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.
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.
Authors: Denong Wang; Laila Dafik; Rosalie Nolley; Wei Huang; Russell D Wolfinger; Lai-Xi Wang; Donna M Peehl Journal: Drug Dev Res Date: 2013-03 Impact factor: 4.360
Authors: Hormuzd A Katki; Stephanie A Kovalchik; Lucia C Petito; Li C Cheung; Eric Jacobs; Ahmedin Jemal; Christine D Berg; Anil K Chaturvedi Journal: Ann Intern Med Date: 2018-05-15 Impact factor: 25.391
Authors: Renda Soylemez Wiener; Michael K Gould; Douglas A Arenberg; David H Au; Kathleen Fennig; Carla R Lamb; Peter J Mazzone; David E Midthun; Maryann Napoli; David E Ost; Charles A Powell; M Patricia Rivera; Christopher G Slatore; Nichole T Tanner; Anil Vachani; Juan P Wisnivesky; Sue H Yoon Journal: Am J Respir Crit Care Med Date: 2015-10-01 Impact factor: 21.405
Authors: Richard Wender; Elizabeth T H Fontham; Ermilo Barrera; Graham A Colditz; Timothy R Church; David S Ettinger; Ruth Etzioni; Christopher R Flowers; G Scott Gazelle; Douglas K Kelsey; Samuel J LaMonte; James S Michaelson; Kevin C Oeffinger; Ya-Chen Tina Shih; Daniel C Sullivan; William Travis; Louise Walter; Andrew M D Wolf; Otis W Brawley; Robert A Smith Journal: CA Cancer J Clin Date: 2013-01-11 Impact factor: 508.702