Biologically relevant simulations for validating risk models under small-sample conditions
- Resource Type
- Conference
- Authors
- Bokov, Alex F.; Manuel, Laura S.; Tirado-Ramos, Alfredo; Gelfond, Jon A.; Pletcher, Scott D.
- Source
- 2017 IEEE Symposium on Computers and Communications (ISCC) Computers and Communications (ISCC), 2017 IEEE Symposium on. :290-295 Jul, 2017
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Signal Processing and Analysis
Data models
Biological system modeling
Sociology
Statistics
Aerospace electronics
Computational modeling
Hazards
Maximum likelihood estimation
Monte Carlo methods
- Language
In designing scientific experiments, power analysis is too often given a superficial treatment— choice of sample size is often made based on idealized distributions and simplistic tests that do not reflect the real-world constraints under which the actual data will be collected. We have developed a general Monte Carlo framework for two-group comparisons which samples points from a two-dimensional parameter space and at each point generates simulated datasets which are compared to simulated datasets for a “control group” at a fixed point in the parameter space. Rather than uniformly sampling this parameter space, our algorithm rapidly converges on a contour corresponding to the smallest detectable difference for the sample size of interest.