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Benchmarking Lung Cancer Mortality Rates in Current and Former Smokers^*

^* From the Memorial Sloan-Kettering Cancer Center (Drs. Bach, Elkin, Kattan, and Begg), New York, NY; Istituto Nazionale Tumori (Dr. Pastorino), Milan, Italy; the Weill Medical School (Dr. Mushlin), Cornell University, New York, NY; and the International Association for Research on Cancer (Drs. Bach and Parkin), Lyon, France.

Correspondence to: Peter B. Bach, MD, FCCP, Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Box 221, New York, NY 10021; e-mail: bachp{at}mskcc.org

Study objectives: To develop and validate a model for estimating the risk of lung cancer death in current and former smokers. The model is intended for use in analyzing a population of subjects who are undergoing lung cancer screening or receiving lung cancer chemoprevention, to determine whether the intervention has altered lung cancer mortality.

Design/setting/patients: Model derivation was based on analyses of the placebo arm of the Carotene and Retinol Efficacy Trial. Model validation was based on analyses of three other longitudinal cohorts.

Measurements: Observed and predicted number of deaths due to lung cancer.

Results: In internal validation, the model was highly concordant and well calibrated. In external validation, the model predictions were similar to what was observed in all of the validation analyses. The predicted and observed deaths within 6 years were very similar when assessed in the Johns Hopkins Hospital trial of chest radiography and sputum cytology screening (176 predicted, 184 observed, p = 0.53), the Memorial Sloan-Kettering Cancer Center trial of chest radiography and sputum cytology screening (108 predicted, 114 observed, p = 0.57), and the National Health and Nutrition Evaluation Survey part I (24 predicted, 21 observed, p = 0.52).

Conclusions: The number of lung cancer deaths in a population of current or former smokers can be accurately predicted, making model-based evaluations of prevention and early detection interventions a useful adjunct to definitive randomized trials. We illustrate this potential use with a small example.

Key Words: Cancer screening • CT • logistic models • lung cancer • risk assessment

This article has been cited by other articles:

				P. B. Bach, J. R. Jett, U. Pastorino, M. S. Tockman, S. J. Swensen, and C. B. Begg Computed Tomography Screening for Lung Cancer Reply JAMA, August 1, 2007; 298(5): 515 - 516. [Full Text] [PDF]

				P. B. Bach, J. R. Jett, U. Pastorino, M. S. Tockman, S. J. Swensen, and C. B. Begg Computed Tomography Screening and Lung Cancer Outcomes JAMA, March 7, 2007; 297(9): 953 - 961. [Abstract] [Full Text] [PDF]

				J. Siemiatycki Synthesizing the Lifetime History of Smoking Cancer Epidemiol. Biomarkers Prev., October 1, 2005; 14(10): 2294 - 2295. [Full Text] [PDF]

				J. L. Mulshine New Developments in Lung Cancer Screening J. Clin. Oncol., May 10, 2005; 23(14): 3198 - 3202. [Abstract] [Full Text] [PDF]

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