Date of Award
5-2006
Degree Type
Report
Degree Name
Master of Science (MS)
Department
Mathematics and Statistics
Committee Chair(s)
Chris Corcoran
Committee
Chris Corcoran
Abstract
Family-based study designs are often employed when investigating the genetic causes of complex disease. While the transmission disequilibrium test (TDT) and its extensions were developed to use family data for assessing linkage between a known genetic marker and a disease-causing gene, the so-called FBAT approach proposed by Rabinowitz and Laird (2000) effectively subsumes these family-based procedures as special cases. FBAT is fully conditional, but its implementation in the freely available FBAT software package uses a large-sample distributional approximation to compute p-values. The exact distribution for FBAT can be enumerated, but doing so explicitly is computationally intensive, particularly for relatively larger sample sizes. Schneiter et al. (2005) proposed an efficient algorithm for computing the exact p-value, but the computational performance of this procedure has not been systematically evaluated. In this report, we carry out a simulation study to determine the relative computational efficiency of large-sample FBAT and exact FBAT (XFBAT). Characteristic of all exact significance tests in statistical practice, XFBAT is significantly more computationally burdensome, and its efficiency decreases exponentially for larger sample sizes. In addition, XFBAT is more computationally intensive under the additive inheritance model, as opposed to either the dominant or recessive models.
Recommended Citation
Ouyang, Yanwei, "Evaluating the Computational Efficiency of XFBAT and FBAT for Family Based Studies" (2006). All Graduate Plan B and other Reports, Spring 1920 to Spring 2023. 1293.
https://digitalcommons.usu.edu/gradreports/1293
Included in
Copyright for this work is retained by the student. If you have any questions regarding the inclusion of this work in the Digital Commons, please email us at .