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hce

Simulate and analyze hierarchical composite endpoints

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You can install hce from CRAN:

install.packages("hce")

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install.packages('hce')

Monthly Downloads

296

Version

0.7.2

License

MIT + file LICENSE

Maintainer

Samvel B. Gasparyan

Last Published

May 14th, 2025

Functions in hce (0.7.2)

calcWO

A generic function for calculating win odds
calcWINS.hce

Win statistics calculation for hce objects
calcWINS

A generic function for calculating win statistics
calcWINS.data.frame

Win statistics calculation using a data frame
KHCE

Kidney Hierarchical Composite Endpoint dataset.
IWP

Calculates patient-level individual win proportions
as_hce

A generic function for coercing data structures to hce objects
print.hce_results

A print method for hce_results objects
hce

Helper function for hce objects
calcWO.hce

Win odds calculation for hce objects
propWINS

Proportion of wins/losses/ties given the win odds and the win ratio
minWO

Minimum detectable or WO for alternative hypothesis for given power (no ties)
as_hce.data.frame

Coerce a data frame to an hce object
plot.hce_results

A print method for hce_results objects
powerWO

Power calculation for the win odds test (no ties)
plot.hce

A plot method for hce objects
calcWO.formula

Win odds calculation using formula syntax
calcWO.data.frame

Win odds calculation using a data frame
regWO.formula

Win Odds Regression Using a Formula Syntax
simHCE

Simulate hce object with given event rates of time-to-event outcomes (Weibull), mean and SD of the continuous outcome (normal or log-normal) by treatment group
simORD

Simulate ordinal variables for two treatment groups using categorization of beta distributions
stratWO.data.frame

Stratified win odds with adjustment
stratWO

A generic function for stratified win odds with adjustment
summaryWO

A generic function for summarizing win odds
summaryWO.data.frame

Win odds summary for a data frame
simADHCE

Simulate adhce object with given event rates of time-to-event outcomes (Weibull), mean and SD of the continuous outcome (normal or log-normal) by treatment group
regWO

A generic function for win odds regression
sizeWO

Sample size calculation for the win odds test (no ties)
sizeWR

Sample size calculation for the win ratio test (with WR = 1 null hypothesis)
regWO.data.frame

Win Odds Regression Using a Data Frame
summaryWO.formula

Win odds summary using formula syntax
summaryWO.hce

Win odds summary for hce objects
HCE3

HCE1, HCE2, HCE3, HCE4 datasets for 1000 patients with different treatment effects.
ADSL

Baseline characteristics dataset of patients with kidney function assessments.
HCE2

HCE1, HCE2, HCE3, HCE4 datasets for 1000 patients with different treatment effects.
calcWINS.formula

Win statistics calculation using formula syntax
COVID19plus

COVID-19 ordinal scale dataset for a combination therapy.
COVID19b

COVID-19 ordinal scale dataset (preliminary report).
COVID19

COVID-19 ordinal scale dataset (full report).
HCE4

HCE1, HCE2, HCE3, HCE4 datasets for 1000 patients with different treatment effects.
as_hce.default

Coerce a data frame to an hce object
ADLB

Laboratory dataset for Glomerular Filtration Rate (GFR) measurements.
ADET

Event-Time dataset for kidney outcomes.
HCE1

HCE1, HCE2, HCE3, HCE4 datasets for 1000 patients with different treatment effects.