Statistical interpolation used a statistical analysis that interpolated missing data, for example, mixed effects model interpolation.
However, there are concerns that common procedures for dealing with missing data, for example, listwise deletion and mean item substitution, are inadequate.
Imputing missing values is crucial for molecular marker data sets generated by methods with inherent high levels of missing data, for example genotyping-by-sequencing (GBS) [ 1].
We excluded less than 1.5% of all the data from the Real Time Monitoring system because of missing data (for example, age not known, deprivation not known).
Furthermore, there were missing data; for example, when patients had good clinical conditions fewer laboratory measurements were performed, resulting in difficulties in calculating SOFA scores.
Thus, other methods of examining missing data, for example, propensity scores, EM algorithm and multiple imputation sensitivity analyses, will be considered.
Most trials have missing data, for example, because people are too busy to reply, are unable to attend a clinic, have moved or no longer want to participate.
Although it is possible that there is some selection bias in the missing data – for example, pathology material is more likely to be unavailable for small tumours – any bias is unlikely to be large.
Observations were excluded if they had any missing data fields for example, if the month or year the patient was treated, the patient's deprivation status, or the patient's age was missing.
Given it is not possible to assess the validity of the MAR assumption, it is also important to assess the sensitivity of the results to this assumption by making alternative assumptions about the missing data, for example that data are missing not at random (MNAR), that is allowing the missingness to depend on the unobserved data.
