The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
summaryWO.adhce() to provide
the summaries by GROUP, as opposed to
summaryWO.hce(), which works without grouping by this
variable.simKHCE() function. The function has been updated to return
all time-to-event outcomes for each patient in the ADET
dataset.regWO(), which previously
caused the results to depend on the order of the input dataset. This
issue also affected the stratWO() function, since it calls
regWO(). A similar issue in the IWP() has also
been fixed. The bug was reported by Cyrill Scheidegger.hce() function has been for consistency with the
as_hce() function. Two new arguments, PADY and
AVAL0, have been added. The PADY argument
serves a similar purpose as now-deprecated ORD argument.
With these updates, hce() can produce outputs of class
adhce when the AVAL0 argument is
provided.calcWINS()
implementation to illustrate the differences between the following
formulas for the standard error of the win proportion: the
Bamber-Brunner-Konietschke formula (see Bamber, 1975; Brunner and
Konietschke, 2025), Brunner-Munzel test (Brunner and Munzel, 2000) based
on the DeLong-Clarke-Pearson (1988) formula, and the Somers (1962)
formula.summaryWO.formula() that previously
caused errors when GROUP values were used.simADHCE() has been replaced by the
all_data = TRUE implementation in
simHCE().simHCE() now returns an object of a new
class called adhce. This class inherits from the
hce class, which itself is a subclass of
data.frame. The underlying structure of the returned object
remains unchanged. The introduction of the adhce class is
intended to clearly distinguish these structured outputs from the more
general hce objects. Specifically, an adhce
object is an analysis-ready hce object that is derived
using multiple time-to-event outcomes and a single continuous (ordinal
or score) endpoint.as_hce() has been updated to support
additional output flexibility. If the input data includes the variables
TRTP, GROUP, AVAL0, and
PADY, the function will return an adhce
object. In this scenario, even if the AVAL variable is
present, it will be recalculated based on the provided data to ensure
consistency with the adhce structure. If only the
TRTP and AVAL variables are available,
as_hce() will return a standard hce object.
This enhancement allows users to generate either general or
analysis-ready hce objects, depending on the available
input variables.regWO() and stratWO() are updated to
return the confidence interval for the adjusted and stratified (or
adjusted/stratified) win probability as well.regWO().plot() method for hce objects (created by
the function as_hce()) is updated to include a
fill argument for filling the area above the graph.calcWINS() is updated to include the
SE_WP_Type argument with default "biased"
(original implementation) and a new "unbiased"
implementation of the Bamber-Brunner-Konietschke (see Bamber (1975),
Brunner and Konietschke (2025)) standard error for the win
proportion.IWP() is added to calculated patient-level
individual win proportions.as_hce() is added.simHCE() is updated to correct for the
copula implementation so that theta = 1 (case of
independence) and theta close to 1 now give similar results
(as expected).simHCE() is updated to include a new
theta argument for Gumbel dependence coefficient of the
Weibull distributions for time-to-event outcomes. Default is
theta = 1 which assumes independence of time-to-event
outcomes. The argument is still experimental.calcWO() is updated to return the confidence interval
for the win probability as well.plot() method for hce objects (created by
the function as_hce()) is implemented to provide the
ordinal dominance graph as suggested by Bamber (1975).powerWO(), sizeWO(), minWO() are updated
to include a new argument alternative to specify the class
of alternative hypothesis. All formulas are based on the Bamber (1975)
paper.COVID19plus.NEWS.md file to track changes to the
package.HCE1 - HCE4 datasest are updated to follow the standard
structure.dec is added to simHCE()
for decimal places used for rounding the continuous outcome in the
simulated dataset. Additionally, the default value for the standard
deviation of the continuous variable in the placebo group
CSD_P is changed to be equal to that of the active group
CSD_A instead of being equal to 1.simADHCE() which simulates
adhce objects, that is, an hce object with its
source datasets. Works similar to simHCE() which provides
only an hce object.simORD() which simulates ordinal
endpoint by categorizing beta distributions.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.