Simulation study of instrumental variable approaches with an application to a study of the antidiabetic effect of bezafibrate

Pharmacoepidemiology and Drug Safety

Volumn 21, Issue SUPPL.2, 2012, Pages 114-120

  Cai, Bing ^a   Hennessy, Sean ^b   Flory, James H ^c   Sha, Daohang ^b   Ten Have, Thomas R ^b,d   Small, Dylan S ^d  

^a PFIZER INC   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

^c WEILL CORNELL MEDICAL CENTER   (United States)

^d UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

Bezafibrate; Causal inference; Diabetes; Generalized structural mean model; Instrumental variable; Two stage regression

Indexed keywords

BEZAFIBRATE; FIBRIC ACID DERIVATIVE;

ANALYTIC METHOD; ANTIDIABETIC ACTIVITY; ARTICLE; DIABETES MELLITUS; GENERALIZED STRUCTURAL MEAN MODEL; HUMAN; INSTRUMENTAL VARIABLE ANALYSIS; OUTCOME ASSESSMENT; PHARMACOEPIDEMIOLOGY; PRESCRIPTION; PRIORITY JOURNAL; PROBABILITY; RISK FACTOR; SIMULATION; STANDARD;

BEZAFIBRATE; COMPUTER SIMULATION; CONFOUNDING FACTORS (EPIDEMIOLOGY); DIABETES MELLITUS; HUMANS; HYPOGLYCEMIC AGENTS; LOGISTIC MODELS; MODELS, STRUCTURAL; ODDS RATIO; PHARMACOEPIDEMIOLOGY;

EID: 84860585678     PISSN: 10538569     EISSN: 10991557     Source Type: Journal    
DOI: 10.1002/pds.3252     Document Type: Article

References (26)

1. Goetghebeur E, Vansteelandt S. Structural mean models for compliance analysis in randomized clinical trials and the impact of errors on measures of treatment. Stat Methods Med Res 2005; 14: 397-415.
2. Vansteelandt S, Goetghebeur E. Causal Inference with generalized structural mean models. J Roy Stat Soc B 2003; 65: 817-835.
3. Nagelkerke N. Estimating treatment effects in randomized clinical trials in the presence of non-compliance. Stat Med 2000; 19(6): 1849-1864.
4. Johnston KM, Gustafson P, Levy AR, Grootendorst P. Use of instrumental variables in the analysis of generalized linear models in the presence of unmeasured confounding with applications to epidemiological research. Stat Med 2008; 27: 1539-1556.
5. Wang P, Schneeweiss S, Avorn J, et al. Risk of death in elderly users of conventional vs. atypical antipsychotic medications. New Engl J Med 2005; 353: 2335-2341.
6. Brooks JM, Chrischilles EA. Heterogeneity and the interpretation of treatment effect estimates from risk adjustment and instrumental variable methods. Med Care 2007; 45: S123-S130.
7. Stukel TA, Fisher ES, Wennberg DE, et al. Analysis of observational studies in the presence of treatment selection bias: effects of invasive cardiac management on AMI survival using propensity score and instrumental variable methods. JAMA 2007; 297:278-285.
8. Basu A, Heckman JJ, Navarro-Lozano S, Urzua S. Use of instrumental variables in the presence of heterogeneity and self-selection: an application to treatments of breast cancer patients. Health Econ 2007; 16: 1133-1157.
9. Angrist JD, Imbens GW, Rubin DB. Identification of causal effects using instrumental variables. J Am Stat Assoc 1996; 91: 444-455.
10. Terza JV. Parametric Nonlinear Regression with Endogenous Switching. Econometric Rev 2009; 28(6):555-580.
11. Terza JV, Basu A, Rathouz PJ. Two-stage residual inclusion estimation: Addressing endogeneity in health econometric modeling. J Health Econ 2008; 27(3): 531-543.
12. Cai B, Small DS, Ten Have T. Bias of Causal Inference for the Odds Ratio Using Two-stage Instrumental Variable Methods. Stat Med 2011; 30: 1809-1824.
13. Tenenbaum A, Fisman EZ, Boyko V, et al. Attenuation of progression of insulin resistance in patients with coronary artery disease by bezafibrate. Arch Intern Med 2006; 166: 737-741.
14. Tenenbaum A, Fisman EZ, Boyko V, et al. Bezafibrate averts progression of insulin resistance in diabetic patients with coronary artery disease. Eur Heart J 2006; 27: 532.
15. Tenenbaum A, Motro M, Fisman EZ, et al. Effect of bezafibrate on incidence of type 2 diabetes mellitus in obese patients. Eur Heart J 2005; 26: 2032-2038.
16. Tenenbaum A, Motro M, Fisman EZ, et al. Peroxisome proliferator-activated receptor ligand bezafibrate for prevention of type 2 diabetes mellitus in patients with coronary artery disease. Circulation 2004; 109: 2197-2202.
17. Fernandes-Santos C, Carneiro RE, Mendonca LD, et al. Pan-PPAR agonist beneficial effects in overweight mice fed a high-fat high-sucrose diet. Nutrition 2009; 25: 818-827.
18. Flory JH, Ellenberg S, Szapary PO, et al. Antidiabetic Action of Bezafibrate in a Large Observational Database. Diabetes Care 2009; 32: 547-551.
19. Brookhart MA, Wang PS, Solomon DH, et al. Evaluating short-term drug effects using a physician-specific prescribing preference as an instrumental variable. Epidemiology 2006; 17: 268-275.
20. Hennessy S, Leonard CE, Palumbo CM, et al. Instantaneous preference was a stronger instrumental variable than 3-and 6-month prescribing preference for NSAIDs. J Clin Epidemiol 2008; 61: 1285-1288.
21. Korn EL, Teeter DM, Baumrind S. Using explicit clinician preferences in nonrandomized study designs. J Stat Plann Infer 2001; 96: 67-82.
22. Hernan MA, Robins JM. Instruments for causal inference - An epidemiologist's dream? Epidemiology 2006; 17: 360-372.
23. Brookhart MA, Schneeweiss S. Preference-based instrumental variable methods for the estimation of treatment effects: assessing validity and interpreting results. Int J Biostatist 2007; 3(1): 1-14.
24. Ten Have TR, Joffe MM, Lynch K, Brown G, Maistro S, Beck A. Causal mediation analyses with rank preserving models. Biometrics 2007; 63: 926-934.
25. Small D. Sensitivity analysis for instrumental variables regression with overidentifying restrictions. J Am Stat Assoc 2007; 102: 1048-1509.
26. Odondi L, McNamee R. Performance of statistical methods for analyzing survival data in the presence of non-random compliance. Stat Med 2010; 29: 2994-3003.