17: Cost-Effective Treatment Regimes with Time-Invariant Decision Rules

Music City Center 

For healthcare systems, determining optimal treatment decisions for any given patient requires consideration of both the potential benefits and costs of an intervention. In recent work, traditional methods for identifying optimal multi-stage treatment regimes have been adapted to penalize treatments which are prohibitively expensive or harmful. These methods, however, typically assume unique treatment rules at each decision interval. Rules of this type may be undesirable in certain clinical applications, such as in the management of high blood pressure, where patients are expected to undergo consistent treatment or monitoring over an extended period. In this work, we develop methodology for estimating shared-parameter/time-invariant treatment regimes which explicitly account for the potential costs of an intervention. Estimation of rules in this setting are made more difficult due to: (1) highly skewed and zero-inflated cost distributions, (2) informatively censored cost data, and (3) confounding in treatment decisions. We propose the use of doubly robust g-methods, augmented with inverse probability weights, to identify optimal time-invariant treatment decisions for use in chronic disease settings. To illustrate usage, we identify optimally cost-effective time-invariant management strategies for patients with hypertension.