Prior event rate ratio adjustment: Numerical studies of a statistical method to address unrecognized confounding in observational studies

Pharmacoepidemiology and Drug Safety

Volumn 21, Issue SUPPL.2, 2012, Pages 60-68

  Yu, Menggang ^a   Xie, Dawei ^b   Wang, Xingmei ^b   Weiner, Mark G ^b   Tannen, Richard L ^b  

^a UNIVERSITY OF WISCONSIN SCHOOL OF MEDICINE AND PUBLIC HEALTH   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

Bias; Cox model; Database study; Randomized controlled trials; Unmeasured confounding

Indexed keywords

ARTICLE; CONFOUNDING VARIABLE; ELECTRONIC MEDICAL RECORD; EMPIRICISM; HUMAN; OBSERVATIONAL STUDY; PRIOR EVENT RATE RATIO ADJUSTMENT; PRIORITY JOURNAL; PROPORTIONAL HAZARDS MODEL; SIMULATION; STATISTICAL ANALYSIS; VALIDITY;

BIAS (EPIDEMIOLOGY); COMPUTER SIMULATION; CONFOUNDING FACTORS (EPIDEMIOLOGY); DATABASES, FACTUAL; EPIDEMIOLOGIC METHODS; MODELS, STATISTICAL; PROPORTIONAL HAZARDS MODELS; RANDOMIZED CONTROLLED TRIALS AS TOPIC; RESEARCH DESIGN;

EID: 84860573013     PISSN: 10538569     EISSN: 10991557     Source Type: Journal    
DOI: 10.1002/pds.3235     Document Type: Article

References (32)

1. Sox H, Greenfield S. Comparative effectiveness research: a report from the Institute of Medicine. Ann Intern Med 2009; 151: 203-205.
2. Tannen RL, Weiner MG, Xie D. Use of primary care electronic medical record database in drug efficacy research on cardiovascular outcomes: comparison of database and randomized controlled trial findings. BMJ 2009; 338: b81. [doi:10.1136/bmj.b81]
3. Tannen RL, Weiner MG, Xie D, Barnhart K. A simulation using data from a primary care practice database closely replicated the women's health initiative trial. J Clin Epidemiol 2007; 60: 686-695.
4. Weiner MG, Xie D, Tannen RL. Replication of the Scandinavian Simvastatin Survival Study using a primary care medical record database prompted exploration of a new method to address unmeasured confounding. Pharmacoepidemiol Drug Saf 2008; 17: 661-670.
5. Tannen RL, Weiner MG, Xie D. Replicated studies of two randomized trials of angiotensin converting enzyme inhibitors: further empiric validation of the "prior event rate ratio" to adjust for unmeasured confounding by indication. Pharmacoepidemiol Drug Saf 2008; 17: 671-685.
6. Gelfand JM, Margolis DJ, Dattani H. The UK General Practice Research Database. In Pharmacoepidemiology, 4th edn, Strom BL (ed.). New York: John Wiley, 2005: 337-346.
7. Writing Group for the Women's Health Initiative Investigators. Risks and benefits of estrogen plus progestin in health postmenopausal women. Principal results from the Women's Health Initiative randomized controlled trial. J Am Med Assoc 2002; 288: 321-333.
8. Women's Health Initiative Steering Committee. Effects of conjugated equine estrogen in postmenopausal women with hysterectomy: the Women's Health Initiative randomized controlled trial. J Am Med Assoc 2004; 291: 1701-1712.
9. Rossouw JE, Prentice RL, Manson JE, et al. Postmenopausal hormone therapy and risk of cardiovascular disease by age and years since menopause. J Am Med Assoc 2007; 297: 1465-1477.
10. Tannen RL, Weiner MG, Xie D, Barnhart K. Estrogen affects post-menopausal women differently than estrogen plus progestin therapy. Hum Reprod 2007; 22: 1769-1777.
11. Scandinavian Simvastatin Survival Study Group. Randomised trial of cholesterol lowering in 4444 patients with coronary heart disease: the Scandinavian Simvastatin Survival Study (4S). Lancet 1994; 344: 1383-1389.
12. Heart Outcomes Prevention Evaluation Study Investigators. Effects of an angiotensin-converting-enzyme inhibitor, ramipril, on cardiovascular events in high risk patients. N Eng J Med 2000; 342: 145-153.
13. European Trial on Reduction of Cardiac Events with Perindopril in Stable Coronary Artery Disease Investigators. Efficacy of perindopril in reduction of cardiovascular events among patients with stable coronary artery disease: randomized double-blind, placebo-controlled, multicentre trial (the EUROPA study). Lancet 2003; 362: 782-788.
14. Bretagnolle J, Huber-Carol C. Effects of omitting covariates in Cox's model for survival data. Scand J Stat 1988; 15: 125-138.
15. Struthers CA, Kalbfleisch JD. Misspecified proportional hazard models. Biometrika 1986; 73: 363-369.
16. Henderson R, Oman P. Effect of frailty on marginal regression estimates in survival analysis. J R Stat Soc Ser B 2002; 61: 367-379.
17. Kosorok MR, Lee LB, Fine JP. Robust inference for univariate proportional hazards frailty regression models. Ann Stat 2004; 32: 1448-1491.
18. Molenberghs G, Verbeke G. Models for Discrete Longitudinal Data. Springer: New York, 2005.
19. Gross TS, Huber C. Matched pair experiments: Cox and maximum likelihood estimation. Scand J Stat 1987; 14: 27-41.
20. Rosenbaum P, Rubin D. The central role of the propensity score in observational studies for causal effects. Biometrika 1983; 70: 41-55.
21. Rosenbaum P, Rubin D. Reducing bias in observational studies using subclassification on the propensity score. J Am Stat Assoc 1984; 79: 516-524.
22. D'Agostino RB, Rubin DB. Estimating and using propensity scores with partially missing data. J Am Stat Assoc 2000; 95: 749-759.
23. Sturmer T, Schneeweiss S, Rothman KJ, Avorn J, Glynn RJ. Performance of propensity score calibration - a simulation study. Am J Epidemiol 2007; 165: 1110-1118.
24. Angrist I. Identification of causal effects using instrumental variables (with discusssion). J Am Stat Assoc 1996; 91: 444-455.
25. 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(3): 268-275.
26. Rassen JA, Schneeweiss S, Glynn RJ, et al. Instrumental variable analysis for estimation of treatment effects with dichotomous outcomes. Am J Epidemiol 2009; 169(3): 273-284.
27. Rassen JA, Brookhart MA, Glynn RJ, et al. Instrumental variables II: instrumental variable application-in 25 variations, the physician prescribing preference generally was strong and reduced covariate imbalance. J Clin Epidemiol 2009; 62(12): 1233-1241.
28. Didelez V, Meng S, Sheehan NA. Assumptions of IV methods for observational epidemiology. Stat Sci 2010; 25(1): 22-40.
29. Athey S, Imbens GW. Identification and inference in nonlinear difference-in-differences models. Econometrica 2006; 74: 431-497.
30. Suissa S. The case-time-control design. Epidemiology 1995; 6(3): 248-253.
31. Greenland S. Confounding and exposure trends in case-crossover and case-time-control designs. Epidemiology 1996; 7(3): 231-239.
32. Suissa S. The case-time-control design: further assumptions and conditions. Epidemiology 1998; 9(4): 441-445.