A mixed effects model for analyzing area under the curve of longitudinally measured biomarkers with missing data.

ABSTRACT: A simple approach for analyzing longitudinally measured biomarkers is to calculate summary measures such as the area under the curve (AUC) for each individual and then compare the mean AUC between treatment groups using methods such as t test. This two-step approach is difficult to implement when there are missing data since the AUC cannot be directly calculated for individuals with missing measurements. Simple methods for dealing with missing data include the complete case analysis and imputation. A recent study showed that the estimated mean AUC difference between treatment groups based on the linear mixed model (LMM), rather than on individually calculated AUCs by simple imputation, has negligible bias under random missing assumptions and only small bias when missing is not at random. H

SUBMITTER: Shi L 

PROVIDER: S-EPMC8604790 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature