Red Meat and Lung Cancer Risk in Heavy Smokers
Red Meat and Lung Cancer Risk in Heavy Smokers
Correlation between potential baseline covariates and average daily intake of selected foods (including beverages), food groups and the aMED score was calculated using Pearson correlation coefficients. Cumulative incidence curves of lung cancer according to quartiles of food or food-group consumptions or categories of the aMED score were plotted using the Kaplan–Meier method. Difference between curves was assessed with the log-rank test. Multivariable Cox proportional hazards regression was used to analyse the association between food intake and lung cancer risk, adjusting for baseline lung cancer risk probability and total energy intake. Models included dummy variables to represent missing values for some co-variables (education, body mass index). Energy adjustment was carried out using both the standard and the nutrient-density method. Baseline lung cancer risk probability was calculated for each individual, using information on age, sex, smoking history (smoking duration, average daily cigarettes consumption, years of cessation) and asbestos exposure. We carried out alternative models adjusted for each single variable plus additional variables such as education. Since the results were comparable, we decided to present results from the simplest model. P-values for trend were calculated using the quartile median values. Analysis was carried out with the SAS software version 8.2 (Cary, NC). All P-values are two-sided.
Statistical Analysis
Correlation between potential baseline covariates and average daily intake of selected foods (including beverages), food groups and the aMED score was calculated using Pearson correlation coefficients. Cumulative incidence curves of lung cancer according to quartiles of food or food-group consumptions or categories of the aMED score were plotted using the Kaplan–Meier method. Difference between curves was assessed with the log-rank test. Multivariable Cox proportional hazards regression was used to analyse the association between food intake and lung cancer risk, adjusting for baseline lung cancer risk probability and total energy intake. Models included dummy variables to represent missing values for some co-variables (education, body mass index). Energy adjustment was carried out using both the standard and the nutrient-density method. Baseline lung cancer risk probability was calculated for each individual, using information on age, sex, smoking history (smoking duration, average daily cigarettes consumption, years of cessation) and asbestos exposure. We carried out alternative models adjusted for each single variable plus additional variables such as education. Since the results were comparable, we decided to present results from the simplest model. P-values for trend were calculated using the quartile median values. Analysis was carried out with the SAS software version 8.2 (Cary, NC). All P-values are two-sided.
Source...