{ "_id": "6a1efa71b401979e73418719", "Type": "Package", "Package": "psborrow2", "Title": "Bayesian Dynamic Borrowing Analysis and Simulation", "Version": "0.0.5.1", "Authors@R": "c(\nperson(\ngiven = \"Matt\",\nfamily = \"Secrest\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-0939-4902\"),\nemail = \"secrestm@gene.com\"\n),\nperson(\ngiven = \"Isaac\",\nfamily = \"Gravestock\",\nrole = c(\"aut\"),\nemail = \"isaac.gravestock@roche.com\"\n),\nperson(\ngiven = \"Craig\",\nfamily = \"Gower-Page\",\nrole = c(\"ctb\"),\nemail = \"craig.gower-page@roche.com\"\n),\nperson(\ngiven = \"Manoj\",\nfamily = \"Khanal\",\nrole = c(\"ctb\"),\nemail = \"khanal_manoj@lilly.com\"\n),\nperson(\ngiven = \"Mingyang\",\nfamily = \"Shan\",\nrole = c(\"ctb\"),\nemail = \"mingyang.shan@lilly.com\"\n),\nperson(\ngiven = \"Kexin\",\nfamily = \"Jin\",\nrole = c(\"ctb\"),\nemail = \"kexin.jin@bms.com\"\n),\nperson(\ngiven = \"Zhi\",\nfamily = \"Yang\",\nrole = c(\"ctb\"),\nemail = \"zhi.yang@bms.com\"\n),\nperson(\"Genentech, Inc.\", role = c(\"cph\", \"fnd\"))\n)", "Description": "Bayesian dynamic borrowing is an approach to incorporating\nexternal data to supplement a randomized, controlled trial\nanalysis in which external data are incorporated in a dynamic\nway (e.g., based on similarity of outcomes); see Viele 2013\n for an overview. This package implements\nthe hierarchical commensurate prior approach to dynamic\nborrowing as described in Hobbes 2011\n. There are three main\nfunctionalities. First, 'psborrow2' provides a user-friendly\ninterface for applying dynamic borrowing on the study results\nhandles the Markov Chain Monte Carlo sampling on behalf of the\nuser. Second, 'psborrow2' provides a simulation framework to\ncompare different borrowing parameters (e.g. full borrowing, no\nborrowing, dynamic borrowing) and other trial and borrowing\ncharacteristics (e.g. sample size, covariates) in a unified\nway. Third, 'psborrow2' provides a set of functions to generate\ndata for simulation studies, and also allows the user to\nspecify their own data generation process. This package is\ndesigned to use the sampling functions from 'cmdstanr' which\ncan be installed from .", "URL": "https://github.com/Genentech/psborrow2,\nhttps://genentech.github.io/psborrow2/index.html", "BugReports": "https://github.com/Genentech/psborrow2/issues", "License": "Apache License 2.0", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.3", "Config/testthat/edition": "3", "Additional_repositories": "https://stan-dev.r-universe.dev", "Language": "en-US", "SystemRequirements": "cmdstan", "Collate": "'generics.R' 'prior_class.R' 'covariate_class.R'\n'add_covariates.R' 'prior_normal.R' 'treatment_class.R'\n'borrowing_class.R' 'outcome_class.R' 'analysis_class.R'\n'borrowing_details.R' 'borrowing_fixed_power_prior.R'\n'borrowing_full.R' 'borrowing_hierarchical_commensurate.R'\n'borrowing_none.R' 'cast_mat_to_long_pem.R'\n'check_data_matrix_has_columns.R' 'cmdstan.R'\n'create_analysis_obj.R' 'create_data_matrix.R'\n'treatment_details.R' 'sim_treatment_list.R' 'helpers.R'\n'outcome_bin_logistic.R' 'prior_exponential.R'\n'outcome_surv_weibull_ph.R' 'outcome_surv_exponential.R'\n'sim_outcome_list.R' 'sim_borrowing_list.R'\n'sim_covariate_list.R' 'sim_data_list.R' 'simulation_class.R'\n'create_simulation_obj.R' 'data.R'\n'get_prior_string_covariates.R' 'prior_half_cauchy.R'\n'outcome_cont_normal.R' 'outcome_surv_pem.R'\n'load_and_interpolate_stan_model.R' 'load_stan_file.R'\n'make_analysis_object_list.R' 'mcmc_sample.R'\n'mcmc_simulation_result.R' 'package.R'\n'prepare_stan_data_inputs.R' 'prior_bernoulli.R' 'prior_beta.R'\n'prior_cauchy.R' 'prior_gamma.R' 'prior_half_normal.R'\n'prior_poisson.R' 'sim_covariates.R' 'sim_estimate_bias.R'\n'sim_estimate_effect_variance.R' 'sim_estimate_mse.R'\n'sim_is_null_effect_covered.R' 'sim_is_true_effect_covered.R'\n'sim_samplesize.R' 'simulate_data_baseline.R' 'simulate_data.R'\n'simvar_class.R' 'trim_data_matrix.R' 'uniform_prior.R' 'zzz.R'", "LazyData": "true", "VignetteBuilder": "knitr", "Repository": "https://genentech.r-universe.dev", "Date/Publication": "2026-05-18 14:54:41 UTC", "RemoteUrl": "https://github.com/Genentech/psborrow2", "RemoteRef": "HEAD", "RemoteSha": "ffc40c76c31110b363fd8f3055fccc004c7d5c42", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-18 15:43:20 UTC", "User": "root" }, "Author": "Matt Secrest [aut, cre] (ORCID:\n),\nIsaac Gravestock [aut],\nCraig Gower-Page [ctb],\nManoj Khanal [ctb],\nMingyang Shan [ctb],\nKexin Jin [ctb],\nZhi Yang [ctb],\nGenentech, Inc. [cph, fnd]", "Maintainer": "Matt Secrest ", "MD5sum": "26cb03c4c58b099103f0ad6d199d53bb", "_user": "genentech", "_type": "src", "_file": "psborrow2_0.0.5.1.tar.gz", "_fileid": "95438eef2dcaff15e606d37ef6f6d99563cdc3bd99736d5f071f484d2f7e485c", "_filesize": 3633519, "_sha256": "95438eef2dcaff15e606d37ef6f6d99563cdc3bd99736d5f071f484d2f7e485c", "_created": "2026-05-18T15:43:20.000Z", "_published": "2026-06-02T15:44:49.065Z", "_distro": "noble", "_jobs": [ { "job": 79111445844, "time": 239, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7062551502" }, { "job": 79111442400, "time": 207, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7062539119" }, { "job": 79111415485, "time": 247, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "7062543705" }, { "job": 79111450829, "time": 229, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7062548858" }, { "job": 79111444847, "time": 575, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7062457595" }, { "job": 79111413438, "time": 169, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7362680882" }, { "job": 79111448085, "time": 149, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "7062517969" }, { "job": 79111436224, "time": 132, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "7062511571" }, { "job": 79111435134, "time": 130, "config": "windows-release", "r": "4.6.0", "check": "OK", "artifact": "7062510622" } ], "_buildurl": "https://github.com/r-universe/genentech/actions/runs/26043513517", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/Genentech/psborrow2", "_commit": { "id": "ffc40c76c31110b363fd8f3055fccc004c7d5c42", "author": "Matt Secrest ", "committer": "GitHub ", "message": "0.0.5.0.cran (#322)\n\n* prep for release\n\n* style\n\n* clean and rerun\n\n* .claude to gitignore\n\n* reran vignettes", "time": 1779116081 }, "_maintainer": { "name": "Matt Secrest", "email": "secrestm@gene.com", "login": "mattsecrest", "description": "Data innovation at @Genentech . ", "uuid": 35199424, "orcid": "0000-0002-0939-4902" }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.1.0", "role": "Depends" }, { "package": "checkmate", "role": "Imports" }, { "package": "glue", "role": "Imports" }, { "package": "methods", "role": "Imports" }, { "package": "graphics", "role": "Imports" }, { "package": "posterior", "role": "Imports" }, { "package": "generics", "role": "Imports" }, { "package": "Matrix", "role": "Imports" }, { "package": "mvtnorm", "role": "Imports" }, { "package": "future", "role": "Imports" }, { "package": "simsurv", "role": "Imports" }, { "package": "cmdstanr", "role": "Suggests" }, { "package": "survival", "role": "Suggests" }, { "package": "flexsurv", "role": "Suggests" }, { "package": "eha", "role": "Suggests" }, { "package": "testthat", "version": ">= 3.0", "role": "Suggests" }, { "package": "usethis", "version": ">= 2.1.5", "role": "Suggests" }, { "package": "vdiffr", "role": "Suggests" }, { "package": "tibble", "role": "Suggests" }, { "package": "xml2", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "bayesplot", "role": "Suggests" }, { "package": "matrixcalc", "role": "Suggests" }, { "package": "WeightIt", "role": "Suggests" }, { "package": "MatchIt", "role": "Suggests" }, { "package": "BayesPPD", "role": "Suggests" }, { "package": "ggsurvfit", "role": "Suggests" }, { "package": "gbm", "role": "Suggests" }, { "package": "ggplot2", "role": "Suggests" }, { "package": "cobalt", "role": "Suggests" }, { "package": "table1", "role": "Suggests" }, { "package": "gt", "role": "Suggests" }, { "package": "gtsummary", "role": "Suggests" } ], "_owner": "genentech", "_selfowned": true, "_usedby": 0, "_updates": [ { "week": "2026-19", "n": 1 }, { "week": "2026-21", "n": 1 } ], "_tags": [ { "name": "v0.0.5.1", "date": "2026-05-18" } ], "_topics": [ "bayesian-dynamic-borrowing", "psborrow2", "simulation-study" ], "_stars": 20, "_contributors": [ { "user": "mattsecrest", "count": 94, "uuid": 35199424 }, { "user": "gravesti", "count": 57, "uuid": 83659704 }, { "user": "arkadiuszbeer", "count": 2, "uuid": 86738093 }, { "user": "danielinteractive", "count": 1, "uuid": 1071227 }, { "user": "cicdguy", "count": 1, "uuid": 26552821 } ], "_userbio": { "uuid": 3913764, "type": "organization", "name": "Genentech", "description": "Dedicated to pursuing groundbreaking science to discover and develop medicines" }, "_downloads": { "count": 288, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/psborrow2" }, "_devurl": "https://github.com/genentech/psborrow2", "_searchresults": 16, "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/NEWS.html", "extra/NEWS.txt", "extra/psborrow2.html", "extra/readme.html", "extra/readme.md", "manual.pdf" ], "_homeurl": "https://github.com/genentech/psborrow2", "_realowner": "genentech", "_cranurl": true, "_releases": [ { "version": "0.0.3.2", "date": "2024-04-03" }, { "version": "0.0.3.3", "date": "2024-04-12" }, { "version": "0.0.3.4", "date": "2024-04-30" }, { "version": "0.0.4.0", "date": "2025-02-12" }, { "version": "0.0.5.0", "date": "2026-05-11" }, { "version": "0.0.5.1", "date": "2026-05-14" } ], "_exports": [ "add_covariates", "baseline_covariates", "bernoulli_prior", "beta_prior", "bin_var", "binary_cutoff", "borrowing_details", "borrowing_fixed_power_prior", "borrowing_full", "borrowing_hierarchical_commensurate", "borrowing_none", "cauchy_prior", "check_cmdstan", "check_cmdstanr", "check_data_matrix_has_columns", "cont_var", "covariance_matrix", "create_analysis_obj", "create_baseline_object", "create_data_matrix", "create_data_simulation", "create_event_dist", "create_simulation_obj", "custom_enrollment", "cut_off_after_events", "cut_off_after_first", "cut_off_after_last", "cut_off_none", "enrollment_constant", "exp_surv_dist", "exponential_prior", "gamma_prior", "generate", "get_cmd_stan_models", "get_data", "get_quantiles", "get_results", "get_stan_code", "get_vars", "half_cauchy_prior", "half_normal_prior", "logistic_bin_outcome", "mcmc_sample", "normal_prior", "null_event_dist", "outcome_bin_logistic", "outcome_cont_normal", "outcome_surv_exponential", "outcome_surv_pem", "outcome_surv_weibull_ph", "plot", "plot_pdf", "plot_pmf", "poisson_prior", "prior_bernoulli", "prior_beta", "prior_cauchy", "prior_exponential", "prior_gamma", "prior_half_cauchy", "prior_half_normal", "prior_normal", "prior_poisson", "rename_draws_covariates", "set_cut_off", "set_dropout", "set_enrollment", "set_transformations", "show_guide", "sim_borrowing_list", "sim_covariate_list", "sim_covariates", "sim_data_list", "sim_outcome_list", "sim_samplesize", "sim_treatment_list", "treatment_details", "uniform_prior", "variable_dictionary", "weib_ph_surv_dist" ], "_datasets": [ { "name": "example_matrix", "title": "Example data matrix", "object": "example_matrix", "class": [ "matrix", "array" ], "fields": [ "id", "ext", "trt", "cov4", "cov3", "cov2", "cov1", "time", "status", "cnsr", "resp" ], "rows": 500, "table": true, "tojson": true }, { "name": "example_surv", "title": "Simulated Survival Data", "object": "example_surv", "class": [ "data.frame" ], "fields": [ "trt", "ext", "eventtime", "status", "censor", "cov1", "cov2", "cov3" ], "rows": 600, "table": true, "tojson": true } ], "_help": [ { "page": "add_covariates", "title": "Add Covariates for Model Adjustment", "topics": [ "add_covariates" ] }, { "page": "Analysis-class", "title": "'Analysis' Class", "topics": [ ".analysis_obj", "Analysis-class" ] }, { "page": "as_data_frame", "title": "Coerce a 'psborrow2' object to a data frame", "topics": [ "as.data.frame.BaselineDataList", "as_data_frame" ] }, { "page": "baseline_covariates", "title": "Specify Correlated Baseline Covariates", "topics": [ "baseline_covariates" ] }, { "page": "BaselineDataFrame-class", "title": "Baseline Data Frame Object", "topics": [ ".baseline_dataframe", "BaselineDataFrame-class" ] }, { "page": "BaselineDataList-class", "title": "Baseline Data Frame List", "topics": [ ".baseline_data_list", "BaselineDataList-class" ] }, { "page": "BaselineObject-class", "title": "'BaselineObject' class for data simulation", "topics": [ ".baseline_object", "BaselineObject-class" ] }, { "page": "bernoulli_prior", "title": "Legacy function for the bernoulli prior", "topics": [ "bernoulli_prior" ] }, { "page": "beta_prior", "title": "Legacy function for the beta prior", "topics": [ "beta_prior" ] }, { "page": "bin_var", "title": "Create binary covariate", "concept": [ "simvar" ], "topics": [ "bin_var" ] }, { "page": "binary_cutoff", "title": "Binary Cut-Off Transformation", "topics": [ "binary_cutoff" ] }, { "page": "BinaryOutcome-class", "title": "'BinaryOutcome' class", "concept": [ "outcome" ], "topics": [ "BinaryOutcome-class" ] }, { "page": "borrowing_details", "title": "Legacy function for specifying borrowing details", "topics": [ "borrowing_details" ] }, { "page": "borrowing_fixed_power_prior", "title": "Fixed Power Prior Borrowing", "topics": [ "borrowing_fixed_power_prior" ] }, { "page": "borrowing_full", "title": "Full borrowing", "concept": [ "borrowing" ], "topics": [ "borrowing_full" ] }, { "page": "borrowing_hierarchical_commensurate", "title": "Hierarchical commensurate borrowing", "topics": [ "borrowing_hierarchical_commensurate" ] }, { "page": "borrowing_none", "title": "No borrowing", "concept": [ "borrowing" ], "topics": [ "borrowing_none" ] }, { "page": "Borrowing-class", "title": "'Borrowing' Class", "concept": [ "borrowing classes" ], "topics": [ "Borrowing-class" ] }, { "page": "BorrowingFixedPowerPrior-class", "title": "'BorrowingFixedPowerPrior' class", "concept": [ "borrowing classes" ], "topics": [ ".borrowing_fixed_power_prior", "BorrowingFixedPowerPrior-class" ] }, { "page": "BorrowingFull-class", "title": "'BorrowingFull' class", "concept": [ "borrowing classes" ], "topics": [ ".borrowing_full", "BorrowingFull-class" ] }, { "page": "BorrowingHierarchicalCommensurate-class", "title": "'BorrowingHierarchicalCommensurate' class", "concept": [ "borrowing classes" ], "topics": [ ".borrowing_hierarchical_commensurate", "BorrowingHierarchicalCommensurate-class" ] }, { "page": "BorrowingNone-class", "title": "'BorrowingNone' class", "concept": [ "borrowing classes" ], "topics": [ ".borrowing_none", "BorrowingNone-class" ] }, { "page": "build_model_string", "title": "Build the model string by interpolating the Stan template", "topics": [ "build_model_string" ] }, { "page": "c", "title": "Combine objects in 'psborrow2'", "topics": [ "c", "c,SimDataList-method" ] }, { "page": "cauchy_prior", "title": "Legacy function for the cauchy prior", "topics": [ "cauchy_prior" ] }, { "page": "check_cmdstanr", "title": "Check Stan", "topics": [ "check_cmdstan", "check_cmdstanr" ] }, { "page": "check_data_matrix_has_columns", "title": "Check Data Matrix for Required Columns", "topics": [ "check_data_matrix_has_columns" ] }, { "page": "check_fixed_external_data", "title": "Create a Fixed External Data Object", "topics": [ "check_fixed_external_data" ] }, { "page": "cont_var", "title": "Create continuous covariate", "concept": [ "simvar" ], "topics": [ "cont_var" ] }, { "page": "ContinuousOutcome-class", "title": "'ContinuousOutcome' class", "concept": [ "outcome" ], "topics": [ "ContinuousOutcome-class" ] }, { "page": "covariance_matrix", "title": "Create Covariance Matrix", "topics": [ "covariance_matrix" ] }, { "page": "Covariates-class", "title": "'Covariate' Class", "topics": [ ".covariate_class", "Covariates-class" ] }, { "page": "create_alpha_string", "title": "Create alpha string", "topics": [ "create_alpha_string", "create_alpha_string,Borrowing-method", "create_alpha_string,BorrowingHierarchicalCommensurate-method" ] }, { "page": "create_analysis_obj", "title": "Compile MCMC sampler using STAN and create analysis object", "topics": [ "create_analysis_obj" ] }, { "page": "create_baseline_object", "title": "Create Baseline Data Simulation Object", "topics": [ "create_baseline_object" ] }, { "page": "create_data_matrix", "title": "Create Data Matrix", "topics": [ "create_data_matrix" ] }, { "page": "create_data_simulation", "title": "Data Simulation", "topics": [ "create_data_simulation" ] }, { "page": "create_event_dist", "title": "Specify a Time to Event Distribution", "topics": [ "create_event_dist", "null_event_dist" ] }, { "page": "create_simulation_obj", "title": "Compile MCMC sampler using STAN and create simulation object", "topics": [ "create_simulation_obj" ] }, { "page": "create_tau_string", "title": "Create tau string", "topics": [ "create_tau_string", "create_tau_string,Borrowing-method", "create_tau_string,BorrowingHierarchicalCommensurate-method" ] }, { "page": "custom_enrollment", "title": "Create a 'DataSimEnrollment' Object", "topics": [ "custom_enrollment" ] }, { "page": "cut_off_funs", "title": "Cut Off Functions", "topics": [ "cut_off_after_events", "cut_off_after_first", "cut_off_after_last", "cut_off_funs", "cut_off_none" ] }, { "page": "DataSimCutOff-class", "title": "Cut Off Object", "topics": [ ".datasim_cut_off", "DataSimCutOff-class" ] }, { "page": "DataSimEnrollment-class", "title": "Enrollment Object", "topics": [ ".datasim_enrollment", "DataSimEnrollment-class" ] }, { "page": "DataSimEvent-class", "title": "Event Time Distribution Object", "topics": [ ".datasim_event", "DataSimEvent-class" ] }, { "page": "DataSimFixedExternalData-class", "title": "Fixed External Control Data Object", "topics": [ ".datasim_fixed_external_data", "DataSimFixedExternalData-class" ] }, { "page": "DataSimObject-class", "title": "Data Simulation Object Class", "topics": [ ".datasim_object", "DataSimObject-class" ] }, { "page": "enrollment_constant", "title": "Constant Enrollment Rates", "topics": [ "enrollment_constant" ] }, { "page": "eval_constraints", "title": "Evaluate constraints", "topics": [ "eval_constraints", "eval_constraints,Prior-method" ] }, { "page": "example_matrix", "title": "Example data matrix", "topics": [ "example_matrix" ] }, { "page": "example_surv", "title": "Simulated Survival Data", "topics": [ "example_surv" ] }, { "page": "exp_surv_dist", "title": "Legacy function for the exponential survival distribution", "topics": [ "exp_surv_dist" ] }, { "page": "exponential_prior", "title": "Legacy function for the exponential prior", "topics": [ "exponential_prior" ] }, { "page": "gamma_prior", "title": "Legacy function for the gamma prior", "topics": [ "gamma_prior" ] }, { "page": "generate", "title": "Generate Data from Object", "topics": [ "generate" ] }, { "page": "generate-BaselineObject-method", "title": "Generate Data for a 'BaselineObject'", "topics": [ "generate,BaselineObject-method" ] }, { "page": "generate-DataSimObject-method", "title": "Generate Data for a 'DataSimObject'", "topics": [ "generate,DataSimObject-method" ] }, { "page": "get_cmd_stan_models", "title": "Get 'CmdStanModel' objects for 'MCMCSimulationResults'", "topics": [ "get_cmd_stan_models", "get_cmd_stan_models,MCMCSimulationResult-method" ] }, { "page": "get_data", "title": "Get Simulated Data from 'SimDataList' object", "topics": [ "get_data", "get_data,SimDataList-method" ] }, { "page": "get_prior_string_covariates", "title": "Get prior string for all covariates", "topics": [ "get_prior_string_covariates" ] }, { "page": "get_quantiles", "title": "Get Quantiles of Random Data", "topics": [ "get_quantiles" ] }, { "page": "get_results", "title": "Get results for 'MCMCSimulationResults' objects", "topics": [ "get_results", "get_results,MCMCSimulationResult-method" ] }, { "page": "get_stan_code", "title": "Get method for Stan model", "topics": [ "get_stan_code", "get_stan_code,Analysis-method" ] }, { "page": "get_vars", "title": "Get Variables", "topics": [ "get_vars", "get_vars,Analysis-method", "get_vars,BaselineObject-method", "get_vars,BinaryOutcome-method", "get_vars,Borrowing-method", "get_vars,BorrowingFixedPowerPrior-method", "get_vars,ContinuousOutcome-method", "get_vars,Covariates-method", "get_vars,NULL-method", "get_vars,SimBorrowingList-method", "get_vars,SimCovariateList-method", "get_vars,SimOutcomeList-method", "get_vars,SimTreatmentList-method", "get_vars,Simulation-method", "get_vars,TimeToEvent-method", "get_vars,Treatment-method" ] }, { "page": "half_cauchy_prior", "title": "Legacy function for the half-cauchy prior", "topics": [ "half_cauchy_prior" ] }, { "page": "half_normal_prior", "title": "Legacy function for the normal half prior", "topics": [ "half_normal_prior" ] }, { "page": "load_and_interpolate_stan_model", "title": "Load and interpolate Stan model", "topics": [ "load_and_interpolate_stan_model" ] }, { "page": "load_stan_file", "title": "Load a Stan 'psborrow2' template", "topics": [ "load_stan_file" ] }, { "page": "logistic_bin_outcome", "title": "Legacy function for binary logistic regression", "topics": [ "logistic_bin_outcome" ] }, { "page": "mcmc_sample", "title": "Sample from Stan model", "topics": [ "mcmc_sample", "mcmc_sample,Analysis-method", "mcmc_sample,ANY-method", "mcmc_sample,Simulation-method" ] }, { "page": "MCMCSimulationResult-class", "title": "'MCMCSimulationResult' Class", "topics": [ ".mcmc_simulation_result", "MCMCSimulationResult-class" ] }, { "page": "normal_prior", "title": "Legacy function for the normal prior", "topics": [ "normal_prior" ] }, { "page": "outcome_bin_logistic", "title": "Bernoulli distribution with logit parametrization", "concept": [ "outcome models" ], "topics": [ "outcome_bin_logistic" ] }, { "page": "outcome_cont_normal", "title": "Normal Outcome Distribution", "concept": [ "outcome models" ], "topics": [ "outcome_cont_normal" ] }, { "page": "outcome_surv_exponential", "title": "Exponential survival distribution", "concept": [ "outcome models" ], "topics": [ "outcome_surv_exponential" ] }, { "page": "outcome_surv_pem", "title": "Piecewise exponential survival distribution", "concept": [ "outcome models" ], "topics": [ "outcome_surv_pem" ] }, { "page": "outcome_surv_weibull_ph", "title": "Weibull survival distribution (proportional hazards formulation)", "concept": [ "outcome models" ], "topics": [ "outcome_surv_weibull_ph" ] }, { "page": "Outcome-class", "title": "'Outcome' class", "concept": [ "outcome" ], "topics": [ "Outcome-class" ] }, { "page": "OutcomeBinaryLogistic-class", "title": "'OutcomeBinaryLogistic' class", "concept": [ "outcome" ], "topics": [ ".outcome_bin_logistic", "OutcomeBinaryLogistic-class" ] }, { "page": "OutcomeContinuousNormal-class", "title": "'OutcomeContinuousNormal' class", "concept": [ "outcome" ], "topics": [ ".outcome_cont_normal", "OutcomeContinuousNormal-class" ] }, { "page": "OutcomeSurvExponential-class", "title": "'OutcomeSurvExponential' Class", "concept": [ "outcome" ], "topics": [ ".outcome_surv_exponential", "OutcomeSurvExponential-class" ] }, { "page": "OutcomeSurvPEM-class", "title": "'OutcomeSurvPEM' Class", "concept": [ "outcome" ], "topics": [ ".outcome_surv_pem", "OutcomeSurvPEM-class" ] }, { "page": "OutcomeSurvWeibullPH-class", "title": "'OutcomeSurvWeibullPH' Class", "concept": [ "outcome" ], "topics": [ ".outcome_surv_weibull_ph", "OutcomeSurvWeibullPH-class" ] }, { "page": "plot", "title": "Plot Prior Objects", "topics": [ "plot", "plot,Prior,missing-method", "plot,PriorBernoulli,missing-method", "plot,PriorBeta,missing-method", "plot,PriorCauchy,missing-method", "plot,PriorExponential,missing-method", "plot,PriorGamma,missing-method", "plot,PriorHalfCauchy,missing-method", "plot,PriorHalfNormal,missing-method", "plot,PriorNormal,missing-method", "plot,PriorPoisson,missing-method", "plot,UniformPrior,missing-method" ] }, { "page": "plot_pdf", "title": "Plot Probability Density Function Values", "topics": [ "plot_pdf" ] }, { "page": "plot_pmf", "title": "Plot Probability Mass Function Values", "topics": [ "plot_pmf" ] }, { "page": "poisson_prior", "title": "Legacy function for the poisson prior", "topics": [ "poisson_prior" ] }, { "page": "possible_data_sim_vars", "title": "Get All Variable Names in Simulated Data Model Matrix", "topics": [ "possible_data_sim_vars" ] }, { "page": "prior_bernoulli", "title": "Prior bernoulli distribution", "concept": [ "priors" ], "topics": [ "prior_bernoulli" ] }, { "page": "prior_beta", "title": "Prior beta distribution", "concept": [ "priors" ], "topics": [ "prior_beta" ] }, { "page": "prior_cauchy", "title": "Prior cauchy distribution", "concept": [ "priors" ], "topics": [ "prior_cauchy" ] }, { "page": "prior_exponential", "title": "Prior exponential distribution", "concept": [ "priors" ], "topics": [ "prior_exponential" ] }, { "page": "prior_gamma", "title": "Prior gamma distribution", "concept": [ "priors" ], "topics": [ "prior_gamma" ] }, { "page": "prior_half_cauchy", "title": "Prior half-cauchy distribution", "concept": [ "priors" ], "topics": [ "prior_half_cauchy" ] }, { "page": "prior_half_normal", "title": "Prior half-normal distribution", "concept": [ "priors" ], "topics": [ "prior_half_normal" ] }, { "page": "prior_normal", "title": "Prior normal distribution", "concept": [ "priors" ], "topics": [ "prior_normal" ] }, { "page": "prior_poisson", "title": "Prior poisson distribution", "concept": [ "priors" ], "topics": [ "prior_poisson" ] }, { "page": "Prior-class", "title": "'Prior' Class", "concept": [ "prior classes" ], "topics": [ "Prior-class" ] }, { "page": "PriorBernoulli-class", "title": "'PriorBernoulli' Class", "concept": [ "prior classes" ], "topics": [ ".prior_bernoulli", "PriorBernoulli-class" ] }, { "page": "PriorBeta-class", "title": "'PriorBeta' Class", "concept": [ "prior classes" ], "topics": [ ".prior_beta", "PriorBeta-class" ] }, { "page": "PriorCauchy-class", "title": "'PriorCauchy' Class", "concept": [ "prior classes" ], "topics": [ ".prior_cauchy", "PriorCauchy-class" ] }, { "page": "PriorExponential-class", "title": "'PriorExponential' Class", "concept": [ "prior classes" ], "topics": [ ".prior_exponential", "PriorExponential-class" ] }, { "page": "PriorGamma-class", "title": "'PriorGamma' Class", "concept": [ "prior classes" ], "topics": [ ".prior_gamma", "PriorGamma-class" ] }, { "page": "PriorHalfCauchy-class", "title": "'PriorHalfCauchy' Class", "concept": [ "prior classes" ], "topics": [ ".prior_half_cauchy", "PriorHalfCauchy-class" ] }, { "page": "PriorHalfNormal-class", "title": "'PriorHalfNormal' Class", "concept": [ "prior classes" ], "topics": [ ".prior_half_normal", "PriorHalfNormal-class" ] }, { "page": "PriorNormal-class", "title": "'PriorNormal' Class", "concept": [ "prior classes" ], "topics": [ ".prior_normal", "PriorNormal-class" ] }, { "page": "PriorPoisson-class", "title": "'PriorPoisson' Class", "concept": [ "prior classes" ], "topics": [ ".prior_poisson", "PriorPoisson-class" ] }, { "page": "rename_draws_covariates", "title": "Rename Covariates in 'draws' Object", "topics": [ "rename_draws_covariates" ] }, { "page": "set_cut_off", "title": "Set Clinical Cut Off Rule", "topics": [ "set_cut_off" ] }, { "page": "set_dropout", "title": "Set Drop Out Distribution", "topics": [ "set_dropout" ] }, { "page": "set_enrollment", "title": "Set Enrollment Rates for Internal and External Trials", "topics": [ "set_enrollment" ] }, { "page": "set_transformations", "title": "Set transformations in 'BaselineObject' objects", "topics": [ "set_transformations" ] }, { "page": "set_transformations-BaselineObject-method", "title": "Set Transformations in Baseline Objects", "topics": [ "set_transformations,BaselineObject-method" ] }, { "page": "show_guide", "title": "Show guide for objects with guides", "topics": [ "show_guide", "show_guide,Simulation-method" ] }, { "page": "sim_borrowing_list", "title": "Input borrowing details for a simulation study", "concept": [ "simulation classes" ], "topics": [ "sim_borrowing_list" ] }, { "page": "sim_covariate_list", "title": "Input covariate adjustment details for a simulation study", "concept": [ "simulation classes" ], "topics": [ "sim_covariate_list" ] }, { "page": "sim_covariates", "title": "Specify covariates for simulation study", "concept": [ "simulation" ], "topics": [ "sim_covariates" ] }, { "page": "sim_covariates_summ", "title": "Summarize the number of continuous and binary covariates in a 'SimCovariates' object created by 'sim_covariates()'", "topics": [ "sim_covariates_summ" ] }, { "page": "sim_data_list", "title": "Input generated data for a simulation study", "concept": [ "simulation classes" ], "topics": [ "sim_data_list" ] }, { "page": "sim_outcome_list", "title": "Input outcome details for a simulation study", "concept": [ "simulation classes" ], "topics": [ "sim_outcome_list" ] }, { "page": "sim_samplesize", "title": "Set simulation study parameters for sample size", "concept": [ "simulation" ], "topics": [ "sim_samplesize" ] }, { "page": "sim_treatment_list", "title": "Input treatment details for a simulation study", "concept": [ "simulation classes" ], "topics": [ "sim_treatment_list" ] }, { "page": "SimBorrowingList-class", "title": "'SimBorrowingList' Class", "topics": [ ".sim_borrowing_list", "SimBorrowingList-class" ] }, { "page": "SimCovariateList-class", "title": "'SimCovariateList' Class", "topics": [ ".sim_covariate_list", "SimCovariateList-class" ] }, { "page": "SimCovariates-class", "title": "'SimCovariates' Class", "topics": [ ".sim_covariates", "SimCovariates-class" ] }, { "page": "SimDataList-class", "title": "'SimDataList' Class", "topics": [ ".sim_data_list", "SimDataList-class" ] }, { "page": "SimOutcomeList-class", "title": "'SimOutcomeList' Class", "topics": [ ".sim_outcome_list", "SimOutcomeList-class" ] }, { "page": "SimSampleSize-class", "title": "'SimSampleSize' Class", "topics": [ ".sim_samplesize", "SimSampleSize-class" ] }, { "page": "SimTreatmentList-class", "title": "'SimTreatmentList' Class", "topics": [ ".sim_treatment_list", "SimTreatmentList-class" ] }, { "page": "Simulation-class", "title": "'Simulation' Class", "topics": [ ".simulation_obj", "Simulation-class" ] }, { "page": "SimVar-class", "title": "'SimVar' Class", "topics": [ "SimVar-class" ] }, { "page": "SimVarBin-class", "title": "'SimVarBin' class", "concept": [ "simvar classes" ], "topics": [ ".bin_var", "SimVarBin-class" ] }, { "page": "SimVarCont-class", "title": "'SimVarCont' class", "concept": [ "simvar classes" ], "topics": [ ".cont_var", "SimVarCont-class" ] }, { "page": "TimeToEvent-class", "title": "'TimeToEvent' class", "concept": [ "outcome" ], "topics": [ "TimeToEvent-class" ] }, { "page": "treatment_details", "title": "Specify Treatment Details", "topics": [ "treatment_details" ] }, { "page": "Treatment-class", "title": "'Treatment' Class", "topics": [ ".treatment_class", "Treatment-class" ] }, { "page": "trim_cols", "title": "Trim columns from Data Matrix Based on Borrowing object type", "topics": [ "trim_cols", "trim_cols,Borrowing-method", "trim_cols,BorrowingFixedPowerPrior-method", "trim_cols,BorrowingHierarchicalCommensurate-method" ] }, { "page": "trim_rows", "title": "Trim Rows from Data Matrix Based on Borrowing object type", "topics": [ "trim_rows", "trim_rows,Borrowing-method", "trim_rows,BorrowingNone-method" ] }, { "page": "uniform_prior", "title": "Prior uniform distribution", "concept": [ "priors" ], "topics": [ "uniform_prior" ] }, { "page": "UniformPrior-class", "title": "'UniformPrior' Class", "concept": [ "prior classes" ], "topics": [ ".uniform_prior", "UniformPrior-class" ] }, { "page": "variable_dictionary", "title": "Create Variable Dictionary", "topics": [ "variable_dictionary" ] }, { "page": "weib_ph_surv_dist", "title": "Legacy function for the Weibull proportional Hazards survival distribution", "topics": [ "weib_ph_surv_dist" ] } ], "_readme": "https://github.com/Genentech/psborrow2/raw/HEAD/README.md", "_rundeps": [ "abind", "backports", "checkmate", "cli", "codetools", "digest", "distributional", "future", "generics", "globals", "glue", "lattice", "lifecycle", "listenv", "magrittr", "Matrix", "matrixStats", "mvtnorm", "numDeriv", "parallelly", "pillar", "pkgconfig", "posterior", "rlang", "simsurv", "tensorA", "tibble", "utf8", "vctrs" ], "_vignettes": [ { "source": "psborrow2.Rmd", "filename": "psborrow2.html", "title": "Getting started with psborrow2 ", "author": "Matt Secrest and Isaac Gravestock", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "1. Apply Bayesian dynamic borrowing methods", "2. Conduct simulation studies of Bayesian dynamic borrowing methods", "3. Generate data for simulation studies", "Additional articles", "Installing cmdstanr", "References" ], "created": "2022-09-06 02:52:14", "modified": "2024-06-21 15:48:37", "commits": 8 }, { "source": "match_weight_01_methods.Rmd", "filename": "match_weight_01_methods.html", "title": "Matching and Weighting Methods ", "author": "Manoj Khanal khanal_manoj@lilly.com, Eli Lilly & Company", "engine": "knitr::rmarkdown", "headings": [ "Simulating trial data", "Simulating external control data", "Merging trial and external control data", "Propensity scores overlap", "Matching Methods", "PSML", "PSMR", "GM", "GMW", "OM", "Weighting Methods", "GBM", "EB", "IPW", "Balance Plots for Matching Methods", "Balance Plots for Weighting Methods", "Selection of matching/weighting methods", "References" ], "created": "2024-04-11 20:12:09", "modified": "2026-05-18 14:54:41", "commits": 7 }, { "source": "dataset.Rmd", "filename": "dataset.html", "title": "Conduct a hybrid control analysis on a dataset using BDB ", "author": "Matt Secrest and Isaac Gravestock", "engine": "knitr::rmarkdown", "headings": [ "Before you start", "Creating an analysis object", "1. data_matrix", "Required elements", "Time-to-event", "Binary endpoints", "Covariates", "Example data", "Creating a data matrix with create_data_matrix()", "psborrow2 example matrix", "2. outcome", "3. borrowing", "3. treatment", "Application", "Sampling from an analysis object", "Summarizing results", "Using bayesplot", "Using posterior" ], "created": "2022-09-06 02:52:14", "modified": "2026-05-18 14:54:41", "commits": 16 }, { "source": "match_weight_02_application.Rmd", "filename": "match_weight_02_application.html", "title": "Matching and Weighting Application with Dynamic Borrowing ", "author": "Manoj Khanal khanal_manoj@lilly.com, Eli Lilly & Company", "engine": "knitr::rmarkdown", "headings": [ "Hazard function", "Cox proportional hazards model", "Baseline hazard function", "Likelihood", "Power prior model", "Commensurate prior model", "Conducting Analysis", "Example data", "Exponential distribution with constant hazard with gamma prior distribution (Using psborrow2 R package)", "Time-to-event analysis with Weibull distribution and proportional hazards parametrization", "Power prior with piecewise exponential distribution (not implemented in psborrow2 R package)", "Commensurate prior with piecewise exponential distribution (not implemented in psborrow2 R package)", "Exponential distribution (constant hazard) and no borrowing: Using psborrow2 R package", "Exponential distribution (constant hazard) and full borrowing: Using psborrow2 R package", "Cox proportional hazards model using frequentist approach (No borrowing)", "Cox proportional hazards model using frequentist approach (Full borrowing from external control)", "Summarizing all the results together", "References" ], "created": "2024-04-11 20:12:09", "modified": "2026-05-18 14:54:41", "commits": 7 }, { "source": "prior_distributions.Rmd", "filename": "prior_distributions.html", "title": "Specifying prior distributions ", "author": "Matt Secrest and Isaac Gravestock", "engine": "knitr::rmarkdown", "headings": [ "Specifying prior distributions", "Types of prior distributions", "Visualizing prior distributions" ], "created": "2022-09-06 02:52:14", "modified": "2025-05-07 14:47:41", "commits": 11 }, { "source": "simulation_study.Rmd", "filename": "simulation_study.html", "title": "Conduct a simulation study ", "author": "Matt Secrest, Isaac Gravestock, Kexin Jin and Zhi Yang", "engine": "knitr::rmarkdown", "headings": [ "Bringing your own simulated data", "data_matrix_list", "data_list", "guide", "effect, drift, and index", "outcome", "borrowing", "covariate", "treatment", "create_simulation_obj()", "mcmc_sample()", "MSE", "Type I error", "Power", "EHSS", "Optimal Accuracy Design", "References" ], "created": "2022-09-06 02:52:14", "modified": "2026-05-18 14:54:41", "commits": 25 }, { "source": "propensity_scores.Rmd", "filename": "propensity_scores.html", "title": "Incorporating propensity scores analysis in psborrow2 ", "author": "Isaac Gravestock and Matt Secrest", "engine": "knitr::rmarkdown", "headings": [ "Alternative PS Weights with WeightIt", "Matching with MatchIt", "Combined Weighting and Dynamic Borrowing", "Fixed Weights", "Reference Models", "Comparison of Results", "References" ], "created": "2022-09-22 14:44:46", "modified": "2026-05-18 14:54:41", "commits": 14 }, { "source": "weighting.Rmd", "filename": "weighting.html", "title": "Comparison of Fixed Weights ", "author": "Matt Secrest and Isaac Gravestock", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Logistic regression", "glm", "BayesPPD", "psborrow2", "Results", "Exponential models" ], "created": "2022-10-25 17:58:09", "modified": "2026-05-18 14:54:41", "commits": 12 }, { "source": "data_simulation.Rmd", "filename": "data_simulation.html", "title": "Data Simulation ", "engine": "knitr::rmarkdown", "headings": [ "Generating Baseline Data", "Generating Survival Data", "Enrollment", "Drop Out", "Clinical Cut Off", "Running a simulation", "Using fixed external data", "Combining simulations" ], "created": "2023-10-03 05:58:02", "modified": "2026-05-18 14:54:41", "commits": 7 }, { "source": "simple_overview.Rmd", "filename": "simple_overview.html", "title": "Simple Overview ", "engine": "knitr::rmarkdown", "headings": [ "Purpose", "Explore example data", "{psborrow2} contains an example matrix", "Load as data.frame for some functions", "Look at distribution of arms", "Naive internal comparisons", "Kaplan-Meier curves", "Cox model", "Hybrid control analysis", "A note on prior distributions", "Outcome objects", "Create an exponential survival distribution Outcome object", "Borrowing object", "Treatment objects", "Analysis objects", "MCMC sampling", "Interpret results", "Control arm imbalances", "Propensity score analysis with BDB" ], "created": "2024-09-09 04:08:06", "modified": "2026-05-18 14:54:41", "commits": 4 } ], "_score": 7.107209969647869, "_indexed": true, "_nocasepkg": "psborrow2", "_universes": [ "genentech", "mattsecrest" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "0.0.5.1", "date": "2026-05-18T15:46:54.000Z", "distro": "noble", "commit": "ffc40c76c31110b363fd8f3055fccc004c7d5c42", "fileid": "58ec4a9cc8edf93e27c68d6dc8c8ed9d88ea559406b98d96f887c274b268a86c", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/genentech/actions/runs/26043513517" }, { "r": "4.6.0", "os": "linux", "version": "0.0.5.1", "date": "2026-05-18T15:46:23.000Z", "distro": "noble", "commit": "ffc40c76c31110b363fd8f3055fccc004c7d5c42", "fileid": "5c0d1f48e1e364fb08ae869e799945a318a91101b5c37610ecf5676e36ba1a07", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/genentech/actions/runs/26043513517" }, { "r": "4.5.3", "os": "mac", "version": "0.0.5.1", "date": "2026-05-18T15:46:37.000Z", "commit": "ffc40c76c31110b363fd8f3055fccc004c7d5c42", "fileid": "1f01f9f0cdd31de7e77e04dda3e6fc3094cc3fe7fd9929b1b7713df5ead51311", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/genentech/actions/runs/26043513517" }, { "r": "4.6.0", "os": "mac", "version": "0.0.5.1", "date": "2026-05-18T15:46:47.000Z", "commit": "ffc40c76c31110b363fd8f3055fccc004c7d5c42", "fileid": "6032701946736e9ea87c282859d3c36468d7fd6bdcd7625589ccccb3780a241b", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/genentech/actions/runs/26043513517" }, { "r": "4.7.0", "os": "win", "version": "0.0.5.1", "date": "2026-05-18T15:45:03.000Z", "commit": "ffc40c76c31110b363fd8f3055fccc004c7d5c42", "fileid": "5b1eed826d0c35040a4e48a46a23306d45cd7fb0a64f991e0e969289d1a58c7d", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/genentech/actions/runs/26043513517" }, { "r": "4.5.3", "os": "win", "version": "0.0.5.1", "date": "2026-05-18T15:44:52.000Z", "commit": "ffc40c76c31110b363fd8f3055fccc004c7d5c42", "fileid": "75007b8e4f9cbf1cb182b279253d18229cdab2a436b28e5441098ecbff4b9713", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/genentech/actions/runs/26043513517" }, { "r": "4.6.0", "os": "win", "version": "0.0.5.1", "date": "2026-05-18T15:44:52.000Z", "commit": "ffc40c76c31110b363fd8f3055fccc004c7d5c42", "fileid": "3ff299b509caae9d5d1c3db03e8180cb89a4d246b08d0cb5850eb4a3e6ff632d", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/genentech/actions/runs/26043513517" }, { "r": "4.6.0", "os": "wasm", "version": "0.0.5.1", "date": "2026-06-02T15:44:24.000Z", "commit": "ffc40c76c31110b363fd8f3055fccc004c7d5c42", "fileid": "3434e8de9884d91337a6e30b250fb879a916b39ba97b6a889af73a4aabe0e3ee", "status": "success", "buildurl": "https://github.com/r-universe/genentech/actions/runs/26043513517" } ] }