OBJECTIVES: Models previously developed for predicting lung cancer mortality from cigarette smoking intensity and duration based on aggregated prospective mortality data have employed a study of British doctors and have assumed a uniform age of initiation of smoking. We reexamined these models using the American Cancer Society's Cancer Prevention Study I data that include a range of ages of initiation to assess the importance of an additional term for age. METHODS: Model parameters were estimated by maximum likelihood, and model fit was assessed by residual analysis, likelihood ratio tests, and chi(2) goodness-of-fit tests. RESULTS: Examination of the residuals of a model proposed by Doll and Peto with the Cancer Prevention Study I data suggested that a better fitting model might be obtained by including an additional term specifying the ages when smoking exposure occurred. An extended model with terms for cigarettes smoked per day, duration of smoking, and attained age was found to fit statistically significantly better than the Doll and Peto model (P < 0.001) and to fit well in an absolute sense (goodness-of-fit; P = 0.34). Finally, a model proposed by Moolgavkar was examined and found not to fit as well as the extended model, although it included similar terms (goodness-of-fit; P = 0.007). CONCLUSIONS: The addition of age, or another measure of the timing of the exposure to smoking, improves the prediction of lung cancer mortality with Doll and Peto's multiplicative power model.
OBJECTIVES: Models previously developed for predicting lung cancer mortality from cigarette smoking intensity and duration based on aggregated prospective mortality data have employed a study of British doctors and have assumed a uniform age of initiation of smoking. We reexamined these models using the American Cancer Society's Cancer Prevention Study I data that include a range of ages of initiation to assess the importance of an additional term for age. METHODS: Model parameters were estimated by maximum likelihood, and model fit was assessed by residual analysis, likelihood ratio tests, and chi(2) goodness-of-fit tests. RESULTS: Examination of the residuals of a model proposed by Doll and Peto with the Cancer Prevention Study I data suggested that a better fitting model might be obtained by including an additional term specifying the ages when smoking exposure occurred. An extended model with terms for cigarettes smoked per day, duration of smoking, and attained age was found to fit statistically significantly better than the Doll and Peto model (P < 0.001) and to fit well in an absolute sense (goodness-of-fit; P = 0.34). Finally, a model proposed by Moolgavkar was examined and found not to fit as well as the extended model, although it included similar terms (goodness-of-fit; P = 0.007). CONCLUSIONS: The addition of age, or another measure of the timing of the exposure to smoking, improves the prediction of lung cancer mortality with Doll and Peto's multiplicative power model.
Authors: Lisa A Peterson; Emily L Bellile; Gregory T Wolf; Shama Virani; Andrew G Shuman; Jeremy M G Taylor; Laura S Rozek Journal: Head Neck Date: 2016-07-19 Impact factor: 3.147