![cmaptools online cmaptools online](https://i.ytimg.com/vi/d_2x1UlW8gk/maxresdefault.jpg)
Use of data standards results in an increase in efficiency of studies by maximizing data utility, minimizing reprocessing of data, and expediting regulatory review of new drug applications (NDAs). Data standards provide the framework for consistent structure and understanding of data. This, in turn, allows for development of common open-source tools. The inability to compare data across different clinical trials arises in part because of differences between them, including data collection and format.ĭata standards enable the integration and analysis of data from multiple sources. The development and regulatory endorsement of these tools has been hampered by the lack of consensus data standards that cover both clinical and biomarker assessments allowing for rapid integrated analyses derived from multiple data sources. New drug development tools such as disease progression models, biomarkers, and outcome measures that can easily and rapidly incorporate new and existing sources of information are urgently needed to accelerate drug development at all stages of the AD disease spectrum. Data standards and the current landscape of Alzheimer's disease drug developmentĭrug development in Alzheimer's disease (AD) is increasingly being aimed at early intervention, with the recognition that such strategies hold the most promise to slow or halt disease progression.