Abstract
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.
Highlights
Biomedical research often involves the use of complex data that can be difficult to summarize, visualize and analyze correctly
The basic summary statistics for the uploaded data are displayed in the Data overview tab (S1 Fig); the main descriptive statistics for the outcome variables, summarized at each evaluation occasion, are displayed in the Summary tab
The demo data set is a real data set from a study on erythema migrans where the clinical, epidemiological and microbiological characteristics of 225 adult patients were evaluated at baseline, 14 days, 2, 6, and 12 months after treatment [1]
Summary
Biomedical research often involves the use of complex data that can be difficult to summarize, visualize and analyze correctly. Longitudinal data are one particular type of complex data that arises in clinical studies when the aim is to analyze the changes occurring over time; the characteristics of the patients are evaluated several times at different time points and often a large number of variables are measured at each evaluation. Stupica et al [1] analysed the differences of erythema migrans (EM, early Lyme borreliosis) patients with either positive. April 2, 2015 medplot: A Web Application for Longitudinal Medical Data Based on R framework of the Operational programme for human resources development for the period 2007 – 2013. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
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