Re: "Modeling Smoking History: A Comparison of Different Approaches"

Hoffmann, Kurt; Bergmann, Manuela M.

doi:10.1093/aje/kwg159

Cited by 15 publications

(18 citation statements)

References 3 publications

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“…Faced with the complexity of the association between duration and intensity and disease risk, investigators may apply nonparametric or semiparametric models, such as splines (3) or generalized additive models (4), or create a single comprehensive smoking index (5,6). However, problems of interpreting multiple characteristics of an exposure remain (7,8). Comparisons of intensity based on odds ratios (OR) for categories of cigarettes per day either adjusted for, or jointly with, duration of smoking are influenced by differences in total exposure to cigarette smoke.…”

Section: Introductionmentioning

confidence: 99%

Cigarette Smoking and Lung Cancer: Modeling Total Exposure and Intensity

Lubin

Caporaso

2006

Cancer Epidemiology, Biomarkers &Amp; Prevention

173

132

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Investigators typically analyze cigarette smoking using smoking duration and intensity (number of cigarettes smoked per day) as risk factors. However, odds ratios (OR) for categories of intensity either adjusted for, or jointly with, duration of smoking may be distorted by differences in total pack-years of exposure to cigarette smoke. To study effects of intensity, we apply a linear excess OR model to compare total exposure delivered at low intensity for a long period of time with an equal total exposure delivered at high intensity for a short period of time to data from a large case-control study of lung cancer. The excess OR per pack-year increases with intensity for subjects who smoke V20 cigarettes per day and decreases with intensity for subjects who smoke >20 cigarettes per day.The intensity patterns are homogeneous by histologic type of lung cancer, suggesting that observed differences in risks by histologic type are related to total smoking exposure or smoking duration and not smoking intensity. At lower smoking intensities, there is an ''exposure enhancement'' effect such that for equal total exposure, the excess OR per pack-year increases with intensity. At higher smoking intensities, there is a ''reduced potency'' or ''wasted exposure'' effect such that for equal total exposure, the excess OR per pack-year decreases with intensity (i.e., smoking at a lower intensity for longer duration is more deleterious than smoking at a higher intensity for shorter duration). (Cancer Epidemiol Biomarkers Prev 2006;15(3):517-23)

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Section: Introductionmentioning

confidence: 99%

Cigarette Smoking and Lung Cancer: Modeling Total Exposure and Intensity

Lubin

Caporaso

2006

Cancer Epidemiology, Biomarkers &Amp; Prevention

173

132

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“…While cigarette smoking is easily quantifiable in terms of pack-years ((average daily number of cigarettes smoked/20 cigarettes per pack) × years of smoking), previous work has shown that pack-years alone may be an overly simplistic means of modelling smoking exposure, and non-linear relations may be present 1 2. Many COPD genetic association analyses model smoking effects by including a pack-years term in a regression model, which assumes a linear relation between pack-years and forced expiratory volume in 1 s (FEV 1 ) or, in the case of logistic regression for COPD status, a linear relation between pack-years and the log odds of having COPD.…”

mentioning

confidence: 99%

Impact of non-linear smoking effects on the identification of gene-by-smoking interactions in COPD genetics studies

Castaldi

DeMeo

Hersh

et al. 2010

Thorax

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Background The identification of gene-by-environment interactions is important to understand the genetic basis of chronic obstructive pulmonary disease (COPD). Many COPD genetic association analyses assume a linear relationship between pack-years of smoking exposure and FEV1; however, this assumption has not been evaluated empirically in cohorts with a wide spectrum of COPD severity. Methods We examined the relationship between FEV1 and pack-years of smoking exposure in 4 large cohorts assembled for the purpose of identifying genetic associations with COPD. Using data from the Alpha-1 Antitrypsin Genetic Modifiers Study, we compared the accuracy and power of two different approaches to model smoking by performing a simulation study of a genetic variant with a range of gene-by-smoking interaction effects. Results We identified nonlinear relationships between smoking and FEV1 in 4 large cohorts. We demonstrated that in most situations where the relationship between pack-years and FEV1 is nonlinear, a piecewise-linear approach to model smoking and gene-by-smoking interactions is preferable to the commonly used total pack-years approach. We applied the piecewise linear approach to a genetic association analysis of the PI*Z allele in the Norway case-control cohort and identified a potential PI*Z-by-smoking interaction (p=0.03 for FEV1 analysis, p= 0.01 for COPD susceptibility analysis). Conclusion In study samples with subjects having a wide range of COPD severity, a nonlinear relationship between pack-years of smoking and FEV1 is likely. In this setting, approaches that account for this nonlinearity can be more powerful and less-biased than the commonly-used approach of using total pack-years to model the smoking effect.

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“…interaction between duration and time since cessation [11,12].For all these reasons, it would be advantageous to represent all the relevant aspects of smoking history by a single aggregate measure [13]. Recently, Hoffman and colleagues have proposed such an aggregate measure that incorporates total duration of smoking, time since smoking cessation, average number of cigarettes smoked per day (intensity), and selected interactions between these variables [14][15][16]. This comprehensive smoking index (CSI) can be easily computed and incorporated in any regression model.…”

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confidence: 99%

Modelling smoking history using a comprehensive smoking index: application to lung cancer

Leffondré

Abrahamowicz

Xiao

et al. 2006

Statistics in Medicine

106

135

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The mathematical representation of smoking history is an important tool in analysis of epidemiological and clinical data. Hoffmann and colleagues recently proposed a single aggregate measure of smoking exposure that incorporates intensity, duration, and time since cessation. This comprehensive smoking index (CSI), which may be incorporated in any regression model, depends on a half-life (tau) and a lag (delta) parameters that have to be fixed a priori, or estimated by maximizing the fit. The CSI has not previously been used for analysis of cancer data. Following some preliminary results on smoking and lung cancer, the authors proposed a new version of the CSI for lung cancer. The aim of this study was to investigate the performance of the original and the new versions of the CSI in the analysis of three data sets from two case-control studies of lung cancer undertaken in Montreal, in 1979-1985 in males, and in 1996-2000 in both males and females. The estimates of tau and delta for both versions of the CSI were similar across data sets. The new version of the CSI fitted the three data sets systematically although moderately better than the original version, and at least as well as other representations of lifetime smoking history that used separate variables for time since cessation and cumulative amount of cigarettes smoked. The results suggest that the CSI may be an attractive and parsimonious alternative to conventional modelling of different aspects of smoking history for lung cancer.

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Re: "Modeling Smoking History: A Comparison of Different Approaches"

Cited by 15 publications

References 3 publications

Cigarette Smoking and Lung Cancer: Modeling Total Exposure and Intensity

Cigarette Smoking and Lung Cancer: Modeling Total Exposure and Intensity

Impact of non-linear smoking effects on the identification of gene-by-smoking interactions in COPD genetics studies

Modelling smoking history using a comprehensive smoking index: application to lung cancer

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