  Measuring quality of cancer care delivered by US health providers is
challenging. Patients receiving oncology care greatly vary in disease
presentation among other key characteristics. In this paper we discuss a
framework for institutional quality measurement which addresses the
heterogeneity of patient populations. For this, we follow recent statistical
developments on health outcomes research and conceptualize the task of quality
measurement as a causal inference problem, helping to target flexible covariate
profiles that can represent specific populations of interest. To our knowledge,
such covariate profiles have not been used in the quality measurement
literature. We use different clinically relevant covariate profiles and
evaluate methods for layered case-mix adjustments that combine weighting and
regression modeling approaches in a sequential manner in order to reduce model
extrapolation and allow for provider effect modification. We appraise these
methods in an extensive simulation study and highlight the practical utility of
weighting methods that warn the investigator when case-mix adjustments are
infeasible without some form of extrapolation that goes beyond the support of
the data. In a study of cancer-care outcomes, we assess the performance of
oncology practices for different profiles that correspond to the types of
patients who may receive cancer care. We describe how the methods examined may
be particularly important for high-stakes quality measurement, such as public
reporting or performance-based payments. These methods may also be applied to
support the health care decisions of individual patients and provide a path to
personalized quality measurement.
