Comparison of statistical methods for pretest-posttest designs in terms of type i error probability and statistical power

Communications in Statistics: Simulation and Computation

Volumn 44, Issue 2, 2015, Pages 284-294

  Wu, Xionghua Wilson ^a,b   Lai, Dejian ^b,c  

^a Halozyme   (United States)

^b UNIVERSITY OF TEXAS SCHOOL OF PUBLIC HEALTH   (United States)

^c JIANGXI UNIVERSITY OF FINANCE AND ECONOMICS   (China)

Author keywords

Analysis of covariance; HuberM estimation; Normal score transformation; Pretest posttest design; Statistical power; Type I error

Indexed keywords

DESIGN; FORESTRY; METADATA; REGRESSION ANALYSIS; STATISTICS; ERRORS; IMAGE ENHANCEMENT;

ANALYSIS OF COVARIANCES; HUBERM-ESTIMATION; NORMAL SCORE TRANSFORMATION; STATISTICAL POWER; TYPE-I ERROR; M-ESTIMATION;

ANALYSIS OF VARIANCE (ANOVA);

EID: 84946181743     PISSN: 03610918     EISSN: 15324141     Source Type: Journal    
DOI: 10.1080/03610918.2013.775295     Document Type: Article

References (21)

1. Box, G.E. P., Cox, D.R. (1964). An analysis of transformations. Journal of Royal Statistical Society, Series B 26: 211-252.
2. Chernoff, H., Savage, I.R. (1958). Asymptotic normality and efficiency of certain nonparametric test statistics. Annals of Mathematical Statistics 29: 972-994.
3. Cochran, W.G. (1957). Analysis of covariance: its nature and uses. Biometrics 13: 261-281.
4. Conover, W.J., Iman, R.L. (1982). Analysis of covariance using the rank transformation. Biometrics 38: 715-724.
5. Cox, D.R., McCullagh, P.A. (1982). Biometrics invited paper with discussion: some aspects of analysis of covariance. Biometrics 38: 541-561.
6. Frison, L., Pocock, S.J. (1992). Repeated measures in clinical trials: analysis using mean summary statistics and its implications for design. Statistics in Medicine 11: 1685-1704.
7. Hoeffding, W. (1952). The large sample power of tests based on permutations of observations. Annals of Mathematical Statistics 27: 169-192.
8. Holland, P.W., Welsch, R.E. (1977). Robust regression using iteratively reweighted least squares. Communications in Statistics, Theory and Methods 6: 813-827.
9. Huber, P.J. (1964). Robust estimation of a location parameter. Annals of Mathematical Statistics 35: 73-101.
10. Huber, P.J. (1973). Robust Regression: asymptotics, conjectures, and Monte Carlo. Annals of Statistics 1: 799-821.
11. ICH. (1998). ICH guidance E9: Statistical principles for clinical trials. The ICH website. Available at http://www.ich.org (accessed 30 May 2014).
12. Knoke, J.D. (1991). Nonparametric analysis of covariance for comparing change in randomized studies with baseline values subject to error. Biometrics 47: 523-533.
13. Laird, N. (1983). Further comparative analysis of pretest-posttest research designs. The American Statistician 37: 329-330.
14. Quade, D. (1967). Rank analysis of covariance. Journal of the American Statistical Association 62: 1187-1200.
15. Rousseeuw, P.J. (1984). Least median of squares regression. Journal of the American Statistical Association 79: 871-880.
16. Seaman, S.L., Algina, J., Olejnik, S.F. (1985). Type I error probability and power of the rank and parametric ANCOVA procedures. Journal of Educational Statistics 10: 345-367.
17. Senn, S. (1994). Testing for baseline balance in clinical trials. Statistics in Medicine 13: 1715-1726.
18. Senn, S. (2006). Change from baseline and analysis of covariance revisited. Statistics in Medicine 25: 4334-4344.
19. Sullivan, L.M., DAgostino, R.B. (1996). Robustness and power of analysis of covariance applied to data distorted from normality by floor effects: homogeneous regression slopes. Statistics in Medicine 15: 477
20. Tukey, J.W. (1962). The future of data analysis. Annals of Mathematical Statistics 33: 1-67.
21. Yohai, V.J. (1987). High breakdown point and high efficiency robust estimates for regression. Annals of Statistics 15: 642-656.