Package: expertsurv 1.4.1

Philip Cooney

expertsurv: Incorporate Expert Opinion with Parametric Survival Models

Enables users to incorporate expert opinion with parametric survival analysis using a Bayesian or frequentist approach. Expert Opinion can be provided on the survival probabilities at certain time-point(s) or for the difference in mean survival between two treatment arms. Please reference it's use as Cooney, P., White, A. (2023) <doi:10.1177/0272989X221150212>.

Authors:Philip Cooney [aut, cre], Arthur White [ths]

expertsurv_1.4.1.tar.gz
expertsurv_1.4.1.tar.gz(r-4.7-arm64)expertsurv_1.4.1.tar.gz(r-4.7-x86_64)expertsurv_1.4.1.tar.gz(r-4.6-arm64)expertsurv_1.4.1.tar.gz(r-4.6-x86_64)
expertsurv_1.4.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
expertsurv/json (API)

# Install 'expertsurv' in R:
install.packages('expertsurv', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • bc - Breast cancer survival data
  • bosms3 - Bronchiolitis obliterans syndrome after lung transplants
  • bosms4 - Bronchiolitis obliterans syndrome after lung transplants
  • data - A fictional survival trial taken directly from survHE.

On CRAN:

Conda:

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

cpp

3.00 score 1 scripts 252 downloads 8 exports 172 dependencies

Last updated from:89c5133b8f. Checks:6 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK279
linux-devel-x86_64OK270
source / vignettesOK310
linux-release-arm64OK236
linux-release-x86_64OK256
wasm-releaseOK216

Exports:compile_stancred_intelicit_survfit.models.expertmake.survmodel.fit.plotplot_expert_opinionpsa.plot

Dependencies:abindassertthatbackportsbase64encbbmlebdsmatrixBHbootbroombslibcachemcallrcarcarDatacheckmatecliclustercodetoolscolorspacecolourpickercommonmarkcorrplotcowplotcpp11curldata.tableDerivdescdeSolvedigestdistributionaldoBydplyrevaluateexactRankTestsfarverfastGHQuadfastmapflexsurvfontawesomeforecastforeignFormulafracdifffsgenericsggExtraggplot2ggpubrggrepelggridgesggsciggsignifggtextgluegridExtragridtextgtablehighrHmischtmlTablehtmltoolshtmlwidgetshttpuvinlineisobandjpegjquerylibjsonliteknitrlabelinglaterlatticelifecyclelitedownlme4lmtestloolsodamagrittrmarkdownMASSMatrixMatrixModelsmatrixStatsmaxstatmemoisemgcvmicrobenchmarkmimeminiUIminqamnormtmodelrmstatemuhazmultcompmvtnormnlmenloptrnnetnumDerivotelpbkrtestpillarpkgbuildpkgconfigpngpolsplinepolynomposteriorprocessxpromisespspurrrquadprogquantregQuickJSRR6rappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRcppParallelRdpackreformulasrlangrmarkdownrmsrpartrstanrstantoolsrstatixrstpm2rstudioapiS7sandwichsassscalesSHELFshinyshinyjsshinyMatrixsnsourcetoolsSparseMsplines2StanHeadersstatmodstringistringrsurvivalsurvminertensorATH.datatibbletidyrtidyselecttimeDatetinytexurcautf8vctrsviridisLitewithrxfunxml2xtableyamlzoo

Introduction to Expertsurv
expertsurv | Installation | Expert Opinion on Survival at timepoints | Expert Opinion using Penalized Maximum Likelihood | Expert Opinion on Survival of a comparator arm | Expert Opinion on Survival Difference | Compatability with underlying packages survHE and flexsurv | Model Diagnostics | General Population Mortality | Setting Initial values to estimate models (Particularly Gompertz) | Technical note on the impact of priors | Potential Future Updates | References
Rendered fromExpertsurv-Vignette.Rmdusingknitr::rmarkdown

Last update: 2025-02-20
Started: 2023-02-11

Shiny Application for expertsurv
References
Rendered fromShinyExpertsurv-Vignette.Rmdusingknitr::rmarkdown

Last update: 2025-02-20
Started: 2023-10-04