Package: EventWinRatios
Type: Package
Title: Event-Specific Win Ratios for Terminal and Non-Terminal Events
Version: 1.0.0
Authors@R: c(
person(given = "Daewoo", family = "Pak", email = "dpak@yonsei.ac.kr", role = c("aut", "cre")),
person(given = "Song", family = "Yang", email = "yangso@nhlbi.nih.gov", role = c("aut"))
)
Maintainer: Daewoo Pak <dpak@yonsei.ac.kr>
Description: Provides several confidence interval and testing procedures using
event-specific win ratios for semi-competing risks data with non-terminal
and terminal events, as developed in Yang et al. (2021<doi:10.1002/sim.9266>).
Compared with conventional methods for survival data, these procedures are
designed to utilize more data for improved inference procedures with
semi-competing risks data. The event-specific win ratios were introduced in
Yang and Troendle (2021<doi:10.1177/1740774520972408>). In this package,
the event-specific win ratios and confidence intervals are obtained for each
event type, and several testing procedures are developed for the global null
of no treatment effect on either terminal or non-terminal events. Furthermore,
a test of proportional hazard assumptions, under which the event-specific win
ratios converge to the hazard ratios, and a test of equal hazard ratios are
provided. For summarizing the treatment effect on all events, confidence
intervals for linear combinations of the event-specific win ratios are available
using pre-determined or data-driven weights. Asymptotic properties of these
inference procedures are discussed in Yang et al (2021<doi:10.1002/sim.9266>).
Also, transformations are used to yield better control of the type one error
rates for moderately sized data sets.
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
Packaged: 2022-04-04 06:49:27 UTC; dpak
Author: Daewoo Pak [aut, cre],
Song Yang [aut]
Repository: CRAN
Date/Publication: 2022-04-04 08:30:06 UTC

Title: Synthesizing Causal Evidence in a Distributed Research Network
Description: Routines for combining causal effect estimates and study diagnostics across multiple data sites in a distributed study, without sharing patient-level data.
Allows for normal and non-normal approximations of the data-site likelihood of the effect parameter.
Imports: ggplot2, gridExtra, meta, EmpiricalCalibration, rJava,
BeastJar, Cyclops (>= 3.1.0), HDInterval, rlang, methods
Suggests: knitr, rmarkdown, testthat, sn