n this research, we propose to address the issue of adjusting for confounders observed post-randomization in randomized clinical trials. Example of these confounders include patient "compliance" measured through pill counts and other biochemical markers, the occurrence of co-morbid events, the use of concomitant and "rescue" medications, withdrawal from the assigned therapy, and so on. Since the confounder is observed following randomization, it can be considered as an outcome measure and analyzed accordingly. Previous research has begun from this premise and demonstrated how to adjust inferences on the primary endpoint for the influence of these confounding variables. In the current study, we propose to extend previously developed methodologies in several important directions: (1) develop a methodology to perform inference on a primary response variable adjusting for a survival confounding measure in repeated measures experiments; (2) develop a methodology in longitudinal data to adjust for informative censoring and other missing value problems; (3) perform a simulation study of the proposed estimation and hypothesis testing procedures; (4) develop procedures for analyzing bivariate repeated measure data; and, (5) develop a general methodology for performing sample size calculations for discrete and continuous repeated measures studies. It is expected that a fully validated methodology for adjusting for confounders arising post randomization will be forthcoming from this research. Grant from NCI, 1-R01-CA70286- 01, 1996-1999.