{ "_id": "6ae3322995ca2370cc8ce100", "Package": "trialr", "Version": "0.1.6", "Date": "2023-03-11", "Title": "Clinical Trial Designs in 'rstan'", "Description": "A collection of clinical trial designs and methods,\nimplemented in 'rstan' and R, including: the Continual\nReassessment Method by O'Quigley et al. (1990)\n; EffTox by Thall & Cook (2004)\n; the two-parameter\nlogistic method of Neuenschwander, Branson & Sponer (2008)\n; and the Augmented Binary method by\nWason & Seaman (2013) ; and more. We\nprovide functions to aid model-fitting and analysis. The\n'rstan' implementations may also serve as a cookbook to anyone\nlooking to extend or embellish these models. We hope that this\npackage encourages the use of Bayesian methods in clinical\ntrials. There is a preponderance of early phase trial designs\nbecause this is where Bayesian methods are used most. If there\nis a method you would like implemented, please get in touch.", "Authors@R": "c(\nperson(\"Kristian\", \"Brock\",\nemail = \"kristian.brock@gmail.com\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-3921-0166\")),\nperson(\"Trustees of\", \"Columbia University\",\nrole = \"cph\"))", "Maintainer": "Kristian Brock ", "License": "GPL (>= 3)", "Encoding": "UTF-8", "ByteCompile": "true", "SystemRequirements": "GNU make", "NeedsCompilation": "yes", "RoxygenNote": "7.1.2", "VignetteBuilder": "knitr", "URL": "https://github.com/brockk/trialr", "BugReports": "https://github.com/brockk/trialr/issues", "Packaged": { "Date": "2026-10-29 11:23:44 UTC", "User": "root" }, "Author": "Kristian Brock [aut, cre]\n(), Trustees of Columbia\nUniversity [cph]", "Config/pak/sysreqs": "make", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2023-03-12 10:00:02 UTC", "RemoteUrl": "https://github.com/cran/trialr", "RemoteRef": "HEAD", "RemoteSha": "a1cf05da8f318ec4a0dcb7f80b3ea3a33eb3abff", "_user": "cran", "_type": "src", "_file": "trialr_0.1.6.tar.gz", "_fileid": "https://r2.ropensci.org/5a669690ab62343c98d87bd21f2fff9daad076b169e6cec882da6d87f94a7d2e", "_filesize": 3584559, "_sha256": "5a669690ab62343c98d87bd21f2fff9daad076b169e6cec882da6d87f94a7d2e", "_expires": "2027-02-06T11:46:47.000Z", "_created": "2026-10-29T11:23:44.000Z", "_published": "2026-10-29T11:46:49.154Z", "_upstream": "https://github.com/cran/trialr", "_status": "success", "_maintainer": { "name": "Kristian Brock", "email": "kristian.brock@gmail.com", "login": "brockk", "description": "I am a clinical trial statistician, with a research focus on efficient methods for early phase trials, especially Bayesian methods. I write in R, Python & Stan.", "uuid": 10177935, "orcid": "0000-0002-3921-0166" }, "_jobs": [ { "job": 123420725671, "time": 597, "config": "linux-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "12894526302" }, { "job": 123420725528, "time": 459, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "12894271459" }, { "job": 123420725820, "time": 516, "config": "linux-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "12894277181" }, { "job": 123420725818, "time": 668, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "12893649578" }, { "job": 123412347429, "time": 1140, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "12893422283" }, { "job": 123420725327, "time": 420, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "12893179919" }, { "job": 123420725756, "time": 640, "config": "windows-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "12893847870" }, { "job": 123420725614, "time": 782, 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"weights_at_dose" ], "_help": [ { "page": "trialr-package", "title": "The 'trialr' package.", "topics": [ "trialr-package", "trialr" ] }, { "page": "as_tibble.augbin_2t_1a_fit", "title": "Cast 'augbin_2t_1a_fit' object to 'tibble'.", "topics": [ "as_tibble.augbin_2t_1a_fit" ] }, { "page": "as_tibble.dose_finding_paths", "title": "Cast 'dose_finding_paths' object to 'tibble'.", "topics": [ "as_tibble.dose_finding_paths" ] }, { "page": "as.data.frame.crm_fit", "title": "Convert crm_fit object to 'data.frame'.", "topics": [ "as.data.frame.crm_fit" ] }, { "page": "as.data.frame.efftox_fit", "title": "Convert efftox_fit object to 'data.frame'.", "topics": [ "as.data.frame.efftox_fit" ] }, { "page": "as.mcmc.list.crm_fit", "title": "Convert 'crm_fit' to instance of 'mcmc.list'", "topics": [ "as.mcmc.list.crm_fit" ] }, { "page": "as.mcmc.list.efftox_fit", "title": "Convert 'efftox_fit' to instance of 'mcmc.list'", "topics": [ "as.mcmc.list.efftox_fit" ] }, { "page": "augbin_2t_1a_fit", "title": "Class used by 'trialr' to fit Wason & Seaman's Augmented Binary method in single arm trials with two post-baseline tumour assessments.", "topics": [ "augbin_2t_1a_fit" ] }, { "page": "augbin_fit", "title": "Class used by 'trialr' to fit Wason & Seaman's Augmented Binary method.", "topics": [ "augbin_fit" ] }, { "page": "binary_prob_success", "title": "Calculate the binary probability of success.", "topics": [ "binary_prob_success", "binary_prob_success.augbin_2t_1a_fit" ] }, { "page": "careful_escalation", "title": "Dose selection function that practices careful escalation.", "topics": [ "careful_escalation" ] }, { "page": "closest_to_target", "title": "Get index of element in vector with value closest to a target", "topics": [ "closest_to_target" ] }, { "page": "crm_codified_dose_logistic", "title": "Calculate codified CRM doses.", "topics": [ "crm_codified_dose_logistic" ] }, { "page": "crm_dtps", "title": "Calculate dose-transition pathways for a CRM study", "topics": [ "crm_dtps" ] }, { "page": "crm_fit-class", "title": "Class of model fit by 'trialr' using the CRM dose-finding design.", "topics": [ "crm_fit", "crm_fit-class" ] }, { "page": "crm_params-class", "title": "Container class for parameters to fit the CRM models in trialr.", "topics": [ "crm_params", "crm_params-class" ] }, { "page": "crm_path_analysis", "title": "Fit a CRM model to the incrementally observed outcomes on a trial pathway.", "topics": [ "crm_path_analysis" ] }, { "page": "crm_prior_beliefs", "title": "Get the prior beliefs for a CRM trial scenario.", "topics": [ "crm_prior_beliefs" ] }, { "page": "crm_process", "title": "Process RStan samples from a CRM model.", "topics": [ "crm_process" ] }, { "page": "df_parse_outcomes", "title": "Parse a string of dose-finding trial outcomes to binary vector notation.", "topics": [ "df_parse_outcomes" ] }, { "page": "dose_finding_fit-class", "title": "Class of dose-finding model fit by 'trialr' using Stan.", "topics": [ "dose_finding_fit", "dose_finding_fit-class" ] }, { "page": "dose_finding_path_node-class", "title": "Class to hold the elements of a single dose-finding analysis residing in a pathway of analyses.", "topics": [ "dose_finding_path_node", "dose_finding_path_node-class" ] }, { "page": "eff_at_dose", "title": "Get the number of efficacy events seen at the doses under investigation.", "topics": [ "eff_at_dose", "eff_at_dose.efftox_fit" ] }, { "page": "efftox_analysis_to_df", "title": "EffTox analysis to data.frame", "topics": [ "efftox_analysis_to_df" ] }, { "page": "efftox_contour_plot", "title": "Plot EffTox utility contours", "topics": [ "efftox_contour_plot" ] }, { "page": "efftox_dtps", "title": "Calculate dose-transition pathways for an EffTox study", "topics": [ "efftox_dtps" ] }, { "page": "efftox_dtps_to_dataframe", "title": "Calculate dose-transition pathways for an EffTox study", "topics": [ "efftox_dtps_to_dataframe" ] }, { "page": "efftox_fit-class", "title": "Class of model fit by 'trialr' using the EffTox dose-finding design.", "topics": [ "efftox_fit", "efftox_fit-class" ] }, { "page": "efftox_get_tox", "title": "Get the Prob(Tox) for Prob(Eff) and utility pairs", "topics": [ "efftox_get_tox" ] }, { "page": "efftox_parameters_demo", "title": "Get parameters to run the EffTox demo", "topics": [ "efftox_parameters_demo" ] }, { "page": "efftox_params-class", "title": "Container class for parameters to fit the EffTox model in trialr.", "topics": [ "efftox_params", "efftox_params-class" ] }, { "page": "efftox_parse_outcomes", "title": "Parse a string of EffTox outcomes to binary vector notation.", "topics": [ "efftox_parse_outcomes" ] }, { "page": "efftox_path_analysis", "title": "Fit an EffTox model to the incrementally observed outcomes on a trial pathway.", "topics": [ "efftox_path_analysis" ] }, { "page": "efftox_priors", "title": "Simple class to hold prior hyperparameters for the EffTox model.", "topics": [ "efftox_priors" ] }, { "page": "efftox_process", "title": "Process RStan samples from an EffTox model", "topics": [ "efftox_process" ] }, { "page": "efftox_simulate", "title": "Run EffTox simulations", "topics": [ "efftox_simulate" ] }, { "page": "efftox_solve_p", "title": "Calculate the p-index for EffTox utility contours", "topics": [ "efftox_solve_p" ] }, { "page": "efftox_superiority", "title": "Get dose-superiority matrix in EffTox", "topics": [ "efftox_superiority" ] }, { "page": "efftox_utility", "title": "Get the utility of efficacy & toxicity probability pairs", "topics": [ "efftox_utility" ] }, { "page": "efftox_utility_density_plot", "title": "Plot densities of EffTox dose utilities", "topics": [ "efftox_utility_density_plot" ] }, { "page": "get_efftox_priors", "title": "Get normal prior hyperparameters for the EffTox model.", "topics": [ "get_efftox_priors" ] }, { "page": "n_at_dose", "title": "Get the number of patients treated at the doses under investigation.", "topics": [ "n_at_dose", "n_at_dose.dose_finding_fit" ] }, { "page": "parse_dose_finding_outcomes", "title": "Parse a string of dose-finding trial outcomes.", "topics": [ "parse_dose_finding_outcomes" ] }, { "page": "parse_eff_tox_dose_finding_outcomes", "title": "Parse a string of phase I/II dose-finding trial outcomes.", "topics": [ "parse_eff_tox_dose_finding_outcomes" ] }, { "page": "peps2_get_data", "title": "Get data to run the PePS2 trial example", "topics": [ "peps2_get_data" ] }, { "page": "peps2_process", "title": "Process RStan samples from a BEBOP model fit to PePS2 data", "topics": [ "peps2_process" ] }, { "page": "plot.crm_fit", "title": "Plot an crm_fit", "topics": [ "plot.crm_fit" ] }, { "page": "plot.efftox_fit", "title": "Plot an efftox_fit", "topics": [ "plot.efftox_fit" ] }, { "page": "predict.augbin_2t_1a_fit", "title": "Predict probability of success for given tumour size measurements.", "topics": [ "predict.augbin_2t_1a_fit" ] }, { "page": "print.augbin_fit", "title": "Print augbin_fit object.", "topics": [ "print.augbin_fit" ] }, { "page": "print.crm_fit", "title": "Print crm_fit object.", "topics": [ "print.crm_fit" ] }, { "page": "print.efftox_fit", "title": "Print efftox_fit object.", "topics": [ "print.efftox_fit" ] }, { "page": "print.nbg_fit", "title": "Print nbg_fit object.", "topics": [ "print.nbg_fit" ] }, { "page": "prior_predictive_augbin_2t_1a", "title": "Sample data from the Augmented Binary model prior predictive distribution.", "topics": [ "prior_predictive_augbin_2t_1a" ] }, { "page": "prob_success", "title": "Calculate the probability of success.", "topics": [ "prob_success", "prob_success.augbin_2t_1a_fit" ] }, { "page": "prob_tox_exceeds", "title": "Calculate the probability that the rate of toxicity exceeds some threshold", "topics": [ "prob_tox_exceeds", "prob_tox_exceeds.dose_finding_fit" ] }, { "page": "ranBin2", "title": "Sample pairs of correlated binary events", "topics": [ "ranBin2" ] }, { "page": "rlkjcorr", "title": "Sample LKJ correlation matrices.", "topics": [ "rlkjcorr" ] }, { "page": "spread_paths", "title": "Spread the information in dose_finding_paths object to a wide data.frame format.", "topics": [ "spread_paths" ] }, { "page": "stan_augbin", "title": "Fit Wason & Seaman's Augmented Binary model for tumour response.", "topics": [ "stan_augbin" ] }, { "page": "stan_augbin_demo", "title": "Simple helper function to demonstrate fitting of an Augmented Binary model.", "topics": [ "stan_augbin_demo" ] }, { "page": "stan_crm", "title": "Fit a CRM model", "topics": [ "stan_crm" ] }, { "page": "stan_efftox", "title": "Fit an EffTox model", "topics": [ "stan_efftox" ] }, { "page": "stan_efftox_demo", "title": "Fit the EffTox model presented in Thall et al. (2014)", "topics": [ "stan_efftox_demo" ] }, { "page": "stan_hierarchical_response_thall", "title": "Fit the hierarchical response model described by Thall _et al._ (2003).", "topics": [ "stan_hierarchical_response_thall" ] }, { "page": "stan_nbg", "title": "Fit a Neuenschwander, Branson & Gsponer logit dose-finding model", "topics": [ "stan_nbg" ] }, { "page": "stan_peps2", "title": "Fit the P2TNE model developed for the PePS2 trial to some outcomes.", "topics": [ "stan_peps2" ] }, { "page": "summary.crm_fit", "title": "Obtain summary of an crm_fit", "topics": [ "summary.crm_fit" ] }, { "page": "summary.efftox_fit", "title": "Obtain summary of an efftox_fit", "topics": [ "summary.efftox_fit" ] }, { "page": "total_weight_at_dose", "title": "Get the total weight of patient outcomes at the doses under investigation.", "topics": [ "total_weight_at_dose", "total_weight_at_dose.default" ] }, { "page": "tox_at_dose", "title": "Get the number of toxicity events seen at the doses under investigation.", "topics": [ "tox_at_dose", "tox_at_dose.dose_finding_fit" ] }, { "page": "trialr_simulate", "title": "Run a simulation study.", "topics": [ "trialr_simulate" ] }, { "page": "weights_at_dose", "title": "Get the weights of patient outcomes at the doses under investigation.", "topics": [ "weights_at_dose", "weights_at_dose.crm_fit", "weights_at_dose.default" ] } ], "_readme": "https://github.com/cran/trialr/raw/HEAD/README.md", "_rundeps": [ "abind", "arrayhelpers", "backports", "BH", "binom", "callr", "checkmate", "cli", "coda", "cpp11", "desc", "distributional", "dplyr", "farver", "generics", "ggdist", "ggplot2", "glue", "gridExtra", "gtable", "gtools", "inline", "isoband", "labeling", "lattice", "lifecycle", "loo", "magrittr", "MASS", "matrixStats", "numDeriv", "otel", "pillar", "pkgbuild", "pkgconfig", "posterior", "processx", "ps", "purrr", "quadprog", "QuickJSR", "R6", "RColorBrewer", "Rcpp", "RcppEigen", "RcppParallel", "rlang", "rstan", "rstantools", "S7", "scales", "StanHeaders", "stringi", "stringr", "svUnit", "tensorA", "tibble", "tidybayes", "tidyr", "tidyselect", "utf8", "vctrs", "viridisLite", "withr" ], "_sysdeps": [ { "shlib": "libtbb", "package": "libtbb12", "headers": "libtbb-dev", "source": "onetbb", "version": "2022.3.0-2", "name": "onetbb", "homepage": "https://uxlfoundation.github.io/oneTBB/", "description": "parallelism library for C++ - runtime files" }, { "shlib": "libstdc++", "package": "libstdc++6", "source": "gcc", "version": "16-20260322-1ubuntu1", "name": "c++", "homepage": "http://gcc.gnu.org/", "description": "GNU Standard C++ Library v3" } ], "_vignettes": [ { "source": "A600-BEBOP.Rmd", "filename": "A600-BEBOP.html", "title": "BEBOP in PePS2", "author": "Kristian Brock", "engine": "knitr::rmarkdown", "headings": [ "Introducing the BEBOP design", "PePS2 Scenario in trialr", "trialr", "References" ], "created": "2020-10-15 22:00:02", "modified": "2020-10-15 22:00:02", "commits": 1 }, { "source": 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