Developing Software for Clinical Trial Analysis

Many studies in the pharmaceutical sciences attempt to assess the effect of factors such as treatment in clinical trials on a categorical outcome variable such as the amount of improvement. Often the outcome categories have a known ordering (e. g., none, some, moderate improvement). Under this general setting, recent developments with association models (Goodman, 1991) suggest that new maximum likelihood methods may offer substantial improvements in power and flexibility over traditional approaches. In Phase I, we will develop a prototype for a new MS/Windons-based computer software package for the analysis of a 2-way table (TREATMENT by OUTCOME) by association models, where OUTCOME is an ordered categorical variable. The software will contain a unique graphical interface to make it especially east to use. By re-analyzing several clinical trial data sets which have been analyzed by traditional methods, we will also demonstrate the feasibility and value of applying association models to assess treatment effects. In Phase II, we will extend the software package to more general association models to include covariates/stratifications such as center (for multi-center studies), baseline measures and demo- graphics such as age, sex and other risk factors.