Methods of covariate selection: Directed acyclic graphs and the change-in-estimate procedure

American Journal of Epidemiology

Volumn 169, Issue 10, 2009, Pages 1182-1190

  Weng, Hsin Yi ^a   Hsueh, Ya Hui ^a   Messam, Locksley L McV ^a   Hertz Picciotto, Irva ^a  

^a 2430 Veterinary Medicine Basic Sciences Building ^*

Author keywords

Bias (epidemiology); Computer simulation; Confounding factors (epidemiology); Epidemiologic methods; Logistic models; Models, statistical; Models, theoretical

Indexed keywords

COMPARATIVE STUDY; GRAPHICAL METHOD; PERFORMANCE ASSESSMENT; POPULATION MODELING;

ALGORITHM; ARTICLE; CHANGE IN ESTIMATE PROCEDURE; COMPUTER SIMULATION; DIRECTED ACYCLIC GRAPH; MATHEMATICAL ANALYSIS; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL;

ALGORITHMS; COMPUTER GRAPHICS; COMPUTER SIMULATION; CONFIDENCE INTERVALS; EPIDEMIOLOGIC METHODS; HUMANS; LOGISTIC MODELS; MODELS, STATISTICAL; MULTIVARIATE ANALYSIS; REGRESSION ANALYSIS; STATISTICS AS TOPIC;

EID: 65949124463     PISSN: 00029262     EISSN: 14766256     Source Type: Journal    
DOI: 10.1093/aje/kwp035     Document Type: Article

References (33)

1. Glymour MM, Weuve J, Berkman LF, et al. When is baseline adjustment useful in analyses of change? An example with education and cognitive change. Am J Epidemiol. 2005;162(3): 267-278.
2. Greenland S. Modeling and variable selection in epidemiologic analysis. Am J Public Health. 1989;79(3): 340-349.
3. Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology. 1999;10(1):37-48.
4. Hernán MA, Hernández-Díaz S, Werler MM, et al. Causal knowledge as a prerequisite for confounding evaluation: an application to birth defects epidemiology. Am J Epidemiol. 2002;155(2):176-184.
5. Maldonado G, Greenland S. Simulation study of confounderselection strategies. Am J Epidemiol. 1993;138(11):923-936.
6. Pearl J. Causal diagrams for empirical research. Biometrika. 1995;82(4):669-688.
7. Robins JM. Data, design, and background knowledge in etiologic inference. Epidemiology. 2001;12(3):313-320.
8. Rothman KJ, Greenland S. Modern Epidemiology. 2nd ed. Philadelphia, PA: Lippincott-Raven; 1998.
9. Nelson MC, Gordon-Larsen P, Adair LS. Are adolescents who were breast-fed less likely to be overweight? Analyses of sibling pairs to reduce confounding. Epidemiology. 2005; 16(2):247-253.
10. Weng HY, Kass PH, Hart LA, et al. Risk factors for unsuccessful dog ownership: An epidemiologic study in Taiwan. Prev Vet Med. 2006;77(1-2):82-95.
11. Messam LL, Kass PH, Chomel BB, et al. The human-canine environment: A risk factor for non-play bites? Vet J. 2008; 177(2):205-215.
12. Miettinen OS, Cook EF. Confounding-essence and detection. Am J Epidemiol. 1981;114(4):593-603.
13. Kleinbaum DG, Kupper LL, Morgenstern H. Epidemiologic Research: Principles and Quantitative Methods. Belmont, CA: Lifetime Learning Publications; 1982.
14. Pearl J. Causality: Models, Reasoning, and Inference. Cambridge, United Kingdom: Cambridge University Press; 2000.
15. Mickey RM, Greenland S. The impact of confounder selection criteria on effect estimation. Am J Epidemiol. 1989;129(1): 125-137.
16. Budtz-Jørgensen E, Keiding N, Grandjean P, et al. Confounder selection in environmental epidemiology: Assessment of health effects of prenatal mercury exposure. Ann Epidemiol. 2007; 17(1):27-35.
17. Sonis J, Hertz-Picciotto I. Accessing the presence of confounding. Fam Med. 1996;28(7):462-463.
18. SAS Institute, Inc. SAS Software for Windows. Version 9.1. Cary, NC: SAS Institute, Inc; 1999.
19. Hegewald J, Pfahlberg A, Uter W. A backwards-manual selection macro for binary logistic regression in SAS® v. 8.02 PROC LOGISTIC procedure. Presented at the 16th Annual North East SAS Users Group Conference (NESUG 2003), Washington, DC, September 2002.
20. Robins JM, Greenland S. The role of model selection in causal inference from nonexperimental data. Am J Epidemiol. 1986; 123(3):392-402.
21. Robinson LD, Jewell NP. Some surprising results about covariate adjustment in logistic regression models. Int Stat Rev. 1991;59(2):227-240.
22. Robinson LD, Dorroh JR, Lien D, et al. The effects of covariate adjustment in generalized linear models. Commun Stat Theory Meth. 1998;27(7):1653-1675.
23. Chatfield C. Model uncertainty, data mining and statistical inference. J R Stat Soc A. 1995;158(part 3):419-466.
24. Greenland S, Robins JM. Identifiability, exchangeability, and epidemiological confounding. Int J Epidemiol. 1986;15(3): 413-419.
25. Gail MH, Wieand S, Piantadosi S. Biased estimates of treatment effect in randomized experiments with non-linear regression and omitted covariates. Biometrika. 1984;71: 431-444.
26. Hauck WW, Neuhaus JM, Kalbfleisch JD, et al. A consequence of omitted covariates when estimating odds ratios. J Clin Epidemiol. 1991;44(1):77-81.
27. Negassa A, Hanley JA. The effect of omitted covariates on confidence interval and study power in binary outcome analysis: A simulation study. Contemp Clin Trials. 2007;28(3): 242-248.
28. Kleinbaum D, Klein M. Logistic Regression: A Self-Learning Text. 2nd ed. New York, NY: Springer; 2002.
29. Greenland S, Thomas DC. On the need for the rare disease assumption in case-control studies. Am J Epidemiol. 1982; 116(3):547-553.
30. Greenland S. Interpretation and choice of effect measures in epidemiologic analyses. Am J Epidemiol. 1987;125(5):761-768.
31. Wacholder S. Binomial regression in GLIM: Estimating risk ratios and risk differences. Am J Epidemiol. 1986;123(1): 174-184.
32. McNutt LA,Wu C Xue X, et al. Estimating the relative risk in cohort studies and clinical trials of common outcomes. Am J Epidemiol. 2003;157(10):940-943.
33. Zou G. A modified Poisson regression approach to prospective studies with binary data. Am J Epidemiol. 2004;159(7): 702-706.