medplot: A Web Application for Dynamic Summary and Analysis of Longitudinal Medical Data Based on R

被引:8
作者
Ahlin, Crt [1 ]
Stupica, Dasa [2 ]
Strle, Franc [2 ]
Lusa, Lara [3 ]
机构
[1] Univ Ljubljana, Stat Programme, Ljubljana, Slovenia
[2] Univ Med Ctr Ljubljana, Dept Infect Dis, Ljubljana, Slovenia
[3] Univ Ljubljana, Inst Biostat & Med Informat, Ljubljana 61000, Slovenia
来源
PLOS ONE | 2015年 / 10卷 / 04期
关键词
FALSE DISCOVERY RATE; REGRESSION; SEPARATION;
D O I
10.1371/journal.pone.0121760
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
In biomedical studies the patients are often evaluated numerous times and a large number of variables are recorded at each time-point. Data entry and manipulation of longitudinal data can be performed using spreadsheet programs, which usually include some data plotting and analysis capabilities and are straightforward to use, but are not designed for the analyses of complex longitudinal data. Specialized statistical software offers more flexibility and capabilities, but first time users with biomedical background often find its use difficult. We developed medplot, an interactive web application that simplifies the exploration and analysis of longitudinal data. The application can be used to summarize, visualize and analyze data by researchers that are not familiar with statistical programs and whose knowledge of statistics is limited. The summary tools produce publication-ready tables and graphs. The analysis tools include features that are seldom available in spreadsheet software, such as correction for multiple testing, repeated measurement analyses and flexible non-linear modeling of the association of the numerical variables with the outcome. medplot is freely available and open source, it has an intuitive graphical user interface (GUI), it is accessible via the Internet and can be used within a web browser, without the need for installing and maintaining programs locally on the user's computer. This paper describes the application and gives detailed examples describing how to use the application on real data from a clinical study including patients with early Lyme borreliosis.
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页数:19
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