Study on the interaction under logistic regression modeling

When study on epidemiological causation is carried out, logistic regression has been commonly used to estimate the independent effects of risk factors, as well as to examine possible interactions among individual risk factor by adding one or more product terms to the regression model. In logistic or...

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Bibliographic Details
Published inZhōnghuá liúxíngbìng zázhì Vol. 29; no. 9; p. 934
Main Authors Qiu, Hong, Yu, Ignatius Tak-Sun, Wang, Xiao-Rong, Fu, Zhen-Ming, Tse, Shelly Lap Ah
Format Journal Article
LanguageChinese
Published China 01.09.2008
Subjects
Case-Control Studies
Confounding Factors (Epidemiology)
Female
Humans
Logistic Models
Lung Neoplasms - epidemiology
Software
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Summary:When study on epidemiological causation is carried out, logistic regression has been commonly used to estimate the independent effects of risk factors, as well as to examine possible interactions among individual risk factor by adding one or more product terms to the regression model. In logistic or Cox's regression model, the regression coefficient of the product term estimates the interaction on a multiplicative scale while statistical significance indicates the departure from multiplicativity. Rothman argues that when biologic interaction is examined, we need to focus on interaction as departure from additivity rather than departure from multiplicativity. He presents three indices to measure interaction on an additive scale or departure from additivity, using logarithmic models such as logistic or Cox's regression model. In this paper, we use data from a case-control study of female lung cancer in Hong Kong to calculate the regression coefficients and covariance matrix of logistic model in SPSS. We then in
ISSN:0254-6450