Two-phase designs for biomarker studies when disease processes are under intermittent observation

Multistate models offer an appealing framework for studying the onset and progression of chronic diseases in large cohort studies. Such studies often involve the collection and storage of biospecimens at an initial assessment, and intermittent observation of the disease process at future assessment times. We consider the design of two-phase biomarker studies in such settings where budgetary constraints prohibit assaying all biospecimens. A subsample of individuals is instead chosen to have their biospecimens assayed to facilitate examination of the association between a biomarker of interest and the disease process. Analyses based on likelihood, conditional likelihood, and estimating functions are considered, with the efficiency gains from various subsampling strategies investigated. Pseudo-score residual-dependent sampling strategies are shown to yield highly efficient maximum likelihood estimates of biomarker effects on disease progression. This sampling strategy along with competing methods are empirically studied and applied to a motivating study of the relationship between the HLA-B27 marker and joint damage in patients with psoriatic arthritis.