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 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.