The phrase "a high proportion of missing values" is correct and usable in written English.
It can be used in contexts such as data analysis, statistics, or research when discussing datasets that have a significant amount of incomplete information.
Example: "The dataset was deemed unreliable due to a high proportion of missing values, which affected the overall analysis."
Alternatives: "a significant amount of missing data" or "a large number of absent values."
In empirical applications where background variables typically are correlated, it is plausible to assume that the more background variables are included, the less problematic is a high proportion of missing values on a single variable as other variables might compensate the loss of information.
However, some variables had a high proportion of missing values because of the lack of standardised maternity charts before 1973.
Then, in order to make more robust and unbiased statistical analysis, probes with a high proportion of missing values were removed from the dataset.
Variables, which had a high proportion of missing values, or which in multivariable analysis turned out to be neither confounder nor secondary exposure, were ultimately removed from the model.
Concerns have been raised that this approach can lead to biased estimation results, particularly where covariates have a high proportion of missing values and are strong confounders (Vach & Blettner, 1991; Greenland & Finkle, 1995).
For Stage 58 analysis, probes with a high proportion of missing values (more than two missing values across the biological replicates of each condition) were removed from the dataset.
Variables with <10% of missing values were imputed using multiple imputation [ 19], and those with a higher proportion of missing values were excluded.
Although a similar proportion of organs appeared normal in the two groups, DCD donors had a higher proportion of missing values (29%) than DBD donors (18%).
The fact that some of the questions had a higher proportion of missing values than others, e.g. "Why did you come here today?
The factor perception of management also had a relatively high proportion of missing values, similar to the previous Norwegian study [ 22].
These were considered to be possible confounders, but were only available for limited periods and had a relatively high proportion of missing values.
