) Repeated measures of continuous data often provides the best combination of cost and statistical sensitivity for a wide range of health research in many disciplines. As driving examples, consider comparing methods of displaying digital mammograms, or radiotherapy planning films. The limited number and cost of qualified readers encourage choosing a minimum sample size, while scientific goals demand great sensitivity to differences. An accurate sample size analysis allows the scientist to resolve the conflict. Unfortunately, accurate sample size choice requires an accurate value for error variance of Gaussian data. Uncertainty surrounding the variance makes an Internal Pilot design very appealing. Such designs use the first fraction of the data to estimate the variance and then adjust the sample size up or down, as needed to achieve the target power. However, appropriate analysis methods are not available for repeated measures with internal pilots. Our research will meet the need for such new methods. (1) We will develop better statistical power approximations for the """"""""univariate approach"""""""" to repeated measures (UNIREP ANOVA), including exact properties and more accurate approximations. (2) We will derive exact and approximate properties of the distribution of final sample size of Internal Pilot designs used with UNIREP ANOVA. (3) We will describe analytic properties of UNIREP ANOVA in Internal Pilot designs, including some exact and large sample distributions, as well as practical algorithms.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA095749-03
Application #
6838682
Study Section
Special Emphasis Panel (ZRG1-SNEM-5 (01))
Program Officer
Tiwari, Ram C
Project Start
2003-01-15
Project End
2007-12-31
Budget Start
2005-01-01
Budget End
2007-12-31
Support Year
3
Fiscal Year
2005
Total Cost
$204,580
Indirect Cost
Name
University of North Carolina Chapel Hill
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
608195277
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599
Kairalla, John A; Muller, Keith E; Coffey, Christopher S (2010) Combining an Internal Pilot with an Interim Analysis for Single Degree of Freedom Tests. Commun Stat Theory Methods 39:3717-3738
Glueck, D H; Karimpour-Fard, A; Mandel, J et al. (2010) Probabilities for separating sets of order statistics. Statistics (Ber) 44:145-153
Johnson, Jacqueline L; Muller, Keith E; Slaughter, James C et al. (2009) POWERLIB: SAS/IML Software for Computing Power in Multivariate Linear Models. J Stat Softw 30:
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
Coffey, Christopher S; Kairalla, John A (2008) Adaptive clinical trials: progress and challenges. Drugs R D 9:229-42
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
Jiroutek, Michael R; Muller, Keith E; Kupper, Lawrence L et al. (2003) A new method for choosing sample size for confidence interval-based inferences. Biometrics 59:580-90