Joint Indirect Standardization When Only Marginal Distributions are Observed in the Index Population

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1 Scopus citations


It is a common interest in medicine to determine whether a hospital meets a benchmark created from an aggregate reference population, after accounting for differences in distributions of 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 challenging. We propose and evaluate a novel approach for conducting indirect standardization when only marginal covariate distributions of the studied hospital are known, but complete information is available for the reference hospitals. We do this with the aid of two existing methods: iterative proportional fit, which estimates the cells of a contingency table when only marginal sums are known, and synthetic control methods, which create a counterfactual control group using a weighted combination of potential control groups. The proper application of these existing methods for indirect standardization would require accounting for the statistical uncertainties induced by a situation where no individual-level data are collected from the studied population. We address this need with a novel method which uses a random Dirichlet parameterization of the synthetic control weights to estimate uncertainty intervals for the standard incidence ratio. We demonstrate our novel methods by estimating hospital-level standardized incidence ratios for comparing the adjusted probability of computed tomography examinations with high radiations doses, relative to a reference standard and we evaluate out methods in a simulation study. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Original languageEnglish (US)
JournalJournal of the American Statistical Association
StateAccepted/In press - Jan 1 2018


  • Causal inference
  • Hospital profiling
  • Iterative proportional fit
  • Survey sampling
  • Synthetic control

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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