Propensity score calibration in the absence of surrogacy

American Journal of Epidemiology

Volumn 175, Issue 12, 2012, Pages 1294-1302

  Lunt, Mark ^a   Glynn, Robert J ^b   Rothman, Kenneth J ^c   Avorn, Jerry ^b   Stürmer, Til ^d  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

^b BRIGHAM AND WOMEN S HOSPITAL   (United States)

^c RTI HEALTH SOLUTIONS   (United States)

^d IL   (United States)

Author keywords

Bias (epidemiology); Confounding factors (epidemiology); Epidemiologic methods; Path analysis; Propensity score; Propensity score calibration; Research design

Indexed keywords

INFLUENZA VACCINE; NONSTEROID ANTIINFLAMMATORY AGENT;

CALIBRATION; COHORT ANALYSIS; DISEASE INCIDENCE; EPIDEMIOLOGY; MODEL VALIDATION; RESEARCH WORK; RISK FACTOR; SAMPLING BIAS;

ARTICLE; CALIBRATION; COHORT ANALYSIS; CORRELATIONAL STUDY; DATA ANALYSIS; HUMAN; INFLUENZA; INFLUENZA VACCINATION; MORTALITY; OUTCOME ASSESSMENT; POISSON DISTRIBUTION; PROPENSITY SCORE; PROPENSITY SCORE CALIBRATION; RISK ASSESSMENT; RISK FACTOR; SURROGACY; VALIDATION STUDY;

BIAS (EPIDEMIOLOGY); COHORT STUDIES; CONFOUNDING FACTORS (EPIDEMIOLOGY); CROSS-SECTIONAL STUDIES; DATA INTERPRETATION, STATISTICAL; EPIDEMIOLOGIC RESEARCH DESIGN; MODELS, STATISTICAL; POISSON DISTRIBUTION; PROPENSITY SCORE; VALIDATION STUDIES AS TOPIC;

EID: 84862497951     PISSN: 00029262     EISSN: 14766256     Source Type: Journal    
DOI: 10.1093/aje/kwr463     Document Type: Article

References (29)

1. Stürmer T, Glynn RJ, Rothman KJ, et al. Adjustments for unmeasured confounders in pharmacoepidemiologic database studies using external information. Med Care. 2007;45(10 suppl 2):S158-S165.
2. Rubin DB. Multiple Imputation for Nonresponse in Surveys. New York, NY: John Wiley & Sons, Inc; 1987.
3. Little RJ. Regression with missing X's: a review. J Am Stat Assoc. 1992;87(420):1227-1237.
4. Little RJA, Rubin DB. Statistical Analysis With Missing Data. 2nd ed. New York, NY: John Wiley & Sons, Inc; 2002.
5. Stürmer T, Schneeweiss S, Avorn J, et al. Adjusting effect estimates for unmeasured confounding with validation data using propensity score calibration. Am J Epidemiol. 2005;162(3):279-289. (Pubitemid 41373532)
6. Stürmer T, Schneeweiss S, Rothman KJ, et al. Performance of propensity score calibration - a simulation study. Am J Epidemiol. 2007;165(10):1110-1118. (Pubitemid 47233723)
7. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70(1):41-55.
8. Rosner B, Willett WC, Spiegelman D. Correction of logistic regression relative risk estimates and confidence intervals for systematic within-person measurement error. Stat Med. 1989;8(9):1051-1069. (Pubitemid 19233679)
9. Rosner B, Spiegelman D, Willett WC. Correction of logistic regression relative risk estimates and confidence intervals for measurement error: the case of multiple covariates measured with error. Am J Epidemiol. 1990;132(4):734-745. (Pubitemid 20344541)
10. Carroll RJ, Ruppert D, Stefanski LA. Measurement Error in Nonlinear Models. London, United Kingdom: Chapman & Hall Ltd; 1995.
11. Wright S. Correlation and causation. J Agric Res. 1921;20:557-585.
12. Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology. 1999;10(1):37-48. (Pubitemid 29014427)
13. Pearl J. Causality. 2nd ed. New York, NY: Cambridge University Press; 2009.
14. GlymourMM, Greenland S. Causal diagrams. In: Rothman KJ, Greenland S, Lash TL, eds. Modern Epidemiology. 3rd ed. Philadelphia, PA: Lippincott Williams & Wilkins; 2008:183-212.
15. Glynn RJ, Knight EL, Levin R, et al. Paradoxical relations of drug treatment with mortality in older persons. Epidemiology. 2001;12(6):682-689. (Pubitemid 33134531)
16. Stürmer T, Schneeweiss S, Brookhart MA, et al. Analytic strategies to adjust confounding using exposure propensity scores and disease risk scores: nonsteroidal antiinflammatory drugs and short-term mortality in the elderly. Am J Epidemiol. 2005;161(9):891-898. (Pubitemid 40577844)
17. Jackson LA, Jackson ML, Nelson JC, et al. Evidence of bias in estimates of influenza vaccine effectiveness in seniors. Int J Epidemiol. 2006;35(2):337-344.
18. McGrath LJ, Kshirsagar AV, Cole SR, et al. Influenza vaccine effectiveness in patients on hemodialysis: an analysis of a natural experiment. Arch Internal Med. 2012;172(7):548-554.
19. Lash TL, Fink AK. Semi-automated sensitivity analysis to assess systematic errors in observational data. Epidemiology. 2003;14(4):451-458. (Pubitemid 40417063)
20. Greenland S. Multiple-bias modelling for analysis of observational data. J R Stat Soc Ser A. 2005;168(2):267-306. (Pubitemid 40427711)
21. Schneeweiss S, Glynn RJ, Tsai EH, et al. Adjusting for unmeasured confounders in pharmacoepidemiologic claims data using external information: the example of COX2 inhibitors and myocardial infarction. Epidemiology. 2005;16(1): 17-24. (Pubitemid 40239274)
22. Greenland S, Lash TL. Bias analysis. In: Rothman KJ, Greenland S, Lash TL, eds. Modern Epidemiology. 3rd ed. Philadelphia, PA: Lippincott Williams & Wilkins; 2008:345-380.
23. Thomas DC, Greenland S. The relative efficiencies of matched and independent sample designs for case-control studies. J Chronic Dis. 1983;36(10):685-697. (Pubitemid 14246890)
24. Greenland S. An introduction to instrumental variables for epidemiologists. Int J Epidemiol. 2000;29(4):722-729. (Pubitemid 30612241)
25. Karlin S, Cameron EC, Chakraborty R. Path analysis in genetic epidemiology: a critique. Am J Hum Genet. 1983;35(4):695-732. (Pubitemid 13007980)
26. Freedman DA. As others see us: a case study in path analysis. J Educ Stat. 1987;12:101-128.
27. Greenland S, Schlesselman JJ, Criqui MH. The fallacy of employing standardized regression coefficients and correlations as measures of effect. Am J Epidemiol. 1986;123(2):203-208. (Pubitemid 16158543)
28. Greenland S, Maclure M, Schlesselman JJ, et al. Standardized regression coefficients: a further critique and review of some alternatives. Epidemiology. 1991;2(5):387-392.
29. Cochran WG. The effectiveness of adjustment by subclassification in removing bias in observational studies. Biometrics. 1968;24(2):295-313.