careful analysis, especially in small samples or where missing data occurs.
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