A. Bokov, Laura S. Manuel, Alfredo Tirado-Ramos, Jon A. Gelfond, S. Pletcher
{"title":"Biologically relevant simulations for validating risk models under small-sample conditions","authors":"A. Bokov, Laura S. Manuel, Alfredo Tirado-Ramos, Jon A. Gelfond, S. Pletcher","doi":"10.1109/ISCC.2017.8024544","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":106141,"journal":{"name":"2017 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE Symposium on Computers and Communications (ISCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCC.2017.8024544","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
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.