Package: nph 2.1

nph: Planning and Analysing Survival Studies under Non-Proportional Hazards

Piecewise constant hazard functions are used to flexibly model survival distributions with non-proportional hazards and to simulate data from the specified distributions. A function to calculate weighted log-rank tests for the comparison of two hazard functions is included. Also, a function to calculate a test using the maximum of a set of test statistics from weighted log-rank tests (MaxCombo test) is provided. This test utilizes the asymptotic multivariate normal joint distribution of the separate test statistics. The correlation is estimated from the data. These methods are described in Ristl et al. (2021) <doi:10.1002/pst.2062>. Finally, a function is provided for the estimation and inferential statistics of various parameters that quantify the difference between two survival curves. Eligible parameters are differences in survival probabilities, log survival probabilities, complementary log log (cloglog) transformed survival probabilities, quantiles of the survival functions, log transformed quantiles, restricted mean survival times, as well as an average hazard ratio, the Cox model score statistic (logrank statistic), and the Cox-model hazard ratio. Adjustments for multiple testing and simultaneous confidence intervals are calculated using a multivariate normal approximation to the set of selected parameters.

Authors:Robin Ristl [aut, cre], Nicolas Ballarini [ctb]

nph_2.1.tar.gz
nph_2.1.zip(r-4.7)nph_2.1.zip(r-4.6)nph_2.1.zip(r-4.5)
nph_2.1.tgz(r-4.6-any)nph_2.1.tgz(r-4.5-any)
nph_2.1.tar.gz(r-4.7-any)nph_2.1.tar.gz(r-4.6-any)
nph_2.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION
card.svg |card.png
nph/json (API)

# Install 'nph' in R:
install.packages('nph', repos = c('https://rmaster1.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • pembro - Reconstructed Data Set Based On Survival Curves In Burtess et al. 2019

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

5.99 score 3 packages 2.7k scripts 593 downloads 2 mentions 15 exports 28 dependencies

Last updated from:e2fa54d4e7. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK158
source / vignettesOK272
linux-release-x86_64OK167
macos-release-arm64OK112
macos-oldrel-arm64OK104
windows-develOK119
windows-releaseOK101
windows-oldrelOK123
wasm-releaseOK99

Exports:logrank.maxtestlogrank.testm2rnph_guinphparamspchazplot_diagramplot_shhrplot_subgroupspop_pchazrSurv_conditional_funrSurv_funsample_conditional_funsample_funsubpop_pchaz

Dependencies:clicodetoolscpp11farverggplot2gluegtableisobandlabelinglatticelifecycleMASSMatrixmuhazmultcompmvtnormR6RColorBrewerrlangS7sandwichscalessurvivalTH.datavctrsviridisLitewithrzoo

| nph Examples: Delayed treatment effects, treatment switches and heterogeneous patient populations: how to design and analyse RCTs in oncology
Progression of disease | Subgroup with differential effect | Delayed Effect | Delayed Effect and Subgroup with differential effect | Treatment Switchers | Harmful drug in a subgroup but beneficial in the complement. | Switchers and subgroup with differential effect.

Last update: 2020-01-10
Started: 2019-08-23

Introduction to nph and Usage Instructions
Overview | Installation | Getting started | Basics | Creating the population model with pop_pchaz | Creating a simulated dataset with sample_fun | The weighted log-rank test and the max-LRtest

Last update: 2020-01-10
Started: 2019-08-23