Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 323 - Estimating Treatment Effects: Applications in Health Policy
Type: Contributed
Date/Time: Wednesday, August 5, 2020 : 10:00 AM to 2:00 PM
Sponsor: Health Policy Statistics Section
Abstract #313771
Title: Quality Measurement as Causal Inference, and Implications for Adjustment
Author(s): Alan Zaslavsky*
Companies: Harvard University
Keywords: causal inference; healh care quality; survey calibration; standardization

Survey measures of the quality of health care units are commonly reported comparatively, "controlling" for the effects of characteristics not due to the units' activities. Such inferences simulate potential outcomes if cohorts of patients with identical sample covariate distributions had been treated at each of the units.

In the annual survey of disenrollees from Medicare Advantage plans, both controlled comparisons and descriptions of units are important, with conflicting implications for sample design and analysis, specifically between representativeness and balance. We compare weighting estimators (nonparametric direct standardization) and regression adjustments. The weighting estimators are valid under relatively weak assumptions and clearly relate to the hypothetical replicated assessment cohorts, but incur a readily estimated variance penalty with unbalanced samples, and may be incalculable when the samples do not have common support in covariate space. Regression can implement more extreme adjust adjustments, but requires stronger statistical assumptions. We calculate and compare the additional variance corresponding to violations of these assumptions.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2020 program