High-Performance Open-Source Archive
hce package. The standardized
output object is now of class adhce. This means also that
there is a more standardized output that make it easier to work with. As
a result, the maraca package has now a higher
hce version dependency (0.8.5). Also, all class dependent
functions in the package have been updated to only work on the
adhce class object (for example
plot.adhce()).remove_outliers in
plot.maraca() and plot.adhce(). In some cases,
there might be outliers that skew the displayed range for the continuous
endpoint. There is now an option to display the continuous endpoint
without the outliers by setting the parameter
remove_outliers = TRUE in the plot.maraca() or
plot.adhce() function. We define outliers here according to
the common boxplot calculation definition: any data outside the range
25th percentile - 1.5 * IQR (inter-quartile range) and 75th percentile +
1.5 * IQR. Note that this required some refactoring in how the plot is
constructed. Especially the violin plot is now pre-calculated and then
plotted using the ggplot2 function
geom_polygon() (rather than the geom_violin()
function).density_plot_type = "box" is selected, the
boxplot will now contain vertical segments to indicate where the
whiskers end.animate_maraca() function. This is an animated version of
the standard maraca plot to allow to show how the plot is being built up
step-by-step. Note that the gganimate package needs to be
installed to create the animation. Additionally, to save the animation
as a gif, the package gifski needs to be installed. This is
an experimental feature, so despite doing some testing during
development there might be some problems or unexpected behavior during
usage. Please take a minute to report any wrong behavior to allow us to
improve the functionality.Slight change in automatic checks after an update of the
hce package (dependency).
mosaic_plot - a new plot to compares
outcome between an active treatment group and a control group,
highlighting areas of “Wins”, “Losses” and “Ties” based on endpoint
hierarchy. Details are given in the new vignette “Maraca Plots -
Introduction to the Mosaic plot”.cumulative_plot()
function - dustin() and dustin_plot().Updated author information.
component_plot(), there has been a
new plot added called cumulative_plot(). As opposed to the
previous plot showing the individual components of the win odds
computation, this plot is displaying the endpoints cumulated instead
(adding one component of hierarchical endpoint at a time). Details can
be found in the vignette “Maraca Plots - Plotting win odds”.tte_outcomes has been changed to
step_outcomes and the parameter
continuous_outcome to last_outcome.ggplot2 is now automatically attached when loading
maraca.maraca has a new dependency - the
patchwork package.trans parameter in the plotting functions was not
working as intended. It now enables x-axis transformation for the
continuous endpoint part of the plot.theme argument in the plotting functions allows
users to easily change the styling of the plot. Details are given in the
new vignette “Maraca Plots - Themes and Styling”.component_plot() function works for both
maraca and hce. Details can be found in the
new vignette “Maraca Plots - Plotting win odds”.validate_maraca() that was added in version
0.5.maraca now has increased the version dependency for the
package hce to >= 0.5.hce package is now automatically attached when
loading maraca.print() function for maraca objects that summarizes key
information.validate_maraca() function that extracts key
information from a maraca plot object. This can be used to validate the
plot against independently coded versions (for example using a different
programming language).maraca() function now requires an input for the
parameter fixed_followup_days. Note that there can be no
observed events in the data after the follow-up time specified.maraca does no longer depend on the
gridExtra package.plot_tte_components() function for plotting the
individual time-to-event outcomes was removed from the package since it
did not prove to be overly useful.plot_tte_composite() was removed for now since the
package cannot correctly calculate the composite version of looking at
multiple time-to-event endpoints when patients have multiple
events.
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