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To better understand the contribution of cigarette smoking, and its changing role in lung cancer, this article provides an introduction to a special issue of Risk Analysis, which considers the relationship between smoking and lung cancer death rates during the period 1975–2000 for U.S. men and women aged 30–84 years. Six models are employed, which are part of a consortium of lung cancer modelers funded by National Cancer Institute's Cancer Intervention and Surveillance Modeling Network (CISNET). Starting with birth‐cohort‐specific smoking histories derived from National Health Interview Surveys, three scenarios are modeled: Actual Tobacco Control (observed trends in smoking), Complete Tobacco Control (a counterfactual lower bound on smoking rates that could have been achieved had all smoking ceased after the first Surgeon General's report in 1964), and No Tobacco Control (a counterfactual upper bound on smoking rates if smoking patterns that prevailed before the first studies in the 1950s began to inform the public about the hazards of smoking). Using these three scenarios and the lung cancer models, the number and percentage of lung cancer deaths averted from 1975–2000, among all deaths that could have been averted if tobacco control efforts been immediate and perfect, can be estimated. The variability of the results across multiple models provides a measure of the robustness of the results to model assumptions and structure. The results provide not only a portrait of the achieved impact of tobacco control on lung cancer mortality, but also the bounds of what still needs to be achieved.

Abstract
Joinpoint regression can model trends in time-specific estimates from aggregated data. These methods have been developed mainly for nonsurvey data such as cancer registry data assuming that the time-specific estimates are uncorrelated from time point to time point. This independence assumption can be violated for trends in time-specific estimates from complex survey samples due to using the same primary sampling units across time and, therefore, the full variance–covariance matrix of the time-specific estimates should be incorporated into the regression model fitting. This article extends these joinpoint methods for analyzing complex survey data within the National Cancer Institute's Joinpoint software and empirically compares the extended method to existing methods for analyses of time trends in three surveys.

We present methods for estimating five‐year birth‐cohort‐specific trends in smoking behavior for individuals born between 1910 and 1984. We combine cross‐sectional survey data on smoking behavior from the National Health Interview Surveys (NHIS) conducted between 1965 and 2001 into a single data set. The cumulative incidence of smoking by year of age and calendar year is constructed for each birth cohort from this data set and the effect of differential mortality on ever smoking prevalence is adjusted by modeling the ever smoking prevalence of each cohort for each survey year and back extrapolating that effect to age 30. Cumulative incidence is then scaled to match the ever smoking prevalence at age 30. Survival analyses generate the cumulative cessation among ever smokers across year of age and calendar year and are used to estimate current smoking prevalence. Data from Substance Abuse and Mental Health Services Administration (SAMHSA) National Survey on Drug Use and Health is used to divide those initiating smoking into quintiles of number of cigarettes smoked per day (CPD) and the mean CPD for each quintile in each calendar year is estimated from the NHIS data. For five‐year birth cohorts of white, african‐american, Hispanic and all race/ethnicity groupings of males and females born between 1910 and 1984, estimates are provided for prevalence of current and ever smoking, incidence of cessation, incidence of initiation, and the distribution of smoking duration and CPD for each calendar year and each single year of age through the year 1999. We believe that we are the first to provide birth‐cohort‐specific estimates of smoking behaviors for the U.S. population that include distributions of duration of smoking and number of cigarettes per day. These additional elements substantively enhance the utility of these estimates for estimating lung cancer risks.

The purpose of this study was to develop life tables by smoking status removing lung cancer as a cause of death. These life tables are inputs to studies that compare the effectiveness of lung cancer treatments or interventions, and provide a way to quantify time until death from causes other than lung cancer. The study combined actuarial and statistical smoothing methods, as well as data from multiple sources, to develop separate life tables by smoking status, birth cohort, by single year of age, and by sex. For current smokers, separate life tables by smoking quintiles were developed based on the average number of cigarettes smoked per day by birth cohort. The end product is the creation of six non‐lung‐cancer life tables for males and six tables for females: five current smoker quintiles and one for never smokers. Tables for former smokers are linear combinations of the appropriate table based on the current smoker quintile before quitting smoking and the never smoker probabilities, plus added covariates for the smoking quit age and time since quitting.