Examples of data manipulation that can introduce bias to the derived variables.

Note. (a) this shows the relationship between an input continuous variable and the derived dichotomized variable. The observations were sorted by increasing order based on the values of the continuous variable, the frailty index in the Burden Model in this case. The horizontal axis was the number of the observations. The vertical axis was the value of the continuous index. The bias variable was the essential information that was not related to original input variable. For example, one observation had a value of 0.1 and the other had 0.9, while the cut-off threshold was 0.2. The values of the bias variable required for the two observations were -0.1 and 0.1 respectively to derive their statuses, non-frail (coded as zero) and frail (coded as one). (b) the sensory problem was defined by having problem of hearing or eyesight. The observations were sorted by having problem of hearing and eyesight. Those having problem of hearing and eyesight needed to be subtracted by one for having two problems. The negative values assigned to those having two problems were the bias variable generated.