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Abstract
Confounding is a major issue in observational epidemiological studies. This paper describes two postestimation commands for assessing confounding effects. One command (confall) displays and plots all possible effect estimates against one of p-value, Akaike information criterion, or Bayesian information criterion. This computing-intensive procedure allows researchers to inspect the variability of the effect estimates from various possible models. Another command (chest) uses a stepwise approach to identify variables that have substantially changed the effect estimate. Both commands can be used after most common estimation commands in epidemiological studies, such as logistic regression, conditional logistic regression, Poisson regression, linear regression, and Cox proportional hazards models.
References
| Akaike, H. 1974. A new look at statistical model identification. IEEE Transactions on Automatic Control AC-19: 716–723. Google Scholar | |
| Debanne, S. M. , and Sokol, R. J. 1986. Adjusting for confounding with statistical software packages. American Journal of Perinatology 3: 133–134. Google Scholar | |
| Grayson, D. A. 1987. Confounding confounding. American Journal of Epidemiology 126: 546–553. Google Scholar | |
| Greenland, S. , and Robins, J. M. 1985. Confounding and misclassification. American Journal of Epidemiology 122: 495–506. Google Scholar | |
| Greenland, S. 1986. Identifiability, exchangeability, and epidemiological confounding. International Journal of Epidemiology 15: 413–419. Google Scholar | |
| Kleinbaum, D. G. , Kupper, L. L. , and Morgenstern, H. 1982. Epidemiologic Research: Principles and Quantitative Methods. New York: Nostrand Reinhold. Google Scholar | |
| Miettinen, O. 1974. Confounding and effect-modification. American Journal of Epidemiology 100: 350–353. Google Scholar | |
| Rothman, K. , and Greenland, S. 1998. Modern Epidemiology. 2nd ed. Philadelphia: Lippincott–Raven. Google Scholar | |
| Schlesselman, J. J. 1978. Assessing effects of confounding variables. American Journal of Epidemiology 108: 3–8. Google Scholar | |
| Schwartz, J. , and Coull, B. A. 2003. Control for confounding in the presence of measurement error in hierarchical models. Biostatistics 4: 539–553. Google Scholar | |
| Schwarz, G. 1978. Estimating the dimension of a model. Annals of Statistics 6: 461–464. Google Scholar | |
| Steenland, K. , and Greenland, S. 2004. Monte Carlo sensitivity analysis and Bayesian analysis of smoking as an unmeasured confounder in a study of silica and lung cancer. American Journal of Epidemiology 160: 384–392. Google Scholar | |
| Sturmer, T. , Schneeweiss, S. , Avorn, J. , and Glynn, R. J. 2005. Adjusting effect estimates for unmeasured confounding with validation data using propensity score calibration. American Journal of Epidemiology 162: 279–289. Google Scholar | |
| Wang, Z. 2000. sbe27: Assessing confounding effects in epidemiological studies. Stata Technical Bulletin 49: 12–15. Reprinted in Stata Technical Bulletin Reprints, vol. 9, pp. 134–138. College Station, TX: Stata Press. Google Scholar | |
| Weinberg, C. R. 1993. Toward a clearer definition of confounding. American Journal of Epidemiology 137: 1–8. Google Scholar |
