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Abstract:
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Estimating current cost of cancer care is important to health policy makers. An indispensable step in cost projection is to estimate the cost trajectory from an incident cohort of cancer patients using longitudinal medical cost data, accounting for terminal events such as death, and right censoring due to loss of follow-up. Since the cancer care cost and survival are correlated, a scientifically meaningful quantity for inference in this context is the mean cost trajectory conditional on survival. We propose a two-stage semiparametric approach to estimate the longitudinal cost trajectories from a joint model of longitudinal medical costs and survival. The proposed approach balances the practical considerations of model flexibility, statistical efficiency and computational tractability. Asymptotic theory and extensive simulation studies demonstrate that the estimator is unbiased, efficient and robust to model misspecifications. We used the proposed method to estimate the cost trajectories of renal cell cancer patients using the Surveillance, Epidemiology, and End Results-Medicare Linked database.
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