Browse > Article
http://dx.doi.org/10.5351/CSAM.2016.23.6.543

Evaluating the efficiency of treatment comparison in crossover design by allocating subjects based on ranked auxiliary variable  

Huang, Yisong (Department of Biostatistics, Jiann-Ping Hsu College of Public Health, Georgia Southern University)
Samawi, Hani M. (Department of Biostatistics, Jiann-Ping Hsu College of Public Health, Georgia Southern University)
Vogel, Robert (Department of Biostatistics, Jiann-Ping Hsu College of Public Health, Georgia Southern University)
Yin, Jingjing (Department of Biostatistics, Jiann-Ping Hsu College of Public Health, Georgia Southern University)
Gato, Worlanyo Eric (Department of Chemistry, Georgia Southern University)
Linder, Daniel F. (Department of Biostatistics and Epidemiology, Medical College of Georgia, Augusta University)
Publication Information
Communications for Statistical Applications and Methods / v.23, no.6, 2016 , pp. 543-553 More about this Journal
Abstract
The validity of statistical inference depends on proper randomization methods. However, even with proper randomization, we can have imbalanced with respect to important characteristics. In this paper, we introduce a method based on ranked auxiliary variables for treatment allocation in crossover designs using Latin squares models. We evaluate the improvement of the efficiency in treatment comparisons using the proposed method. Our simulation study reveals that our proposed method provides a more powerful test compared to simple randomization with the same sample size. The proposed method is illustrated by conducting an experiment to compare two different concentrations of titanium dioxide nanofiber (TDNF) on rats for the purpose of comparing weight gain.
Keywords
ranked set sampling; ranked auxiliary covariate; experimental design; treatment allocation method; crossover design; Latin square design; TDNF; weight gain;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Al-Saleh MF and Zheng G (2002). Estimation of bivariate characteristics using ranked set sampling, Australian & New Zealand Journal of Statistics, 44, 221-232.   DOI
2 Chen Z (2007). Ranked set sampling: its essence and some new applications, Environmental and Ecological Statistics, 14, 355-363.   DOI
3 Linder D, Samawi H, Yu L, Chatterjee A, Huang Y, and Vogel R (2015). On stratified bivariate ranked set sampling for regression eestimators, Journal of Applied Statistics, 42, 2571-2583.   DOI
4 Lynne Stokes S (1977). Ranked set sampling with concomitant variables communications in statistics theory and methods, Communications in Statistics - Theory and Methods, 6, 1207-1211.   DOI
5 McIntyre GA (1952). A method of unbiased selective sampling using ranked sets, Australian Journal of Agricultural Research, 3, 385-390.   DOI
6 Samawi HM, Ahmed MS, and Abu-Dayyeh W (1996). Estimating the population mean using extreme ranked set sampling, Biometrical Journal, 38, 577-586.   DOI
7 Senn S (1993). Cross-Over Trials in Clinical Research, John Wiley & Sons, New York.