- Brian Hobbs, The University of Texas at Austin
- Bo Huang, Pfizer
- Ying Huang, Fred Hutch
- Gang Li, University of California, Los Angeles
- Sheng Luo, Duke University
- Arvind Rao, University of Michigan
- Yu Shen, University of Texas MD Anderson Cancer Center
- Lorenzo Trippa, Harvard University
- Sue-Jane Wang, US FDA
- Yanxun Xu, Johns Hopkins University
Discovery: Statistical models for bioinformatics and biomedical data in cancer.
Translational Research and Clinical Trials: Statistical methods for cancer clinical trials for cancer diagnosis and treatment.
Population Science: Statistical models and methods for cancer population research including prevention and surveilance.
[Statistical Models for Bioinformatics and Biomedical Data in Cancer] The section welcomes submission of statistical models aiming to discovery patterns and mechanisms of cancer by analyzing bioinformatics and biomedical data. Examples include high-throughput sequencing data, electronic medical records, large imaging data, and combination of multiple data modalities.
[Translational Research and Clinical Trials] The section also welcomes submission of statistical methods for cancer clinical trials for establishing efficacy, diagnosis, and effectiveness, including but not limited to, adaptive designs, analysis approaches, decision making frameworks, discussion on important and controversial approaches, and review articles.
[Population Science for Cancer Prevention and Surveilance] The section welcomes submission of statistical models and methods for population registry and surveillance data, cancer prevention, and real world data and real world evidence for clinical developments.
Other than review articles, the submitted papers will be evaluated by the potential impact and novelty. The review papers will be evaluated based on the timing and depth of the review. Both methodological and applied manuscripts will be accepted.