Observer studies provide a necessary and compelling evaluation of any medical imaging technique. Consequently a large number of such studies are included in this program project. A number of computer based model evaluations will also be conducted. The core personnel will provide leadership in study design and will conduct the required statistical analyses. The research program will be improved directly by optimal design and analysis, and indirectly by the scientist time freed. Core personnel will also conduct all research data management. We shall also support any program of research by developing methods more efficient than those presently available. Many analyses in previous years have used nonlinear models as the first step. The necessarily small samples and correlated observations disallow using available methods for direct analysis because they depend on large sample arguments and only apply to limited designs. Hence nonlinear model parameter estimates were used as input to traditional linear model methods in order to provide defensible inference. The limitations of existing methods have not been well publicized to, nor well understood by, medical imaging researchers. Hence we shall extend available methods to the more general case mentioned, and share those advances with the entire medical imaging community. The statistical core activity has four components, each with its own specific aims. 1, Design. Implement the principles of design recommended by Muller, Barton, and Benigus [1984] and Kleinbaum, Kupper and Muller [1988] in all studies. 2, Analysis. Conduct all statistical data analyses for project studies. 3, Research Data Management. Archive and manage all data resulting from statistical analysis and study planning for project studies. 4, Methods. Develop accurate small sample hypothesis tests for a class of nonlinear models with repeated measures needed for medical imaging studies.
| Sadeghi, Neda; Prastawa, Marcel; Fletcher, P Thomas et al. (2012) STATISTICAL GROWTH MODELING OF LONGITUDINAL DT-MRI FOR REGIONAL CHARACTERIZATION OF EARLY BRAIN DEVELOPMENT. Proc IEEE Int Symp Biomed Imaging :1507-1510 |
| El-Sayed, Mohamed; Steen, R Grant; Poe, Michele D et al. (2010) Brain volumes in psychotic youth with schizophrenia and mood disorders. J Psychiatry Neurosci 35:229-36 |
| Chi, Yueh-Yun; Muller, Keith E (2010) Using scientifically and statistically sufficient statistics in comparing image segmentations. Stat Interface 3:91-101 |
| Simpson, Sean L; Edwards, Lloyd J; Muller, Keith E et al. (2010) A linear exponent AR(1) family of correlation structures. Stat Med 29:1825-38 |
| Clement-Spychala, Meagan E; Couper, David; Zhu, Hongtu et al. (2010) Approximating the Geisser-Greenhouse sphericity estimator and its applications to diffusion tensor imaging. Stat Interface 3:81-90 |
| Edwards, Lloyd J; Muller, Keith E; Wolfinger, Russell D et al. (2008) An R2 statistic for fixed effects in the linear mixed model. Stat Med 27:6137-57 |
| Hemminger, Bradley M; Bauers, Anne; Yang, Jian (2008) Comparison of navigation techniques for large digital images. J Digit Imaging 21 Suppl 1:S13-38 |
| Crouch, Jessica R; Pizer, Stephen M; Chaney, Edward L et al. (2007) Automated finite-element analysis for deformable registration of prostate images. IEEE Trans Med Imaging 26:1379-90 |
| Gurka, Matthew J; Coffey, Christopher S; Muller, Keith E (2007) Internal pilots for a class of linear mixed models with Gaussian and compound symmetric data. Stat Med 26:4083-99 |
| Muller, Keith E; Edwards, Lloyd J; Simpson, Sean L et al. (2007) Statistical tests with accurate size and power for balanced linear mixed models. Stat Med 26:3639-60 |
Showing the most recent 10 out of 69 publications
