Package: blendR 1.0.0

Zhaojing Che

blendR: Blended Survival Curves

Create a blended curve from two survival curves, which is particularly useful for survival extrapolation in health technology assessment. The main idea is to mix a flexible model that fits the observed data well with a parametric model that encodes assumptions about long-term survival. The two curves are blended into a single survival curve that is identical to the first model over the range of observed times and gradually approaches the parametric model over the extrapolation period based on a given weight function. This approach allows for the inclusion of external information, such as data from registries or expert opinion, to guide long-term extrapolations, especially when dealing with immature trial data. See Che et al. (2022) <doi:10.1177/0272989X221134545>.

Authors:Nathan Green [aut], Zhaojing Che [aut, cph, cre]

blendR_1.0.0.tar.gz
blendR_1.0.0.zip(r-4.7-any)blendR_1.0.0.zip(r-4.6-any)blendR_1.0.0.zip(r-4.5-any)
blendR_1.0.0.tgz(r-4.6-any)blendR_1.0.0.tgz(r-4.5-any)
blendR_1.0.0.tar.gz(r-4.7-any)blendR_1.0.0.tar.gz(r-4.6-any)
blendR_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
blendR/json (API)

# Install 'blendR' in R:
install.packages('blendR', repos = c('https://statisticshealtheconomics.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/statisticshealtheconomics/blendr/issues

Uses libs:
  • openjdk– OpenJDK Java runtime, using Hotspot JIT
Datasets:

On CRAN:

Conda:

openjdk

5.11 score 1 stars 13 scripts 412 downloads 5 exports 103 dependencies

Last updated from:096b73f350. Checks:7 ERROR, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develERROR841
source / vignettesOK815
linux-releaseERROR596
macos-releaseERROR222
macos-oldrelERROR255
windows-develERROR223
windows-releaseERROR230
windows-oldrelERROR259
wasm-releaseOK779

Exports:blendsurvext_surv_simfit_inla_pwmake_survweightplot

Dependencies:assertthatbackportsbase64encbbmlebdsmatrixbslibcachemcheckmatecliclustercodetoolscolorspacecpp11data.tabledeSolvedigestdplyrevaluatefarverfastGHQuadfastmapflexsurvfontawesomeforeignFormulafsgenericsggplot2gluegridExtragtablehighrHmischtmlTablehtmltoolshtmlwidgetsisobandjquerylibjsonliteknitrlabelinglatticelifecyclelsodamagrittrmanipulateMASSMatrixMatrixModelsmemoisemgcvmimemnormtmstatemuhazmultcompmvtnormnlmennetnumDerivpillarpkgconfigpolsplinepurrrquadprogquantregR6rappdirsRColorBrewerRcppRcppArmadillorJavarlangrmarkdownrmsrpartrstpm2rstudioapiS7sandwichsassscalessnSparseMstatmodstringistringrsurvHEsurvivalTH.datatibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunxlsxxlsxjarsyamlzoo

Basic examples
Introduction | Example 1: INLA piece-wise exponential and external knowledge | Prepare the models | Blend the curves | Example 2: Blending two HMC survHE models | Example 3: Blending HMC and frequentist models
Rendered frombasic-examples.Rmdusingknitr::rmarkdown

Last update: 2026-09-03
Started: 2025-01-22

{expertsurv} package examples
{expertsurv} background fit | Using sample size formula
Rendered fromexpertsurv-example.Rmdusingknitr::rmarkdown

Last update: 2026-07-30
Started: 2025-01-22