  To make informed health policy decisions regarding a treatment, we must
consider both its cost and its clinical effectiveness. In past work, we
introduced the net benefit separation (NBS) as a novel measure of
cost-effectiveness. The NBS is a probabilistic measure that characterizes the
extent to which a treated patient will be more likely to experience benefit as
compared to an untreated patient. Due to variation in treatment response across
patients, uncovering factors that influence cost-effectiveness can assist
policy makers in population-level decisions regarding resource allocation. In
this paper, we introduce a regression framework for NBS in order to estimate
covariate-specific NBS and find determinants of variation in NBS. Our approach
is able to accommodate informative cost censoring through inverse probability
weighting techniques, and addresses confounding through a semiparametric
standardization procedure. Through simulations, we show that NBS regression
performs well in a variety of common scenarios. We apply our proposed
regression procedure to a realistic simulated data set as an illustration of
how our approach could be used to investigate the association between cancer
stage, comorbidities and cost-effectiveness when comparing adjuvant radiation
therapy and chemotherapy in post-hysterectomy endometrial cancer patients.
