title: Performance of prior event rate ratio adjustment method in pharmacoepidemiology: a simulation study.
abstract: OBJECTIVE
  Prior event rate ratio (PERR) adjustment method has been proposed to control for unmeasured confounding. We aimed to assess the performance of the PERR method in realistic pharmacoepidemiological settings.
  
  
  METHODS
  Simulation studies were performed with varying effects of prior events on the probability of subsequent exposure and post-events, incidence rates, effects of confounders, and rate of mortality/dropout. Exposure effects were estimated using conventional rate ratio (RR) and PERR adjustment method (i.e. ratio of RR post-exposure initiation and RR prior to initiation of exposure).
  
  
  RESULTS
  In the presence of unmeasured confounding, both conventional and the PERR method may yield biased estimates, but PERR estimates appear generally less biased estimates than the conventional method. However, when prior events strongly influence the probability of subsequent exposure, the exposure effect from the PERR method was more biased than the conventional method. For instance, when the effect of prior events on the exposure was RR = 1.60, the effect estimate from the PERR method was RR = 1.13 and from the conventional method was RR = 2.48 (true exposure effect, RR = 2). In all settings, the variation of the estimates was larger for the PERR method than for the conventional method.
  
  
  CONCLUSIONS
  The PERR adjustment method can be applied to reduce bias as a result of unmeasured confounding. However, only in particular situations, it can completely remove the bias as a result of unmeasured confounding. When applying this method, theoretical justification using available clinical knowledge for assumptions of the PERR method should be provided.
authors.affiliation: Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, the Netherlands.
authors.forename: Md Jamal
authors.initials: MJ
authors.lastname: Uddin
authors.forename: Rolf H H
authors.initials: RH
authors.lastname: Groenwold
authors.forename: Tjeerd P
authors.initials: TP
authors.lastname: van Staa
authors.forename: Anthonius
authors.initials: A
authors.lastname: de Boer
authors.forename: Svetlana V
authors.initials: SV
authors.lastname: Belitser
authors.forename: Arno W
authors.initials: AW
authors.lastname: Hoes
authors.forename: Kit C B
authors.initials: KC
authors.lastname: Roes
authors.forename: Olaf H
authors.initials: OH
authors.lastname: Klungel
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data*; D015982:Bias; D003198:Computer Simulation; D064420:Drug-Related Side Effects and Adverse Reactions / Q000453:epidemiology* / Q000209:etiology; D006801:Humans; D008962:Models, Theoretical; D009010:Monte Carlo Method; D017891:Pharmacoepidemiology / Q000379:methods* / Q000706:statistics & numerical data
keywords: bias; epidemiology; pharmacoepidemiology; post-event; prior event; rate ratio; simulation; unmeasured confounding