Paper 50

Enhance flexibility and variability by utilizing an Intermediate Dataset when developing the ADaM Efficacy Dataset in Vaccine Studies

September 23, 2025: 8:30 AM - 9:00 AM

Data Collection, Management, and Manipulation (DCMM), Executive Dining Room #3

Authors	Abstract
Edward Boykis	This paper outlines the steps involved in developing the Efficacy Analysis ADEFF Dataset for vaccine studies by generating the intermediate dataset and subsequently using it to derive efficacy endpoints. This method allows for straightforward customization of endpoints. The significant advantage of this approach is that it gathers all essential symptoms, indicators, and dates into a single intermediate dataset, enabling the most efficient, rapid, and traceable derivation of endpoints. This capability enables programmers to generate efficacy endpoints in a standardized and consistent manner, ensuring accuracy and efficiency. This article provides comprehensive explanation, accompanied by a data flow diagram and the code snippets.

Edward Boykis

This paper outlines the steps involved in developing the Efficacy Analysis ADEFF Dataset for vaccine studies by generating the intermediate dataset and subsequently using it to derive efficacy endpoints. This method allows for straightforward customization of endpoints. The significant advantage of this approach is that it gathers all essential symptoms, indicators, and dates into a single intermediate dataset, enabling the most efficient, rapid, and traceable derivation of endpoints. This capability enables programmers to generate efficacy endpoints in a standardized and consistent manner, ensuring accuracy and efficiency. This article provides comprehensive explanation, accompanied by a data flow diagram and the code snippets.