Separating variability in healthcare practice patterns from random error

Laine E. Thomas, Phillip J. Schulte

Research output: Contribution to journalArticle

Abstract

Improving the quality of care that patients receive is a major focus of clinical research, particularly in the setting of cardiovascular hospitalization. Quality improvement studies seek to estimate and visualize the degree of variability in dichotomous treatment patterns and outcomes across different providers, whereby naive techniques either over-estimate or under-estimate the actual degree of variation. Various statistical methods have been proposed for similar applications including (1) the Gaussian hierarchical model, (2) the semi-parametric Bayesian hierarchical model with a Dirichlet process prior and (3) the non-parametric empirical Bayes approach of smoothing by roughening. Alternatively, we propose that a recently developed method for density estimation in the presence of measurement error, moment-adjusted imputation, can be adapted for this problem. The methods are compared by an extensive simulation study. In the present context, we find that the Bayesian methods are sensitive to the choice of prior and tuning parameters, whereas moment-adjusted imputation performs well with modest sample size requirements. The alternative approaches are applied to identify disparities in the receipt of early physician follow-up after myocardial infarction across 225 hospitals in the CRUSADE registry.

Original languageEnglish (US)
Pages (from-to)1247-1260
Number of pages14
JournalStatistical Methods in Medical Research
Volume28
Issue number4
DOIs
StatePublished - Apr 1 2019

Keywords

  • Provider variation
  • hierarchical data
  • measurement error
  • moment-adjusted imputation
  • practice patterns

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

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