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Package: bonsaiforest 0.1.1

Isaac Gravestock

bonsaiforest: Shrinkage Based Forest Plots

Subgroup analyses are routinely performed in clinical trial analyses. From a methodological perspective, two key issues of subgroup analyses are multiplicity (even if only predefined subgroups are investigated) and the low sample sizes of subgroups which lead to highly variable estimates, see e.g. Yusuf et al (1991) <doi:10.1001/jama.1991.03470010097038>. This package implements subgroup estimates based on Bayesian shrinkage priors, see Carvalho et al (2019) <https://proceedings.mlr.press/v5/carvalho09a.html>. In addition, estimates based on penalized likelihood inference are available, based on Simon et al (2011) <doi:10.18637/jss.v039.i05>. The corresponding shrinkage based forest plots address the aforementioned issues and can complement standard forest plots in practical clinical trial analyses.

Authors:Mar Vazquez Rabunal [aut], Daniel Sabanés Bové [aut], Marcel Wolbers [aut], Isaac Gravestock [cre], F. Hoffmann-La Roche AG [cph, fnd]

bonsaiforest_0.1.1.tar.gz
bonsaiforest_0.1.1.zip(r-4.7-x86_64)bonsaiforest_0.1.1.zip(r-4.7-arm64)bonsaiforest_0.1.1.zip(r-4.6-x86_64)bonsaiforest_0.1.1.zip(r-4.6-arm64)
bonsaiforest_0.1.1.tar.gz(r-4.7-arm64)bonsaiforest_0.1.1.tar.gz(r-4.7-x86_64)bonsaiforest_0.1.1.tar.gz(r-4.6-arm64)bonsaiforest_0.1.1.tar.gz(r-4.6-x86_64)
bonsaiforest_0.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
bonsaiforest/json (API)

# Install 'bonsaiforest' in R:

install.packages('bonsaiforest', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/insightsengineering/bonsaiforest/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3
openmp– GCC OpenMP (GOMP) support library

Datasets:

design_dummy1 - Design Dummy Subgroup x_1a
design_matrix1 - Design Matrix Subgroup x_1a
elastic_net_fit_bin - Elastic Net Fit Binary
elastic_net_fit_surv - Elastic Net Fit Survival
elastic_net_surv - H0, Coefficients Elastic Net Survival Model and Matrices
est_coef_bin1 - Estimated coefficients elastic net Binary
example_data - Example data
horseshoe_fit_bin - Horseshoe Fit Binary
horseshoe_fit_surv - Horseshoe Fit Survival
naive_fit_bin - Naive Fit Binary
naive_fit_surv - Naive Fit Survival
naivepop_fit_bin - Naivepop Fit Binary
naivepop_fit_surv - Naivepop Fit Survival

On CRAN:

openblas cpp openmp

2.11 score 13 scripts 356 downloads 16 exports 104 dependencies

Last updated from:ca4e0b4513. Checks:6 OK, 4 ERROR. Indexed: no.

Target	Result	Time
linux-devel-arm64	OK	349
linux-devel-x86_64	OK	431
source / vignettes	OK	265
linux-release-arm64	OK	305
linux-release-x86_64	OK	266
windows-devel-arm64	ERROR	390
windows-devel-x86_64	ERROR	414
windows-release-arm64	ERROR	296
windows-release-x86_64	ERROR	281
wasm-release	OK	169

Exports:ahr_estimation ahr_from_km compare elastic_net generate_stacked_data horseshoe lor_estimation naive naivepop preprocess simul_covariates simul_data simul_pfs subgroups survival_curves trt_horseshoe

Dependencies:abind backports base64enc bayesplot BH bridgesampling brio brms Brobdingnag broom callr checkmate cli coda codetools cpp11 crayon desc diffobj digest distributional dplyr evaluate farver fastmap forcats foreach fs future future.apply gbm generics ggdist ggplot2 ggridges glmnet globals glue gridExtra gtable htmltools inline isoband iterators jsonlite labeling lattice lifecycle listenv loo magrittr MASS Matrix matrixStats mgcv mvtnorm nleqslv nlme numDeriv otel parallelly pillar pkgbuild pkgconfig pkgload plyr posterior praise processx ps purrr quadprog QuickJSR R6 RColorBrewer Rcpp RcppArmadillo RcppEigen RcppParallel reshape2 rlang rprojroot rstan rstantools S7 scales shape splines2 StanHeaders stringi stringr survival tensorA testthat tibble tidyr tidyselect utf8 vctrs vdiffr viridisLite waldo withr xml2

Introduction

Rendered fromintroduction.Rmdusingknitr::rmarkdown

Last update: 2024-06-18
Started: 2024-06-18

Help page	Topics
Average Hazard Ratio Estimation	ahr_estimation
Average Hazard Estimation based on Kaplan-Meier Estimates	ahr_from_km
Compare Treatment Estimate Methods	compare
Design Dummy Subgroup x_1a	design_dummy1
Design Matrix Subgroup x_1a	design_matrix1
Elastic Net Penalization Model Estimation	elastic_net
Elastic Net Fit Binary	elastic_net_fit_bin
Elastic Net Fit Survival	elastic_net_fit_surv
H0, Coefficients Elastic Net Survival Model and Matrices	elastic_net_surv
Estimated coefficients elastic net Binary	est_coef_bin1
Example data	example_data
Generation of Stacked Data by Subgroups	generate_stacked_data
Bayesian Shrinkage Model Estimation	horseshoe
Horseshoe Fit Binary	horseshoe_fit_bin
Horseshoe Fit Survival	horseshoe_fit_surv
Estimation of Log-Odds Ratio	lor_estimation
Naive Model Estimation	naive
Naive Fit Binary	naive_fit_bin
Naive Fit Survival	naive_fit_surv
Naive Overall Population Model Estimation	naivepop
Naivepop Fit Binary	naivepop_fit_bin
Naivepop Fit Survival	naivepop_fit_surv
Compare Forest Plots	plot.compare.data
Forest plot Summary Elastic Net	plot.summary.elastic_net
Forest plot Summary Horseshoe	plot.summary.horseshoe
Forest plot Summary Naive	plot.summary.naive
Data Preprocessing	preprocess
Print Function for Elastic Net Summary	print.summary.elastic_net
Print Function for Horseshoe Summary	print.summary.horseshoe
Print Function for Naive Summary	print.summary.naive
Print Function for Naivepop Summary	print.summary.naivepop
Generation of a Design Matrix for Simulations	simul_covariates
Simulate Covariates and Progression Free Survival Data	simul_data
Simulation of Progression Free Survival Times	simul_pfs
Subgroup Treatment Effect	subgroups
Summary Elastic Net Function	summary.elastic_net
Summary Horseshoe Function	summary.horseshoe
Summary Naive	summary.naive
Summary Naivepop Function	summary.naivepop
Average Survival Curves	survival_curves
Subgroup Treatment Effect Horseshoe	trt_horseshoe

Package: bonsaiforest 0.1.1

bonsaiforest: Shrinkage Based Forest Plots

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)