Am J Epidemiol 2002; 156:813-823.
Copyright © 2002 by Johns
Hopkins Bloomberg School of Public Health
ORIGINAL CONTRIBUTIONS |
Modeling Smoking History: A Comparison of Different Approaches
1 Department of Epidemiology and Biostatistics, Faculty of Medicine, McGill University, Montreal, Quebec, Canada.
2 Division of Clinical Epidemiology, The Montreal General Hospital, Montreal, Quebec, Canada.
3 Department of Epidemiology and Biostatistics, INRS–Institut Armand-Frappier, Université du Québec, Laval-des-Rapides, Quebec, Canada.
4 Canada Research Chair in Environmental Epidemiology, Université de Montréal, Montreal, Quebec, Canada.
The impact of cigarette smoking on various diseases is studied frequently in epidemiology. However, there is no consensus on how to model different aspects of smoking history. The aim of this investigation was to elucidate the impact of several decisions that must be made when modeling smoking variables. The authors used data on lung cancer from a case-control study undertaken in Montreal, Quebec, Canada, in 1979–1985. The roles of smoking status, intensity, duration, cigarette-years, age at initiation, and time since cessation were investigated using time-dependent variables in an adaptation of Cox’s model to case-control data. The authors reached four conclusions. 1) The estimated hazard ratios for current and ex-smokers depend strongly on how long subjects are required to not have smoked to be considered "ex-smokers." 2) When the aim is to estimate the effect of continuous smoking variables, a simple approach can be used (and is proposed) to separate the qualitative difference between never and ever smokers from the quantitative effect of smoking. 3) Using intensity and duration as separate variables may lead to a better model fit than using their product (cigarette-years). 4) When estimating the effects of time since cessation or age at initiation, it is still useful to use cigarette-years, because it reduces multicollinearity.
case-control studies; epidemiologic methods; multicollinearity; multivariate analysis; neoplasms; proportional hazard; smoking; time-dependent covariate
Abbreviations: Abbreviation: AIC, Akaike’s Information Criterion.
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