title: Regulator Loss Functions and Hierarchical Modeling for Safety Decision Making.
abstract: Regulators must act to protect the public when evidence indicates safety problems with medical devices. This requires complex tradeoffs among risks and benefits, which conventional safety surveillance methods do not incorporate.
  
  
  
  To combine explicit regulator loss functions with statistical evidence on medical device safety signals to improve decision making.
  
  
  
  In the Hospital Cost and Utilization Project National Inpatient Sample, we select pediatric inpatient admissions and identify adverse medical device events (AMDEs). We fit hierarchical Bayesian models to the annual hospital-level AMDE rates, accounting for patient and hospital characteristics. These models produce expected AMDE rates (a safety target), against which we compare the observed rates in a test year to compute a safety signal. We specify a set of loss functions that quantify the costs and benefits of each action as a function of the safety signal. We integrate the loss functions over the posterior distribution of the safety signal to obtain the posterior (Bayes) risk; the preferred action has the smallest Bayes risk. Using simulation and an analysis of AMDE data, we compare our minimum-risk decisions to a conventional Z score approach for classifying safety signals.
  
  
  
  The 2 rules produced different actions for nearly half of hospitals (45%). In the simulation, decisions that minimize Bayes risk outperform Z score-based decisions, even when the loss functions or hierarchical models are misspecified.
  
  
  
  Our method is sensitive to the choice of loss functions; eliciting quantitative inputs to the loss functions from regulators is challenging.
  
  
  
  A decision-theoretic approach to acting on safety signals is potentially promising but requires careful specification of loss functions in consultation with subject matter experts.
authors.affiliation: Department of Health Care Policy, Harvard Medical School, Boston, MA, USA (LAH, VA).
authors.forename: Laura A
authors.initials: LA
authors.lastname: Hatfield
authors.affiliation: Interfaculty Initiative in Health Policy, Harvard University, Cambridge, MA, USA (CMB).
authors.forename: Christine M
authors.initials: CM
authors.lastname: Baugh
authors.affiliation: Department of Health Care Policy, Harvard Medical School, Boston, MA, USA (LAH, VA).
authors.forename: Vanessa
authors.initials: V
authors.lastname: Azzone
authors.affiliation: Department of Health Care Policy, Harvard Medical School and Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA (S-LTN).
authors.forename: Sharon-Lise T
authors.initials: ST
authors.lastname: Normand
mesh_terms: D000328:Adult; D003657:Decision Making; D006801:Humans; D008962:Models, Theoretical; D012449:Safety; D014481:United States; D014486:United States Food and Drug Administration
keywords: Bayesian statistical methods; database analysis; decision analysis; hierarchical models; performance measures; simulation methods