Confounders and conditioning of analyses

Idea: Statistical associations between any two variables generally vary depending on the values taken by other “confounding” variables. We need to take this dependency (or conditionality) into account when using our analyses to make predictions or hypothesize about causes, but how do we decide which variables are relevant and real confounders?

In cases such as the following, a range of ways can be seen for adjusting for confounding variables (which includes age-standardization).  The questions for students explore that as well as controversies or discordant views about how to do adjustment and eliminate confounding.

Immunization levels (Egede): Note the conclusion about racial/ethnic inequality even after adjusting for other variables thought to correlate with race/ethnicity. Do you agree with the three implications p. 326ff) drawn from the results?

SES gradients in disease (Krieger): The abstract states that “for virtually all outcomes, risk increased with CT [census tract] poverty, and when we adjusted for CT poverty, racial/ethnic disparities were substantially reduced.” Where can the result of adjustment be seen in the paper?

Hormone replacement therapy (Prentice vs. Petitti): Notice the adjustments used by the first paper that bring the clinical component of the WHI hormone replacement trial into line with the observational component. Do Pettiti acknowledge and rebut this in concluding that it was wrong to think that hormone therapy prevents CV disease?

Birth weight and blood pressure (Huxley vs. Davies): Along with Huxley et al’s general argument that the birthweight-adult blood pressure association may well be an artifact of selective publication of studies with small sample size, they criticise the adjustment of the association for adult weight. (In other words, the association holds for people in the same stratum or slice of weight.) Try to form an opinion about whether you agree or disagree with such an adjustment. Davies et al. provide counter-evidence to Huxley et al. — how does their study differ in methods, results, and interpretation?

Control at work and mortality (Davey-Smith 1997): This simple study shows that “control at work” is not the cause of SES gradients in health outcomes. What method(s) do they use to undermine previous claims about control at work?

Mendelian randomization to analyze environmental exposures (Davey-Smith & Ebrahim 2007): The approach introduced in this paper is cutting edge “epidemiology in the age of genomics” and has led to funding of a major new Research Center under Davey-Smith at Bristol. I suggest that you summarize for yourself the logic of this approach so you can explain it to someone who’s never heard of it.

(This post continues a series laying out a sequence of basic ideas in thinking like epidemiologists, especially epidemiologists who pay attention to possible social influences on the development and unequal distribution of diseases and behaviors in populations [see first post in series and contribute to open-source curriculum].)


Davey-Smith, G. and S. Harding (1997). “Is control at work the key to socioeconomic gradients in mortality?” Lancet 350: 1369-1370.

Davey-Smith, G. and S. Ebrahim (2007). “Mendelian randomization: Genetic variants as instruments for strengthening causal influences in observational studies. Pp 336-366 in Weinstein, M., Vaupel, J. W., Wachter, K.W. (eds) Biosocial Surveys. Washington, DC, National Academies Press.

Davies, A., G. Davey-Smith, et al. (2006). “Association between birth weight and blood pressure is robust, amplifies with age, and may be underestimated.” Hypertension 48: 431-436.

Egede, L. E. and D. Zheng (2003). “Racial/Ethnic Differences in Adult Vaccination Among Individuals With Diabetes.” American Journal of Public Health 93(2): 324-329.

Hernan, M. A. (2002). “Causal Knowledge as a Preequisite for Confounding Evaluation: An Application to Birth Defects Epidemiology.” American Journal of Epidemiology 155: 176-184.

Huxley, R., A. Neil, et al. (2002). “Unravelling the fetal origins hypothesis: is there really an inverse association between birthweight and subsequent blood pressure?” Lancet 360(9334): 659-65.

Lawlor, D. A., G. Davey-Smith, et al. (2004). “Those confounded vitamins: what can we learn from the differences between observational versus randomised trial evidence?” The Lancet 363: 1724-1726.

Lynch, J. (2007) Relevant Risk, Revolution and Revisiting Rose – Causes of Population Levels and Social Inequalities in Health.

Petitti, D. B. and D. A. Freedman (2005). “Invited Commentary: How Far Can Epidemiologists Get with Statistical Adjustment?” American Journal of Epidemiology 162: 415-418.

Prentice, R. L., R. Langer, et al. (2005). “Combined Postmenopausal Hormone Therapy and Cardiovascular Disease: Toward Resolving the Discrepancy between Observational Studies and the Women’s Health Initiative Clinical Trial.” American Journal of Epidemiology 162(5): 404-414.


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