Visual grids for managing data completeness in clinical research datasets

被引：5

作者：

Kelley, Robert R. ^{[1
]}

Mattingly, William A. ^{[1
]}

Wiemken, Timothy L. ^{[1
]}

Khan, Mohammad ^{[1
]}

Coats, Daniel ^{[1
]}

Curran, Daniel ^{[1
]}

Chariker, Julia H. ^{[2
]}

Ramirez, Julio ^{[1
]}

机构：

[1] Univ Louisville, Dept Med, Div Infect Dis, Louisville, KY 40202 USA

[2] Univ Louisville, Dept Psychol & Brain Sci, Louisville, KY 40202 USA

来源：

JOURNAL OF BIOMEDICAL INFORMATICS | 2015年 / 54卷

基金：

美国国家卫生研究院;

关键词：

Missing data; Data visualization; Clinical trial data; Data completeness; MISSING DATA; ADVANCED STATISTICS; DATA QUALITY; VISUALIZATION;

D O I：

10.1016/j.jbi.2014.12.002

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Missing data arise in clinical research datasets for reasons ranging from incomplete electronic health records to incorrect trial data collection. This has an adverse effect on analysis performed with the data, but it can also affect the management of a clinical trial itself. We propose two graphical visualization schemes to aid in managing the completeness of a clinical research dataset: the binary completeness grid (BCG) for single patient observation, and the gradient completeness grid (GCG) for an entire dataset. We use these tools to manage three clinical trials. Two are ongoing observational trials, while the other is a cohort study that is complete. The completeness grids revealed unexpected patterns in our data and enabled us to identify records that should have been purged and identify missing follow-up data from sets of observations thought to be complete. Binary and gradient completeness grids provide a rapid, convenient way to visualize missing data in clinical datasets. (C) 2015 Elsevier Inc. All rights reserved.

引用

页码：337 / 344

页数：8

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