384 Abstracts

corporated by accelerated failure time regression, and the model parameters are estimated by maximum likelihood. We then define quality functions which assign a “score” to a life having given transitions, and the modeling results are used to estimate the expectation of these functions. Standard errors and confidence intervals are computed using the bootstrap method and the delta method. The results are useful for evaluating treatments in terms of both quantity and quality of life.

As an example, we apply the methodology to data from the international Breast Cancer Study Group Trial V to compare short duration chemotherapy versus long duration chemotherapy in the treatment of node- positive breast cancer. The events studied are: (1) the end of treatment toxicity, (2) disease recurrence, and (3) overall survival.

Al9 LESSONS LEARNED AND PITFALLS TO BE AVOIDED IN THE DESIGN OF STUDY DRUG

TITRATION SYSTEMS

Norma Lynn Fox, Evelyn Mlrenzi, and Frances LoPresti Maryland Medical Research lnsfitute

Baltimore, Maryland

The Post-CABG Coordinating Center has had more than two years of experience in titrating lipid-lowering medications for patients enrolled in this clinical trial. Coordinating Center staff issue recommendations for titration of each patient’s medication regimen, based on information from multiple sources. An automated event-driven titration system reviews the pertinent history of each patient at each follow-up visit. If all data required for a titration decision are available, the system prints out the appropriate notification. If data are missing, inconsistent, or meet other criteria for monitoring, the system prints the history for staff review.

The automated titration system has been efficient and reliable, but the titration histories of a substantial proportion of patients have required staff review on one or more occasions. Some of the staff review could have been avoided by establishing guidelines for decision-making when data are missing or inconsistent. Minimizing the sources and amount of data required to prompt a decision would also have helped to limit staff review. The application of this experience to the design of study protocols and automated titration systems will be discussed.

A20 ADVERSE MEDICAL EVENTS IN CLINICAL TRIALS: REPORTING AND EVALUATION

Philip Day, Mark Jones, Clalr Haakenson, Cindy Colllng, Carol Fye, and Mike Sather VA Cooperative Studies Program

Albuquerque, New Mexico

AMES (adverse medical events) represent a major concern in all clinical trials involving drugs or medical devices. To prevent exposure of patients to unnecessary risk and to aid in documenting new AMES, a concise mechanism for the collection, categorization and analysis of AME data is required.

The VA CSP (Veterans Affairs Cooperative Studies Program) has developed a system to uniformly handle AMES in its trials involving drugs or medical devices. One component of this system is a computerized mechanism for evaluation of all AME reports. The heart of this computerized system is an AME coding scheme based upon a modified CoSTART (Coding Symbols for Thesaurus of Adverse Reaction Terms) terminology. CoSTART is the terminology developed and used by the Food and Drug Administration (FDA) for coding, filing and retrieving postmarketing adverse reaction reports.

The system allows study management to quickly evaluate each AME report and determine if findings suggest a new development which requires notification of investigators, the FDA and Data Monitoring Board. Implementing a unique coding system for incorporation into all VA CSP studies will also allow for trend analysis of AME data across trials.

A21 BAR CODING IN VALlDATlNG INVESTIGATIONAL DRUG PACKAGING

Mark S. Jones and Phlllp L. Day VA Cooperative Studies Program

Albuquerque, New Mexico

Providing the “right drug” to the “right patient” is a formidable task when dealing with large patient pop- ulations involving “tens-of-thousands” of dosage units. This task becomes even more ominous when dealing with large multi-center, randomized, double-blind, controlled clinical trials. Insuring that all investigational drug/