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medplot: a web application for dynamic summary and analysis of longitudinal medical data based on R.

Ahlin Č, Stupica D, Strle F, Lusa L - PLoS ONE (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: PhD Candidate of Statistics Programme, University of Ljubljana, Ljubljana, Slovenia.

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.

No MeSH data available.


Related in: MedlinePlus

Graphical exploration tab output for numerical variables—lasagna plot.The heat map displays graphically the intensity of arthralgia for each patient (horizontal axis) and evaluation occasion (vertical axis). A dendrogram showing patient similarity is plotted on the vertical axis.
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pone.0121760.g005: Graphical exploration tab output for numerical variables—lasagna plot.The heat map displays graphically the intensity of arthralgia for each patient (horizontal axis) and evaluation occasion (vertical axis). A dendrogram showing patient similarity is plotted on the vertical axis.

Mentions: The use of profile plots and heat maps was advocated in longitudinal studies for the identification of trends and to display individual changes [12, 17], which are not visible with boxplots. Profile plots are scatterplots displaying the evaluation times and the values of the variables, where the values from the same subject are connected (S5 Fig). However, they are less useful when many subjects are plotted together and the profiles overlap, obscuring the trends. To overcome this problem medplot includes the possibility of displaying a random subset of the subjects (S6 Fig) or multiple plots for each variable. Alternatively, heat maps can be used for large data sets: evaluation times are reported horizontally, as in the profile plots, but the measurements of each subject appear in the same row and colors are used to display the value of the variables (Fig 5). In our implementation, the subjects are arranged using a hierarchical clustering algorithm (with Euclidean distance and complete linkage agglomeration method). The rearrangement of the subjects is useful for data exploration because similar subjects are grouped together.


medplot: a web application for dynamic summary and analysis of longitudinal medical data based on R.

Ahlin Č, Stupica D, Strle F, Lusa L - PLoS ONE (2015)

Graphical exploration tab output for numerical variables—lasagna plot.The heat map displays graphically the intensity of arthralgia for each patient (horizontal axis) and evaluation occasion (vertical axis). A dendrogram showing patient similarity is plotted on the vertical axis.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4383594&req=5

pone.0121760.g005: Graphical exploration tab output for numerical variables—lasagna plot.The heat map displays graphically the intensity of arthralgia for each patient (horizontal axis) and evaluation occasion (vertical axis). A dendrogram showing patient similarity is plotted on the vertical axis.
Mentions: The use of profile plots and heat maps was advocated in longitudinal studies for the identification of trends and to display individual changes [12, 17], which are not visible with boxplots. Profile plots are scatterplots displaying the evaluation times and the values of the variables, where the values from the same subject are connected (S5 Fig). However, they are less useful when many subjects are plotted together and the profiles overlap, obscuring the trends. To overcome this problem medplot includes the possibility of displaying a random subset of the subjects (S6 Fig) or multiple plots for each variable. Alternatively, heat maps can be used for large data sets: evaluation times are reported horizontally, as in the profile plots, but the measurements of each subject appear in the same row and colors are used to display the value of the variables (Fig 5). In our implementation, the subjects are arranged using a hierarchical clustering algorithm (with Euclidean distance and complete linkage agglomeration method). The rearrangement of the subjects is useful for data exploration because similar subjects are grouped together.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: PhD Candidate of Statistics Programme, University of Ljubljana, Ljubljana, Slovenia.

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.

No MeSH data available.


Related in: MedlinePlus