Interaction between continuous variables in logistic regression model / 中华流行病学杂志

Rothman argued that interaction estimated as departure from additivity better reflected the biological interaction. In a logistic regression model, the product term reflects the interaction as departure from multiplicativity. So far, literature on estimating interaction regarding an additive scale using logistic regression was only focusing on two dichotomous factors. The objective of the present report was to provide a method to examine the interaction as departure from additivity between two continuous variables or between one continuous variable and one categorical variable.We used data from a lung cancer case-control study among males in Hong Kong as an example to illustrate the bootstrap re-sampling method for calculating the corresponding confidence intervals.Free software R (Version 2.8.1) was used to estimate interaction on the additive scale.

Study on the interaction under logistic regression modeling / 中华流行病学杂志

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 muhiplicative 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 logistie model in SPSS.We then introduce an Excel spreadsheet set up by Tomas Andersson to calculate the indices of interaction on an additive scale and the corresponding confidence intervals.The results can be used as reference by epidemiologists to assess the biologic interaction between factors.The proposed method is convenient and the Excel spreadsheet is available online for free.