空间聚类分析

获奖的Xcellerate空间聚类分析根据符合条件的患者群的位置指导选址,以改进研究规划并加速患者登记。

Leverages historical investigator recruitment performance data and real-world diagnostic data

Identifies high-quality and high-performing research sites near patient densities

Drives actionable insights to optimize site/country configuration for a specific study or program

价值it Brings to the Outcome of a Trial

Xcellerate空间聚类分析将符合研究资格标准的未确定患者群与历史上表现良好的站点相匹配,以创造更高的满足招募里程碑的概率。

概述:

Xcellerate Spatial Cluster improves site selection and optimizes clinical trial design. It harnesses the collective power of our unique-to-the-industry proprietary investigator recruitment performance analysis derived from data on more than 50 percent of all global studies and real-world diagnostic data sets, including 30 billion+ test results from over 50% of the U.S. population. It allows us to examine the impact of inclusion and exclusion criteria on the available patient pool. Xcellerate Spatial Cluster Analysis can also identify additional locations where eligible patient densities exist but are a long distance from known investigators, allowing for focused investigator expansion activities.

空间聚类分析是一种成本mized statistical methodology that identifies the intersection between patient clusters and the most productive sites.

空间聚类分析是一种成本mized statistical methodology that identifies the intersection between patient clusters and the most productive sites.

Xcellerate空间聚类分析的好处:

  • 预测性: leverages historical investigator recruitment performance data and real-world diagnostic data
  • Effective:确定靠近患者密度的高质量和高性能研究地点
  • Efficient:推动可操作的见解,以优化特定研究或计划的站点/国家配置

Performance Metrics:

Patient density 63% better vs. the industry in Cardiovascular – Global – Phase I-IV studies
Patient density 好47% 肌肉骨骼-全球-I-IV期研究与行业对比
Spatial Cluster Analysis identifies patient clusters with no investigator nearby to identify opportunities for development.
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