title: Orphan therapies: making best use of postmarket data.
abstract: BACKGROUND
  Postmarket surveillance of the comparative safety and efficacy of orphan therapeutics is challenging, particularly when multiple therapeutics are licensed for the same orphan indication. To make best use of product-specific registry data collected to fulfill regulatory requirements, we propose the creation of a distributed electronic health data network among registries. Such a network could support sequential statistical analyses designed to detect early warnings of excess risks. We use a simulated example to explore the circumstances under which a distributed network may prove advantageous.
  
  
  METHODS
  We perform sample size calculations for sequential and non-sequential statistical studies aimed at comparing the incidence of hepatotoxicity following initiation of two newly licensed therapies for homozygous familial hypercholesterolemia. We calculate the sample size savings ratio, or the proportion of sample size saved if one conducted a sequential study as compared to a non-sequential study. Then, using models to describe the adoption and utilization of these therapies, we simulate when these sample sizes are attainable in calendar years. We then calculate the analytic calendar time savings ratio, analogous to the sample size savings ratio. We repeat these analyses for numerous scenarios.
  
  
  RESULTS
  Sequential analyses detect effect sizes earlier or at the same time as non-sequential analyses. The most substantial potential savings occur when the market share is more imbalanced (i.e., 90% for therapy A) and the effect size is closest to the null hypothesis. However, due to low exposure prevalence, these savings are difficult to realize within the 30-year time frame of this simulation for scenarios in which the outcome of interest occurs at or more frequently than one event/100 person-years.
  
  
  CONCLUSIONS
  We illustrate a process to assess whether sequential statistical analyses of registry data performed via distributed networks may prove a worthwhile infrastructure investment for pharmacovigilance.
authors.affiliation: Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, 133 Brookline Avenue, 6th Floor, Boston, MA, 02215, USA, jmaro@mit.edu.
authors.forename: Judith C
authors.initials: JC
authors.lastname: Maro
authors.forename: Jeffrey S
authors.initials: JS
authors.lastname: Brown
authors.forename: Gerald J
authors.initials: GJ
authors.lastname: Dal Pan
authors.forename: Lingling
authors.initials: L
authors.lastname: Li
mesh_terms: D066275:Health Information Exchange; D006801:Humans; D006938:Hyperlipoproteinemia Type II / Q000188:drug therapy / Q000453:epidemiology; D009965:Orphan Drug Production / Q000706:statistics & numerical data*; D011358:Product Surveillance, Postmarketing; D035583:Rare Diseases / Q000188:drug therapy* / Q000453:epidemiology*; D012042:Registries; D018401:Sample Size; D013997:Time Factors