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Abstract:
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Studies aimed at estimating medical costs accrued under different treatments are critical to making informed healthcare policy decisions. Because these studies often use data from observational sources, results may be biased due to unmeasured confounding or informative cost censoring. We propose a partitioned, instrumental variable estimator of the complier average treatment effect on costs. Given a valid instrument, our estimator will be unbiased in the presence of potential unmeasured confounding. Additionally, the use of a partitioned cost estimator allows us to address informative cost censoring and improve efficiency by utilizing data from patients with censored medical costs. Our proposed estimator is based on influence functions and hence is doubly robust, efficient, and can incorporate semiparametric approaches for estimation. We present results from simulation studies to assess the performance of our proposed estimator under varying degrees of censoring and strength of IV. We apply our approach to a study assessing the costs of surgical and non-surgical interventions for gallstones and hemorrhaging using observational data.
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