Package: OncoBayes2 0.9-1
OncoBayes2: Bayesian Logistic Regression for Oncology Dose-Escalation Trials
Bayesian logistic regression model with optional EXchangeability-NonEXchangeability parameter modelling for flexible borrowing from historical or concurrent data-sources. The safety model can guide dose-escalation decisions for adaptive oncology Phase I dose-escalation trials which involve an arbitrary number of drugs. Please refer to Neuenschwander et al. (2008) <doi:10.1002/sim.3230> and Neuenschwander et al. (2016) <doi:10.1080/19466315.2016.1174149> for details on the methodology.
Authors:
OncoBayes2_0.9-1.tar.gz
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OncoBayes2.pdf |OncoBayes2.html✨
OncoBayes2/json (API)
NEWS
| # Install 'OncoBayes2' in R: |
| install.packages('OncoBayes2', repos = c('https://weberse2.r-universe.dev', 'https://cloud.r-project.org')) |
- codata_combo2 - Dataset: historical and concurrent data on a two-way combination
- dose_info_combo2 - Dataset: trial dose information for a dual-agent combination study
- drug_info_combo2 - Dataset: drug information for a dual-agent combination study
- hist_SA - Single-agent example
- hist_combo2 - Dataset: historical data on two single-agents to inform a combination study
- hist_combo3 - Dataset: historical and concurrent data on a three-way combination
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 15 days agofrom:3b170fea0b. Checks:5 OK, 7 NOTE. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 17 2025 |
| R-4.5-win-x86_64 | OK | Mar 17 2025 |
| R-4.5-mac-x86_64 | OK | Mar 17 2025 |
| R-4.5-mac-aarch64 | OK | Mar 17 2025 |
| R-4.5-linux-x86_64 | OK | Mar 17 2025 |
| R-4.4-win-x86_64 | NOTE | Mar 17 2025 |
| R-4.4-mac-x86_64 | NOTE | Mar 17 2025 |
| R-4.4-mac-aarch64 | NOTE | Mar 17 2025 |
| R-4.4-linux-x86_64 | NOTE | Mar 17 2025 |
| R-4.3-win-x86_64 | NOTE | Mar 17 2025 |
| R-4.3-mac-x86_64 | NOTE | Mar 17 2025 |
| R-4.3-mac-aarch64 | NOTE | Mar 17 2025 |
Exports:as_drawsas_draws_arrayas_draws_dfas_draws_listas_draws_matrixas_draws_rvarsbind_rows_0blrm_exnexblrm_formula_linearblrm_formula_saturatingblrm_trialcritical_quantileexample_modelinv_logitlog_posteriorlogitneff_rationsamplesnuts_paramsplot_toxicity_curveplot_toxicity_intervalsplot_toxicity_intervals_stackedposterior_intervalposterior_linpredposterior_predictpredictive_intervalprior_summaryrhat
Dependencies:abindassertthatbackportsbayesplotBHbridgesamplingbrmsBrobdingnagcallrcheckmateclicodacodetoolscolorspacecpp11descdigestdistributionaldplyrfansifarverFormulafuturefuture.applygenericsggplot2ggridgesglobalsgluegridExtragtableinlineisobandlabelinglatticelifecyclelistenvloomagrittrMASSMatrixmatrixStatsmgcvmunsellmvtnormnleqslvnlmenumDerivparallellypillarpkgbuildpkgconfigplyrposteriorprocessxpspurrrQuickJSRR6RBesTRColorBrewerRcppRcppEigenRcppParallelreshape2rlangrstanrstantoolsscalesStanHeadersstringistringrtensorAtibbletidyrtidyselectutf8vctrsviridisLitewithr
Guiding Oncology Dose-Escalation Trials
Rendered fromintroduction.Rmdusingknitr::rmarkdownon Mar 17 2025.Last update: 2025-03-01
Started: 2019-08-29
Meta-Analytic-Predictive (MAP) approach for dose-toxicity modelling
Rendered frommap_approach.Rmdusingknitr::rmarkdownon Mar 17 2025.Last update: 2025-03-17
Started: 2025-03-17
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Bind rows of multiple data frames with zero fill | bind_rows_0 |
| Bayesian Logistic Regression Model for N-compounds with EXNEX | blrm_exnex print.blrmfit |
| Build a BLRM formula with linear interaction term in logit-space | blrm_formula_linear |
| Build a BLRM formula with saturating interaction term in logit-space | blrm_formula_saturating |
| Dose-Escalation Trials guided by Bayesian Logistic Regression Model | blrm_trial print.blrm_trial |
| Dataset: historical and concurrent data on a two-way combination | codata_combo2 |
| Critical quantile | critical_quantile critical_quantile.blrmfit critical_quantile.blrm_trial |
| Extract Diagnostic Quantities of 'OncoBayes2' Models | diagnostic-quantities log_posterior log_posterior.blrmfit neff_ratio neff_ratio.blrmfit nuts_params nuts_params.blrmfit rhat rhat.blrmfit |
| Dataset: trial dose information for a dual-agent combination study | dose_info_combo2 |
| Transform 'blrmfit' to 'draws' objects | as_draws as_draws.blrmfit as_draws_array as_draws_array.blrmfit as_draws_df as_draws_df.blrmfit as_draws_list as_draws_list.blrmfit as_draws_matrix as_draws_matrix.blrmfit as_draws_rvars as_draws_rvars.blrmfit draws-OncoBayes2 |
| Dataset: drug information for a dual-agent combination study | drug_info_combo2 |
| Runs example models | example_model |
| Two-drug combination example | example-combo2 |
| Two-drug combination example using BLRM Trial | example-combo2_trial |
| Three-drug combination example | example-combo3 |
| Single Agent Example | example-single-agent |
| Dataset: historical data on two single-agents to inform a combination study | hist_combo2 |
| Dataset: historical and concurrent data on a three-way combination | hist_combo3 |
| Single-agent example | hist_SA |
| Logit (log-odds) and inverse-logit function. | inv_logit lodds logit |
| Return the number of posterior samples | nsamples nsamples.blrmfit |
| OncoBayes2 | OncoBayes2-package OncoBayes2 |
| Plot a fitted model | plot_blrm plot_toxicity_curve plot_toxicity_curve.blrmfit plot_toxicity_curve.blrm_trial plot_toxicity_intervals plot_toxicity_intervals.blrmfit plot_toxicity_intervals.blrm_trial plot_toxicity_intervals_stacked plot_toxicity_intervals_stacked.blrmfit plot_toxicity_intervals_stacked.blrm_trial |
| Posterior intervals | posterior_interval posterior_interval.blrmfit |
| Posterior of linear predictor | posterior_linpred posterior_linpred.blrmfit |
| Posterior of predictive | posterior_predict posterior_predict.blrmfit |
| Posterior predictive intervals | predictive_interval predictive_interval.blrmfit |
| Summarise model prior | prior_summary prior_summary.blrmfit |
| Summarise trial | summary.blrm_trial |
| Summarise model results | summary.blrmfit |
| Update data and/or prior of a BLRM trial | update.blrm_trial |
| Update data of a BLRM analysis | update.blrmfit |
