He Zhe, Carini Simona, Sim Ida, Weng Chunhua
Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA.
Division of General Internal Medicine, University of California, San Francisco, San Francisco, CA 94143, USA.
J Biomed Inform. 2015 Apr;54:241-55. doi: 10.1016/j.jbi.2015.01.005. Epub 2015 Jan 20.
To develop a method for profiling the collective populations targeted for recruitment by multiple clinical studies addressing the same medical condition using one eligibility feature each time.
Using a previously published database COMPACT as the backend, we designed a scalable method for visual aggregate analysis of clinical trial eligibility features. This method consists of four modules for eligibility feature frequency analysis, query builder, distribution analysis, and visualization, respectively. This method is capable of analyzing (1) frequently used qualitative and quantitative features for recruiting subjects for a selected medical condition, (2) distribution of study enrollment on consecutive value points or value intervals of each quantitative feature, and (3) distribution of studies on the boundary values, permissible value ranges, and value range widths of each feature. All analysis results were visualized using Google Charts API. Five recruited potential users assessed the usefulness of this method for identifying common patterns in any selected eligibility feature for clinical trial participant selection.
We implemented this method as a Web-based analytical system called VITTA (Visual Analysis Tool of Clinical Study Target Populations). We illustrated the functionality of VITTA using two sample queries involving quantitative features BMI and HbA1c for conditions "hypertension" and "Type 2 diabetes", respectively. The recruited potential users rated the user-perceived usefulness of VITTA with an average score of 86.4/100.
We contributed a novel aggregate analysis method to enable the interrogation of common patterns in quantitative eligibility criteria and the collective target populations of multiple related clinical studies. A larger-scale study is warranted to formally assess the usefulness of VITTA among clinical investigators and sponsors in various therapeutic areas.
开发一种方法，用于剖析针对同一医学病症的多项临床研究每次使用一个入选特征进行招募的总体人群。
以前瞻性发布的数据库COMPACT作为后端，我们设计了一种用于临床试验入选特征可视化汇总分析的可扩展方法。该方法分别由四个模块组成，用于入选特征频率分析、查询构建器、分布分析和可视化。此方法能够分析：（1）为选定医学病症招募受试者时常用的定性和定量特征；（2）每项定量特征在连续值点或值区间上的研究入组分布；（3）各项特征在边界值、允许值范围和值范围宽度上的研究分布。所有分析结果均使用谷歌图表应用程序编程接口进行可视化。五名招募的潜在用户评估了该方法在识别用于临床试验参与者选择的任何选定入选特征中的常见模式方面的有用性。
我们将此方法实现为一个名为VITTA（临床研究目标人群可视化分析工具）的基于网络的分析系统。我们分别使用两个涉及“高血压”和“2型糖尿病”病症的定量特征BMI和糖化血红蛋白（HbA1c）的示例查询来说明VITTA的功能。招募的潜在用户对VITTA的用户感知有用性进行评分，平均得分为86.4/100。
我们提供了一种新颖的汇总分析方法，以探究定量入选标准和多项相关临床研究的总体目标人群中的常见模式。有必要进行更大规模的研究，以正式评估VITTA在各个治疗领域的临床研究人员和申办者中的有用性。
