title: Simulation study of instrumental variable approaches with an application to a study of the antidiabetic effect of bezafibrate.
abstract: OBJECTIVE
  We studied the application of the generalized structural mean model (GSMM) of instrumental variable (IV) methods in estimating treatment odds ratios (ORs) for binary outcomes in pharmacoepidemiologic studies and evaluated the bias of GSMM compared to other IV methods.
  
  
  METHODS
  Because of the bias of standard IV methods, including two-stage predictor substitution (2SPS) and two-stage residual inclusion (2SRI) with binary outcomes, we implemented another IV approach based on the GSMM of Vansteelandt and Goetghebeur. We performed simulations under the principal stratification setting and evaluated whether GSMM provides approximately unbiased estimates of the causal OR and compared its bias and mean squared error to that of 2SPS and 2SRI. We then applied different IV methods to a study comparing bezafibrate versus other fibrates on the risk of diabetes.
  
  
  RESULTS
  Our simulations showed that unlike the standard logistic, 2SPS, and 2SRI procedures, our implementation of GSMM provides an approximately unbiased estimate of the causal OR even under unmeasured confounding. However, for the effect of bezafibrate versus other fibrates on the risk of diabetes, the GSMM and two-stage approaches yielded similarly attenuated and statistically non-significant OR estimates. The attenuation of the OR by the two-stage and GSMM IV approaches suggests unmeasured confounding, although violations of the IV assumptions or differences in the parameters estimated could be playing a role.
  
  
  CONCLUSIONS
  The GSMM IV approach provides approximately unbiased adjustment for unmeasured confounding on binary outcomes when a valid IV is available.
authors.affiliation: Epidemiology Department of Pfizer Inc, 500 Arcola Road, Collegeville, PA, USA. bing.cai@pfizer.com
authors.forename: Bing
authors.initials: B
authors.lastname: Cai
authors.forename: Sean
authors.initials: S
authors.lastname: Hennessy
authors.forename: James H
authors.initials: JH
authors.lastname: Flory
authors.forename: Daohang
authors.initials: D
authors.lastname: Sha
authors.forename: Thomas R
authors.initials: TR
authors.lastname: Ten Have
authors.forename: Dylan S
authors.initials: DS
authors.lastname: Small
mesh_terms: D001629:Bezafibrate / Q000008:administration & dosage / Q000627:therapeutic use; D003198:Computer Simulation; D015986:Confounding Factors, Epidemiologic; D003920:Diabetes Mellitus / Q000453:epidemiology / Q000517:prevention & control; D006801:Humans; D007004:Hypoglycemic Agents / Q000008:administration & dosage / Q000627:therapeutic use*; D016015:Logistic Models; D008961:Models, Structural; D016017:Odds Ratio; D017891:Pharmacoepidemiology / Q000379:methods* / Q000706:statistics & numerical data
chemical_list: D007004:Hypoglycemic Agents; D001629:Bezafibrate