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Cancer Intervention and Surveillance Modeling Network

Modeling to guide public health research and priorities

Comparative Modeling of Lung Cancer Control Policies

Principal Investigator: Pamela McMahon, PhD
Institution: Massachusetts General Hospital

Co-PIs:

Harry de Koning, MD, PhD Erasmus MC
David Levy, PhD Pacific Institute for Research and Evaluation
Sylvia Plevritis, PhD Stanford University
Suresh H. Moolgavkar, MD, PhD Fred Hutchinson Cancer Research Center
Theodore Holford, PhD Yale School of Public Health through Pacific Institute for Research and Evaluation

Grant Number: 1U01CA152956-01

Abstract: Sophisticated modeling techniques can be powerful tools for decision makers seeking to understand the effects of cancer control interventions on population trends in cancer incidence and mortality. Yet the proven value of such models in health policy is limited by legitimate concerns over lack of transparency of complex models and variability in published results from different groups.

NCI's Cancer Intervention and Surveillance Modeling Network (CISNET) was created to promote collaboration between independent modeling groups investigating similar questions. By using the same sources of data for inputs and agreeing on uniform outcome measures, the variability in results reflects uncertainty in the effects of cancer control interventions rather than differences in design of the analysis. Further, by working together, the modeling groups can coherently explain the causes of variation.

This proposal furthers the goals of CISNET by using comparative modeling approach to estimate the contributions of tobacco control and screening to reducing deaths from lung cancer. Over the next 5 years, major trials will report results on the efficacy of helical CT screening for lung cancer. The five CISNET models described in this proposal represent an existing infrastructure with which to synthesize these new data with existing data from observational and cohort studies and tumor registries. This group of models is poised to translate trial results into population-level effects and to project trends in lung cancer incidence and mortality if these policy interventions were adopted alone or in combination. We propose to disseminate our results in ways that will allow decision makers to prioritize one or more specific interventions to achieve the greatest reduction in lung cancer deaths.