Chapter 8: The FHCRC Lung Cancer Model

Abstract

As a member of the Cancer Intervention and Surveillance Modeling Network (CISNET), the lung cancer group at Fred Hutchinson Cancer Research Center (FHCRC) developed a model for evaluating US lung cancer mortality trends and the impact of changing tobacco consumption. Model components include a biologically-based two-stage clonal expansion (TSCE) model; a smoking simulator to generate smoking histories and other cause mortality; and adjustments for period and birth-cohort to improve calibration to US lung cancer mortality. The TSCE model was first calibrated to five substantial cohorts: British doctors’, American Cancer Society CPS-I and CPS-II, Health Professionals’ Follow-Up Study (HPFS), and Nurses’ Health Study (NHS). The NHS and HPFS cohorts included the most detailed smoking histories and were chosen to represent the effects of smoking on US lung cancer mortality. The calibrated TSCE model and smoking simulator were used to simulate US lung cancer mortality. Further adjustments were necessary to account for unknown factors. This provided excellent fits between simulated and observed US lung cancer mortality for ages 30–84 and calendar years 1975–2000. The FHCRC lung cancer model may be used to study the effects of public health information on US lung cancer trends and the impact of tobacco control policy. For example, we estimated that over 500,000 males and 200,000 females avoided lung cancer death between 1975–2000 due to increasing awareness since the mid-1950’s of the harmful effects of smoking. We estimated that 1.1 million male and 0.6 million female lung cancer deaths were avoidable if smokers quit smoking in 1965.

Comparison of Smoking History Patterns Among African American and White Cohorts in the United States Born 1890 to 1990

“…More recent work with additional cohort data indicates that an even better estimate of lung cancer mortality risk can be achieved by including life history of exposure. 23,24 This work implies that age at initiation and cessation provides better estimates of risk that are not captured by duration, alone, and the history of smoking intensity further improves estimates of risk. Hence, both summary measures of smoking history have limitations and this is especially true for pack-years.…”

Chapter 5: Actual and Counterfactual Smoking Prevalence Rates in the U.S. Population via Microsimulation

“…We chose t lag = 5 years based on likelihood analysis previously done by fitting the TSCE model to lung cancer incidence or mortality cohorts. (4–5) We used the estimates for other biological parameters from the TSCE model calibration to two lung cancer mortality cohorts: Nurses’ Health Study (NHS) cohort for females and the Health Professionals’ Follow-Up Study (HPFS) cohort for males (for details, see Chapter 8 of this monograph (15) ). Using the cohort-calibrated TSCE model, we compute the probability of lung cancer death for each simulated individual as a function of age based on his/her smoking history.…”

“…We derive the hazard function h ( t ) using the two-stage clonal expansion (TSCE) model (for details of the model, refer to Chapter 8 of this monograph (15) ). The lag time between lung cancer incidence and death from lung cancer is modeled by a constant term.…”

Chapter 13: CISNET Lung Models: Comparison of Model Assumptions and Model Structures

“…The TSCE model, as employed by the FHCRC, PIRE, Rice and Yale groups (F, P, R, and Y), was calibrated to mortality rates in different cohorts as described earlier and in the individual articles detailing each model. ( 1‐6 ) The TSCE model includes a lag (generally five years) between the first malignant cell and death from cancer. Thus, there is no separate clinical detection event and, therefore, no incidence or survival rates are predicted.…”

“…This article highlights aspects of the models that are most relevant to similarities of or differences between the results of the Smoking Base Case analyses. Additional details for each model are available in other articles in this monograph ( 1‐6 ) and on the individual model profiles (http://www.cisnet.cancer.gov/profiles). Results of the main endpoint (changes in mortality trends and numbers of lung cancer deaths) are provided in Boer et al .…”