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SEND, or the Standard for Exchange of Nonclinical Data, is an implementation of the CDISC Standard Data Tabulation Model (SDTM) for nonclinical studies, which specifies a way to present nonclinical data in a consistent format. These types of studies are related to animal testing conducted during drug development. Raw data of all animal studies started after December 15th 2016 to support submission of new drugs to the US Food and Drug Administration will be submitted to the agency using SEND. Having a common model to which the industry can conform enables benefits such as the ability for vendors to develop tools, for inter-organizational data exchange that is consistent in format regardless of the parties involved, and so on. A SEND package consists of a few parts, but the main focus is on individual endpoint data. Endpoints typically map to domains (essentially, datasets), with a number of variables (a.k.a., columns or fields). ==SEND Implementation== The SEND Implementation Guide (SENDIG) is a document that provides implementers with specifications for implementing SEND, including how to model various nonclinical endpoints, rules to doing so, and examples with sample data. This document is available on the (CDISC SEND ) website. Supplementing the guide is the (SEND Implementation Wiki ) is a wiki hosted by (PhUSE ) designed to assist with the implementation process and filling in some of the gaps, most notably containing: *(SEND ), (CT ), and (Define.xml ) Fundamentals pages - providing entry-level descriptions of fundamental concepts in SEND, such as SEND itself, Controlled Terminology (CT), and Define.xml *(Getting SEND-ready ) - to help new implementers get started *(FAQ ) - providing a large, evolving list of commonly asked questions Companion to the wiki is the (SEND Implementation Forum ), which allows implementers to ask questions and get responses from SEND experts. New implementers are encouraged to ask questions here. 抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)』 ■ウィキペディアで「Standard for Exchange of Non-clinical Data」の詳細全文を読む スポンサード リンク
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