Measurements below the detection limit.

Let's start with an example: Figure 1 shows a fictional dataset of measurements of serum levels of a substance in two patient groups. The crosses indicate the actual values, but only those above the lower detection limit can be measured. We know how many values are below the detection limit, but not the actual values. These data are missing not at random (MNAR) because the probability depends on the non-observed values being below the detection limit (1).