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Furthermore, as the number of variables increases, even a small proportion of missing data for each variable can lead to severely depleted data for CCA and substantial loss of power.
For many types of omics assays assumption of absence of missing data could be (almost) true, and a small proportion of missing data could be imputed (in the simplest case – replaced with average value) with little negative effect onto statistical properties of the method.
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Greater sequencing depth leads to a smaller proportion of missing data but increases genotyping cost.
There is also missing data in the registry, although we encountered only a small proportion of missing information for the variables of interest.
We applied an indicator variable approach to address the small proportion of missing data on the adjustment covariates (5.6% of study participants were missing data on at least one covariate).
Given the very small proportion of missing data, we did not employ a data imputation method, as doing so would not have affected the direction or magnitude of our results.
Because of this relatively small proportion of missing data, their impact on the estimates is likely to be marginal.
Strengths of our data include the large population base, the very small proportion of missing data (1.6%) and the standardised classification of cause of death.
This study was strengthened by prospective data collection and having a low proportion of missing data.
However, this approach is only appropriate for small proportions of missing data.
With a small proportion of missing binary outcomes, best and worst case analyses may be appropriate.
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