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Review
. 2013 Mar 19;185(5):401-6.
doi: 10.1503/cmaj.120592. Epub 2013 Feb 11.

Adjustment for continuous confounders: an example of how to prevent residual confounding

Rolf H H Groenwold  1 , Olaf H Klungel, Douglas G Altman, Yolanda van der Graaf, Arno W Hoes, Karel G M Moons; PROTECT WP2 (Pharmacoepidemiological Research on Outcomes of Therapeutics by a European Consortium, Work Programme 2 [Framework for pharmacoepidemiology studies])
Collaborators, Affiliations
Review

Adjustment for continuous confounders: an example of how to prevent residual confounding

Rolf H H Groenwold et al. CMAJ. .
No abstract available

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Figures

Figure 1:
Figure 1:
Comparison of methods to model the relation between a continuous confounder (use of health care) and outcome (death) in a study of influenza vaccination. Use of health care was defined as the number of contacts with general practitioners in the 12 months before the influenza vaccination. Use of health care was dichotomized at the median (i.e., 12 contacts in the 12 months before the vaccination). Use of health care was divided into 5 categories based on quintiles; cut-values were 5, 9, 14 and 22 contacts in the 12 months before the vaccination. CI = confidence interval.
Figure 2:
Figure 2:
Functional relation between the continuous confounder use of health care and death in a study of influenza vaccination. GP = general practitioner.
Figure 3:
Figure 3:
Functional relation between the continuous confounder age and cardiovascular death in a study of cardiovascular risk factors.
Figure 4:
Figure 4:
Comparison of methods to model the relation between a continuous confounder (age) and outcome (cardiovascular death) in a study of cardiovascular risk factors (notably smoking). Age was dichotomized at the median (i.e., 56 yr). Age was divided into 5 categories based on quintiles; cut-values were 45, 53, 60 and 68 years. CI = confidence interval.
See this image and copyright information in PMC

References

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    1. Steyerberg EW. Clinical prediction models: a practical approach to development, validation and updating. New York: Springer; 2009
    1. Harrell FE., Jr Regression modelling strategies: with applications to linear models, logistic regression, and survival analysis. New York: Springer; 2001. p. 16–26
    1. Royston P, Moons KG, Altman DG, et al. Prognosis and prognostic research: developing a prognostic model. BMJ 2009;338:b604. - PubMed
    1. Groenwold RHH, van Deursen AM, Hoes AW, et al. Poor quality of reporting confounding bias in observational intervention studies: a systematic review. Ann Epidemiol 2008;18:746–51 - PubMed

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