The general objective of the proposed research is to develop a battery of statistical methods for specific application to the design and statistical monitoring of complex clinical trials in cancer. In many of these clinical trials, the principal outcomes include measures of morbidity as well as mortality, and sometimes these measurements are recorded over time on each patient. The main research topic (1) focuses on the sequential monitoring of a Gaussian process which may not have independent increments. The test statistics used for the comparison of two groups of patients based upon either survival time or a series of repeated measures, when computed repeatedly during data monitoring, form a Gaussian process which is not necessarily Brownian Motion. Therefore, the classical group sequential methods can not be applied directly. To monitor such processes, we propose to use the Lan-DeMets approach with a surrogate measure for information. We first consider (a) the implementation of a group sequential design in clinical trials when the response variable is time-to-event. In this case we will search for the most appropriate surrogate information for the Wilcoxon statistic, and then consider other two-sample linear rank statistics as well. In (b), we will consider the case of longitudinal data where the response variable is . measured repeatedly over time, and where such data are analyzed using linear or nonlinear models. We would like to investigate the effect of modelling on the choice of surrogate information. Two additional proposed topics are: (2) Uses of conditional power in a two-stage design; (3) Implication of Halperin's definition of the multiple comparison problem. Additional research topics will be identified as work proceeds on these projects, and in response to the discussions with our collaborators.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA055098-03
Application #
2096329
Study Section
Special Emphasis Panel (SSS (B6))
Project Start
1991-07-01
Project End
1995-06-30
Budget Start
1993-07-01
Budget End
1995-06-30
Support Year
3
Fiscal Year
1993
Total Cost
Indirect Cost
Name
George Washington University
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
City
Washington
State
DC
Country
United States
Zip Code
20052
Lan, K K Gordon; Lachin, John M; Bautista, Oliver (2003) Over-ruling a group sequential boundary--a stopping rule versus a guideline. Stat Med 22:3347-55
Hu, M; Lachin, J M (2001) Application of robust estimating equations to the analysis of quantitative longitudinal data. Stat Med 20:3411-28
Bautista, O M; Bain, R P; Lachin, J M (2000) A flexible stochastic curtailing procedure for the log-rank test. Control Clin Trials 21:428-39
Lan, K K; Rosenberger, W F; Lachin, J M (1995) Sequential monitoring of survival data with the Wilcoxon statistic. Biometrics 51:1175-83
Lan, K K; Lachin, J M (1995) Martingales without tears. Lifetime Data Anal 1:361-75
Wu, M C; Lan, K K; Connett, J E (1994) Use of surrogate information time for monitoring the effect of treatment on the change in a response variable in clinical trials. Stat Med 13:945-53
Lan, K K; Rosenberger, W F; Lachin, J M (1993) Use of spending functions for occasional or continuous monitoring of data in clinical trials. Stat Med 12:2219-31
Lan, K K; Zucker, D M (1993) Sequential monitoring of clinical trials: the role of information and Brownian motion. Stat Med 12:753-65