I recently encountered a small cross-sectional retrospective study that was quite interesting.
- 25 subjects were reported have been exposed to some agent and their serum was collect (in 1999)
- 13 clinically healthy control subjects had their serum collected (in 2019)
There are two (very) new AKI biomarkers that are being tested on these samples (the PI/ PhD Student hoping this will lead to a publication). The overall hypothesis was to observe if the control group samples are different from the exposed group.
When the serum from the control and the exposed population were run, the control samples had >65% of the samples with values < LLOQ for both serum samples (say 50 ng/dL for AKI marker 1 and 100 ng/dL for AKI marker 2).
You can imagine with such a high number observations below the LLOQ, methods like zero-inflated Poisson, multiple imputation and others do not work very well. In general, imputing values below the LLOQ with the LLOQ value is considered too conservative. But in this case, I feel though as though the claims that can be made with the data is quite limited (small sample size, vastly different collection times between the results, etc…) so being as conservative as possible is justified.
Would be interested to hear other people’s thoughts.