27: Two-Phase Designs for Biomarker Studies when Disease Processes are under Intermittent Observation

Music City Center 

Many chronic diseases can be characterized using multistate models. Longitudinal cohorts and registry studies of chronic diseases typically recruit and follow individuals to record data on the nature and timing of disease progression. In many cases the exact transition times between disease states are not observed directly, but the state occupied at each clinic visit is known. Such studies also routinely collect and store serum samples at the intermittent clinic visits. We consider the design of two-phase studies aimed at selecting individuals for biospecimen assays to measure biomarkers of interest and estimate their association with disease progression. Likelihood-based and estimating function approaches are developed and the efficiency gains from residual-dependent sampling strategies are investigated for joint models of the biomarker and disease progression processes. The robustness and efficiency of different frameworks are investigated, and the methods are applied to a motivating study of the relationship between the HLA-B27 marker and joint damage in arthritis.

two-phase design

multistate model

intermittent observation

maximum likelihood

inverse probability weighting

design efficiency