Multivariable analysis: a primer for readers of medical research.
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.
Katz
Mitchell H
MH
San Francisco Department of Public Health, San Francisco, California, USA. Mitch.Katz@sfdph.org
D035843:Biomedical Research; D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D006801:Humans; D016014:Linear Models; D016015:Logistic Models; D015999:Multivariate Analysis; D016016:Proportional Hazards Models; D012307:Risk Factors