Related Experiment Video
Updated: May 19, 2026
13:34
A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
Published on: April 6, 2016
Chapter 13: CISNET lung models: comparison of model assumptions and model structures.
Pamela M McMahon1, William D Hazelton, Marek Kimmel
1Institute of Technology Assessment, 101 Merrimac St., Boston, MA 02114-4724, USA. pamela@mgh-ita.org
Summary
This study used six independent models to assess U.S. tobacco control's impact on lung cancer deaths from 1975-2000. The Cancer Intervention and Surveillance Modeling Network (CISNET) collaboration enhanced transparency in cancer modeling.
Area of Science:
- Oncology
- Public Health
- Biostatistics
Background:
- Cancer control interventions significantly impact population incidence and mortality trends.
- Detailed modeling methodologies are often omitted in published research, limiting transparency.
- The National Cancer Institute's Cancer Intervention and Surveillance Modeling Network (CISNET) was established to address these gaps.
Purpose of the Study:
- To evaluate the contribution of U.S. tobacco-control efforts to reductions in lung cancer mortality between 1975 and 2000.
- To compare and contrast the results from six independent, yet complementary, lung cancer simulation models.
- To increase the transparency of complex cancer modeling techniques.
Main Methods:
- Six distinct modeling groups within CISNET independently developed models of lung cancer natural history.
- All models utilized the same input data, analysis design, and outcome measures for comparability.
- A structured comparison of model aspects was performed to understand similarities and differences in results.
Main Results:
- The study facilitated a transparent comparison of independent models assessing tobacco control interventions.
- Similarities and differences in model outputs were highlighted, providing insights into model behavior.
- The collaborative approach demonstrated the utility of diverse modeling strategies in cancer research.
Conclusions:
- Independent yet comparable modeling approaches can effectively assess the population impact of cancer control interventions.
- Structured comparisons enhance the transparency and understanding of complex epidemiological models.
- CISNET's collaborative efforts provide valuable insights into the effectiveness of public health initiatives like tobacco control.
