Imputing right-skewed bounded biomarkers in partially measured cohorts

In large medical and epidemiological studies, important biomarkers are often only available for a limited fraction of participants due to the high laboratory costs or feasibility constraints. This results in a high proportion of missing values. Imputation strategies can be employed to prevent the loss of information. However, imputing biomarker values is challenging due to the rightskewed and naturally bounded values of biomarker distributions. In this talk, we compare two imputation strategies that can handle such challenges: a likelihood-based approach and logistic quantile imputation implemented in Stata. We evaluate the performance of both methods through simulation, assessing bias and inferential errors. The approaches are illustrated with a practical example of recently discovered blood biomarkers in Alzheimer’s research. The results provide some insight on recovering biomarker distributions when outcome data are fully observed but biomarkers are only partially measured.