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

Abstract

Sophisticated modeling techniques can be powerful tools to help us understand the effects of cancer control interventions on population trends in cancer incidence and mortality. Readers of journal articles are however rarely supplied with modeling details. Six modeling groups collaborated as part of the National Cancer Institute’s Cancer Intervention and Surveillance Modeling Network (CISNET) to investigate the contribution of US tobacco control efforts towards reducing lung cancer deaths over the period 1975 to 2000. The models included in this monograph were developed independently and use distinct, complementary approaches towards modeling the natural history of lung cancer. The models used the same data for inputs and agreed on the design of the analysis and the outcome measures. This article highlights aspects of the models that are most relevant to similarities of or differences between the results. Structured comparisons can increase the transparency of these complex models.

Predicting the Mortality Benefit of CT Screening for Second Lung Cancer in a High-Risk Population

“…Fig 1 shows the predictions of cancer mortality between 55 and 80 years of age provided by Eqs 3 and 5 when the model was fit to the mean predictions provided by the published CISNET models [15]. The 7 different smoking histories in Fig 1 are the same that were used to evaluate the CISNET models.…”

“…Finally, the free parameters γ , a , b , β , φ 1 , and φ 2 were determined by fitting the predictions of Eqs 3 and 5 to the mean predictions of cancer death versus age provided by the Cancer Intervention Surveillance and Modeling Network (CISNET) models [15]. The fitting was performed by grid search for 7 different smoking histories simultaneously.…”

Comparing Benefits from Many Possible Computed Tomography Lung Cancer Screening Programs: Extrapolating from the National Lung Screening Trial Using Comparative Modeling

“…Although the rankings of programs were consistent across models, uncertainty in absolute numbers of lung cancer deaths avoided (and life years saved) remained, due to variation in the underlying assumptions regarding unobserved disease processes [37]. Underlying the differences across models in predicted absolute benefits is a variation in the predicted future number of lung cancer cases in the absence of screening (Figure S5 in File S1).…”

Comparative analysis of 5 lung cancer natural history and screening models that reproduce outcomes of the NLST and PLCO trials

“…Model M uses as dose-response module a probabilistic LC risk model previously calibrated to SEER and US LC data 14, 15 and recalibrated to NLST and PLCO, whereas all other groups use multistage carcinogenesis models 16–18 . Both multistage 5, 16, 17, 19 and probabilistic models have been used extensively to investigate the effects of smoking on LC risk 12, 20, 21 . Model E uses a multistage model based on the Nurses’ Health Study (NHS) and Health Professionals’ Follow-up Study (HPFS) 16 .…”

“…Recent examples include analyses of the impact of tobacco control on LC mortality rates 5 , comparative studies assessing the effects of different screening modalities in colorectal cancer 6 , cost-effectiveness analyses of breast cancer screening strategies 7 , and studies evaluating the impact of PSA screening in reducing prostate cancer rates 8, 9 . All of these examples used a comparative modeling framework by which researchers across institutions can directly compare and contrast results from distinct models 10–12 . The conclusions arising from comparative modeling analyses are more robust and reliable than single-model studies and this approach has been cited as an example of Good Modeling Practices 13 .…”