Bias Formulas for External Adjustment and Sensitivity Analysis of Unmeasured Confounders

Annals of Epidemiology

Volumn 18, Issue 8, 2008, Pages 637-646

  Arah, Onyebuchi A ^a,b   Chiba, Yasutaka ^b,c   Greenland, Sander ^b,d  

^a UNIVERSITY OF AMSTERDAM   (Netherlands)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c KYOTO UNIVERSITY SCHOOL OF PUBLIC HEALTH   (Japan)

^d UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Bias; Bias Adjustment; Confounding; Epidemiologic Methods; Odds Ratio; Risk Difference; Risk Ratio; Sensitivity Analysis; Unmeasured Confounders

Indexed keywords

ARTICLE; ATTRIBUTABLE RISK; EXTERNAL BIAS; MATHEMATICAL COMPUTING; OUTCOME ASSESSMENT; PREVALENCE; PRIORITY JOURNAL; RISK FACTOR; SENSITIVITY ANALYSIS;

BIAS (EPIDEMIOLOGY); CASE-CONTROL STUDIES; CONFOUNDING FACTORS (EPIDEMIOLOGY); EPIDEMIOLOGIC METHODS; HUMANS; MODELS, STATISTICAL; MONTE CARLO METHOD; ODDS RATIO; SENSITIVITY AND SPECIFICITY;

EID: 47349085647     PISSN: 10472797     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.annepidem.2008.04.003     Document Type: Article

References (28)

1. In: Rothman K.J., Greenland S., and Lash T.L. (Eds). Modern epidemiology. 3rd ed. (2008), Lippincott, Philadelphia
2. Cornfield J., Haenszel W., Hammond E.C., Lilienfeld A.M., Shimkin M.B., and Wynder E.L. Smoking and lung cancer: recent evidence and a discussion of some questions. J Natl Cancer Inst 22 (1959) 173-203
3. Bross I.D. Spurious effects from an extraneous variable. J Chronic Dis 19 (1966) 637-647
4. Bross I.D. Pertinency of an extraneous variable. J Chronic Dis 20 (1967) 487-495
5. Schlesselman J.J. Assessing effects of confounding variables. Am J Epidemiol 108 (1978) 3-8
6. Breslow N.E., and Day N.E. Statistical methods in cancer research. Volume 1: The analysis of case-control studies. Chapter 3 (1980), International Agency for Research on Cancer, Lyon
7. Yanagawa T. Case-control studies: assessing the effect of a confounding factor. Biometrika 71 (1984) 191-194
8. Axelson O., and Steenland K. Indirect methods of assessing the effects of tobacco use in occupational studies. Am J Ind Med 13 (1988) 105-118
9. Gail M.H., Wacholder S., and Lubin J.H. Indirect corrections for confounding under multiplicative and additive risk models. Am J Ind Med 13 (1988) 119-130
10. Flanders W.D., and Khoury M.J. Indirect assessment of confounding: graphic description and limits on effect of adjusting for covariates. Epidemiology 1 (1990) 239-246
11. Lin D.Y., Psaty B.M., and Kronmal R.A. Assessing the sensitivity of regression results to unmeasured confounders in observational studies. Biometrics 54 (1998) 948-963
12. Hernán M., and Robins J.M. Assessing the sensitivity of regression results to unmeasured confounders in observational studies. [letter]. Biometrics 55 (1999) 1316-1317
13. Rosenbaum P.R. Observational studies. 2nd ed. (2002), Springer-Verlag, New York
14. Greenland S. Quantifying biases in causal models: classical confounding vs collider-stratification bias. Epidemiology 14 (2003) 300-306
15. MacLehose R.F., Kaufman S., Kaufman J.S., and Poole C. Bounding causal effects under uncontrolled confounding using counterfactuals. Epidemiology 16 (2005) 548-555
16. Greenland S. The impact of prior distributions for uncontrolled confounding and response bias: a case study of the relation of wire codes and magnetic fields to childhood leukemia. J Am Stat Assoc 98 (2003) 47-54
17. Greenland S. Multiple-bias modeling for analysis of observational data (with discussion). [Series A]. J R Stat Soc 168 (2005) 267-308
18. Steenland K., and Greenland S. Monte Carlo sensitivity analysis and Bayesian analysis of smoking as an unmeasured confounder in a study of silica and lung cancer. Am J Epidemiol 160 (2004) 384-392
19. Greenland S., Robins J.M., and Pearl J. Confounding and collapsibility in causal inference. Stat Sci 14 (1999) 29-46
20. Kitagawa E.M. Components of a difference between two rates. J Am Stat Assoc 50 (1955) 1168-1194
21. Meinert C.L., Knatterud G.L., Prout T.E., and Klimt C.R. A study of the effects of hypoglycemic agents on vascular complications in patients with adult-onset diabetes. II. Mortality results. Diabetes 19 Suppl (1970) 747-830
22. Brenner H., and Blettner M. Controlling for continuous confounders in epidemiologic research. Epidemiology 8 (1997) 429-434
23. Brenner H. A potential pitfall in control of covariates in epidemiologic studies. Epidemiology 9 (1998) 68-71
24. Power C., and Elliot J. Cohort profile: 1958 British birth cohort (National Child Development Study). Int J Epidemiol 35 (2006) 34-41
25. Stürmer T., Schneeweiss S., Rothman K.J., Avorn J., and Glynn R. Performance of propensity score calibration - a simulation study. Am J Epidemiol 165 (2007) 1110-1118
26. Greenland S., Pearl J., and Robins J.M. Causal diagrams for epidemiologic research. Epidemiology 10 (1999) 37-48
27. Pearl J. Causality: models, reasoning and inference (2000), Cambridge University Press, Cambridge
28. Brookhart M.A., Schneeweiss S., Rothman K.J., Glynn R.J., Avorn J., and Stürmer T. Variable selection for propensity score models. Am J Epidemiol 163 (2006) 1149-1156