Version 2.0 of the Alzheimer’s Disease Therapeutic Area User Guide (TAUG-Alzheimer's) was developed under the CDISC Standards Development Process. TAUG-Alzheimer’s v2.0 describes the most common biomedical concepts relevant to studies of Alzheimer’s disease and mild cognitive impairment, and the necessary metadata to represent such data consistently with Terminology and SDTM.
TA Standards extend the Foundational Standards to represent data that pertain to specific indications within disease areas. CDISC Standards specify how to structure the data; they do not specify what data should be collected or how to conduct clinical trials, assessments or endpoints.
Public Review Comments
CDISC posts Public Review comments and resolutions to ensure transparency and show implementers how comments were addressed in the standard development process.
| Attachment | Size |
|---|---|
 CDISC TAUG-Alzheimer's v2.0 | 3.65 MB |
 CDISC Alzheimer’s Disease v2 Public Review Comments | 29.25 KB |
Version 1.0 of the Alzheimer's Disease Therapeutic Area User Guide (TAUG-Alzheimer's) was developed in collaboration between CDISC and the Coalition Against Major Diseases (CAMD) - a program of the Critical Path Institute. TAUG-Alzheimer's v1.0 describes how to implement SDTM safety domains related to Alzheimer's disease (AD)/Mild Cognitive Impairment (MCI) clinical trials.
Public Review Comments
CDISC posts Public Review comments and resolutions to ensure transparency and show implementers how comments were addressed in the standard development process.
| Attachment | Size |
|---|---|
| SDTM Alzheimer's Disease/Mild Cognitive Impairment User Guide v1 | 572.34 KB |
| CDISC SDTM Alzheimer’s Disease Public Review Comments | 78.5 KB |
The Coalition Against Major Diseases (CAMD) used CDISC standards to develop a database that enables pooling of data from different sources to create an Online Data Repository for Alzheimer’s with the aim of supporting accelerated drug development.
Alzheimer’s Disease trial simulation tool was built using CDISC standardized data allows researchers to optimize the design of new trials.
