This release (nearly) finalizes the development of the function
simKHCE() and should be the preferred version when using
this function.
simKHCE() affecting the
piecewise-constant kidney failure event generation with time-dependent
predicted true eGFR. The uniform random variable for event time was
previously sampled per visit (incorrect); it is now sampled once per
subject. Event determination across inter‑visit intervals was adjusted
to use cumulative hazard crossing with within‑interval
interpolation.simTTE() where some first death events
were incorrectly classified as censored.simKHCE() function now uses more extreme default
event rates based on observed eGFR to make effects more pronounced. KFRT
risk is adjusted at the patient level according to observed eGFR: when
observed eGFR is above 30 (mL/min/1.73 m2), the event rate is set to a
very low value (10E-7); when observed eGFR is at or below 7 (mL/min/1.73
m2), the event rate is set to a very high value (10E5).The vignette on hierarchical composite endpoints now includes
complete documentation of the methodology behind the
simKHCE() function.
A simple example has been added to the vignette on win statistics to show how the win ratio splits ties proportionally to the observed wins.
m and hce_type to
simTTE() to enable simulation of discrete-time outcomes and
creation of hierarchical composite endpoints using the
move-down approach.deltaWO(), which computes win odds based on
a specified threshold for adhce objects.simKHCE() function related
to the implementation of sustained decline.simKHCE() affecting the
piecewise-constant kidney failure event generation that uses
time-dependent predicted true eGFR between visits, ensuring events are
determined correctly over inter-visit intervals.summaryWO.formula() that previously
caused errors when GROUP values were used.sigma) in the simKHCE() function has been
updated. It now depends on the time-dependent predicted eGFR, hence
lower eGFR values result in lower variability.simKHCE() has been
revised to prevent events when the most recent eGFR is sufficiently high
and to trigger events when the most recent eGFR is below the
threshold.two_meas, has been added to the
simKHCE() function to enable duplicate eGFR measurements at
baseline and/or at the end of follow-up. This implementation was
suggested by Amy Shi.summaryWO.adhce() results now include cumulative
wins by component.simTTE(), simulates an hce
dataset with two correlated outcomes under an illness-death
model. It allows population heterogeneity in the first event (which also
determines correlation among first events), while the risk of the second
event depends on the timing of the first event in the same way across
treatment groups.calcWINS() for cases where
SE_WP_Type = "unbiased", providing Wilson-type
confidence intervals for the win probability, net benefit, and win odds,
following the approach of Schüürhuis, Konietschke, and Brunner
(2025).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 updated 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.summaryWO.adhce() to provide
the summaries by GROUP, as opposed to
summaryWO.hce(), which works without grouping by this
variable.Details have been added regarding the implementation of the
simKHCE() function. The function has been updated to return
all time-to-event outcomes for each patient in the ADET
dataset.
Examples have been added to the 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.
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.
Need a high-speed mirror for your open-source project?
Contact our mirror admin team at info@clientvps.com.
This archive is provided as a free public service to the community.
Proudly supported by infrastructure from VPSPulse , RxServers , BuyNumber , UnitVPS , OffshoreName and secure payment technology by ArionPay.