Joint modeling of biomarkers, treatment initiation and disease progression with incomplete data

Music City Center 

Study and understanding of disease processes based on registry data or electronical medical records is challenging because disease process evolve in continuous-time but individuals are only seen upon encounters with the healthcare system. We consider issues in the study of chronic diseases where a dynamic marker is both associated with disease progression and the prescription of treatment but information is only available at intermittent clinic visits that may also be driven by the marker process. Through this joint model we identify different facets of the confounding that arise from some standard and more involved analyses and discuss identifiability issues when aiming to fit comprehensive models. Remarks on causal analyses for both the potential outcomes and Granger schools are also made. This is joint work with Richard Cook and Jerry Lawless.

dynamic biomarker

time-dependent confounding

intermittent observation

multistate model

intensity functions

estimands