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Activity Number: 115 - HPSS Student Paper Competition Winners: Statistics Advancing Health Policy
Type: Topic Contributed
Date/Time: Monday, July 31, 2017 : 8:30 AM to 10:20 AM
Sponsor: Health Policy Statistics Section
Abstract #322651
Title: Joint Indirect Standardization When Only Marginal Distributions Are Observed in the Index Population
Author(s): Yifei Wang* and Diana Miglioretti and Daniel Tancredi
Companies: University of California, Davis and University of California, Davis and University of California, Davis
Keywords: Iterative Proportional Fit ; Synthetic Control Methods ; Hospital Profiling ; Survey Sampling ; Causal Inference
Abstract:

It is a common interest in medicine to determine whether a facility meets a benchmark created from an aggregate reference population, after accounting for distributional differences in multiple covariates. Due to the difficulties of collecting individual-level data, however, it is often the case that only marginal distributions of the covariates are available, making covariate-adjusted comparison difficult.

We propose and evaluate a novel approach for conducting these covariate-adjusted comparisons when only marginal covariate distributions of the studied facility are known. Our approach utilizes novel statistical methods that combine and extend existing methods, which are traditionally unrelated both to each other and to the medical problem of interest. These methods include Iterative Proportional Fit, which estimates the cells of a contingency table when only marginal sums are known, and Synthetic Control Methods, which extract information from a dataset when not all collected data points are equally representative of the object of study. Our novel methods will be applied to an example data reflecting the medical problem of interest, as well as simulated data.


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

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