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Multivariable Analysis: A Primer for Readers of Medical Research

Mitchell H. Katz, MD
[+] Article, Author, and Disclosure Information

From San Francisco Department of Public Health, San Francisco, California.

Potential Financial Conflicts of Interest: None disclosed.

Requests for Single Reprints: Mitchell H. Katz, MD, 101 Grove Street, Room 308, San Francisco, CA 94102; e-mail, Mitch.Katz@sfdph.org.

Ann Intern Med. 2003;138(8):644-650. doi:10.7326/0003-4819-138-8-200304150-00012
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Many clinical readers, especially those uncomfortable with mathematics, treat published multivariable models as a black box, accepting the author's explanation of the results. However, multivariable analysis can be understood without undue concern for the underlying mathematics. This paper reviews the basics of multivariable analysis, including what multivariable models are, why they are used, what types exist, what assumptions underlie them, how they should be interpreted, and how they can be evaluated. A deeper understanding of multivariable models enables readers to decide for themselves how much weight to give to the results of published analyses.


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Figure 1.
Relationship among risk factor, confounder, and outcome.(1)

Reprinted with permission from Cambridge University Press, Cambridge, United Kingdom .

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Figure 2.
Multivariable association between four risk factors and coronary artery disease.

The thicker arrow indicates that smoking is associated with coronary heart disease, even after adjustment for male sex, poverty, and sedentary lifestyle.

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Figure 3.
Potential confounders of the relationship between periodontitis and coronary heart disease.
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Figure 4.
An interaction effect.solid lines(1)

The effect of the risk factor on outcome ( ) differs depending on the value of the interaction variable. The dotted line is the average of the two effects. Adapted with permission from Cambridge University Press, Cambridge, United Kingdom .

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