{ "_id": "6ae63c571e2c7b3a4f52f164", "Package": "forestsearch", "Title": "Exploratory Subgroup Identification in Survival and GLM Outcomes", "Version": "0.4.1", "Authors@R": "person(\"Larry\", \"Leon\", , \"larry.leon.05@post.harvard.edu\", role = c(\"aut\", \"cre\"))", "Description": "Implements statistical methods for exploratory subgroup\nidentification in clinical trials. Provides tools for\nidentifying patient subgroups with differential treatment\neffects using machine learning approaches including Generalized\nRandom Forests (GRF), LASSO regularization, and exhaustive\ncombinatorial search algorithms. Supports survival endpoints\n(Cox proportional hazards), binary outcomes (log odds ratio,\nlog relative risk, risk difference), continuous outcomes (mean\ndifference), and count / rate outcomes (log incidence rate\nratio via Poisson, quasi-Poisson, or negative-binomial GLMs\nwith optional person-time offset). Features bootstrap bias\ncorrection using infinitesimal jackknife methods to address\nselection bias in post-hoc analyses. Designed for clinical\nresearchers conducting exploratory subgroup analyses in\nrandomized controlled trials, particularly for multi-regional\nclinical trials (MRCT) requiring regional consistency\nevaluation. Methods are described in Leon et al. (2024)\n. Post-selection inference for the\nidentified subgroup follows León and Anderson (2026)\n. The search methodology is\ngiven in León and Anderson (2026)\n.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "VignetteBuilder": "knitr, quarto", "SystemRequirements": "Quarto command line tools\n(https://github.com/quarto-dev/quarto-cli)", "URL": "https://github.com/larry-leon/forestsearch,\nhttps://larry-leon.github.io/forestsearch/", "BugReports": "https://github.com/larry-leon/forestsearch/issues", "Config/testthat/edition": "3", "Config/roxygen2/version": "8.1.1", "Config/pak/sysreqs": "cmake make libuv1-dev libxml2-dev libssl-dev\nlibnode-dev", "Repository": "https://larry-leon.r-universe.dev", "Date/Publication": "2026-10-31 16:46:10 UTC", "RemoteUrl": "https://github.com/larry-leon/forestsearch", "RemoteRef": "HEAD", "RemoteSha": "19b703b089e5de92198a3b9c8c7db2d84a42db89", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-31 18:54:05 UTC", "User": "root" }, "Author": "Larry Leon [aut, cre]", "Maintainer": "Larry Leon ", 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"title": "Estimate heterogeneous treatment effects via DINA", "topics": [ "dina_fit" ] }, { "page": "dina_fit_bagged", "title": "Bagged DINA with infinitesimal-jackknife variance", "topics": [ "dina_fit_bagged" ] }, { "page": "dina_frontier", "title": "Extract DINA per-covariate Pareto frontiers as forestsearch cuts", "topics": [ "dina_frontier" ] }, { "page": "dina_subgroup", "title": "Identify a harm subgroup from a DINA fit via univariate threshold search", "topics": [ "dina_subgroup" ] }, { "page": "dina_subgroup_bootstrap", "title": "Selection-adjusted bootstrap CI for dina_subgroup()", "topics": [ "dina_subgroup_bootstrap" ] }, { "page": "dina_subgroup_refit", "title": "Standard within-subgroup treatment-effect model for a DINA subgroup", "topics": [ "dina_subgroup_refit" ] }, { "page": "dummy_encode", "title": "Dummy-code a data frame (numeric pass-through, factors expanded)", "topics": [ "dummy_encode" ] }, { "page": "early_stop_decision", "title": "Early Stopping Decision", "topics": [ "early_stop_decision" ] }, { "page": "estimate_propensity_scores", "title": "Estimate Propensity Scores", "topics": [ "estimate_propensity_scores" ] }, { "page": "evaluate_comparison", "title": "Evaluate a Comparison Expression Without eval(parse())", "topics": [ "evaluate_comparison" ] }, { "page": "evaluate_consistency_twostage", "title": "Evaluate Consistency (Two-Stage Algorithm)", "topics": [ "evaluate_consistency_twostage" ] }, { "page": "evaluate_cuts_once", "title": "Cache and validate cut expressions efficiently", "topics": [ "evaluate_cuts_once" ] }, { "page": "evaluate_subgroup_consistency", "title": "Evaluate Subgroup Consistency (Single-Stage)", "topics": [ "evaluate_subgroup_consistency" ] }, { "page": "expand_covariates", "title": "Expand a seed covariate frame to n_super rows via a Gaussian copula.", "topics": [ "expand_covariates" ] }, { "page": "explain_pareto_selection", "title": "Describe the Pareto-Frontier Selection in Words", "topics": [ "explain_pareto_selection" ] }, { "page": "extract_grf_args", "title": "Recover the GRF arguments used inside a forestsearch run", "topics": [ "extract_grf_args" ] }, { "page": "extract_selected_subgroups", "title": "Tidy summary of selected subgroups across a comparison run", "topics": [ "extract_selected_subgroups" ] }, { "page": "figure_note", "title": "Generate Figure Note for Quarto/RMarkdown", "topics": [ "figure_note" ] }, { "page": "filter_by_lassokeep", "title": "Filter a vector by LASSO-selected variables", "topics": [ "filter_by_lassokeep" ] }, { "page": "filter_call_args", "title": "Filter and merge arguments for function calls", "topics": [ "filter_call_args" ] }, { "page": "find_k_inter_for_target_hr", "title": "Find k_inter Value to Achieve Target Harm Subgroup Hazard Ratio", "topics": [ "find_k_inter_for_target_hr" ] }, { "page": "find_leaf_split", "title": "Find the split that leads to a specific leaf node (deprecated)", "topics": [ "find_leaf_split" ] }, { "page": "find_required_sample_size", "title": "Find Minimum Sample Size for Target Detection Power", "topics": [ "find_required_sample_size" ] }, { "page": "fit_causal_forest", "title": "Fit causal survival forest", "topics": [ "fit_causal_forest" ] }, { "page": "fit_cox_models", "title": "Fit Cox Models for Subgroups", "topics": [ "fit_cox_models" ] }, { "page": "fit_policy_trees", "title": "Fit policy trees up to specified depth", "topics": [ "fit_policy_trees" ] }, { "page": "forest_height", "title": "Recommended figure height for a forest panel", "topics": [ "forest_height" ] }, { "page": "forestsearch", "title": "ForestSearch: Exploratory Subgroup Identification", "topics": [ "forestsearch" ] }, { "page": "forestsearch_bootstrap_dofuture", "title": "ForestSearch Bootstrap with doFuture Parallelization", "topics": [ "forestsearch_bootstrap_dofuture" ] }, { "page": "forestsearch_Kfold", "title": "ForestSearch K-Fold Cross-Validation", "topics": [ "forestsearch_Kfold" ] }, { "page": "forestsearch_KfoldOut", "title": "ForestSearch K-Fold Cross-Validation Output Summary", "topics": [ "forestsearch_KfoldOut" ] }, { "page": "forestsearch_tenfold", "title": "ForestSearch Repeated K-Fold Cross-Validation", "topics": [ "forestsearch_tenfold" ] }, { "page": "format_bootstrap_table", "title": "Format Bootstrap Results Table with gt", "topics": [ "format_bootstrap_table" ] }, { "page": "format_CI", "title": "Format Confidence Interval for Estimates", "topics": [ "format_CI" ] }, { "page": "format_fs_details", "title": "Format ForestSearch Details Output for Beamer Two-Column Display", "topics": [ "format_fs_details" ] }, { "page": "format_oc_results", "title": "Format Operating Characteristics Results as GT Table", "topics": [ "format_oc_results" ] }, { "page": "fpr_approx", "title": "Approximate Procedure-Level False Positive Rate", "topics": [ "fpr_approx" ] }, { "page": "fpr_calibration", "title": "Direct Simulation-Based FPR Calibration at a Specific Threshold Pair", "topics": [ "fpr_calibration" ] }, { "page": "fpr_calibration_transfer", "title": "Transfer Corrected FPR Prediction to a Nearby Threshold Pair", "topics": [ "fpr_calibration_transfer" ] }, { "page": "frontier_member_flags", "title": "Per-Subject Membership Matrix for Pareto Frontier Members", "topics": [ "frontier_member_flags" ] }, { "page": "fs_assert_membership", "title": "Assert the exported selected-subgroup membership matches forestsearch's own", "topics": [ "fs_assert_membership" ] }, { "page": "fs_declaration_calibration", "title": "Calibrated declaration threshold and family-wise size of the p-star screen", "topics": [ "fs_declaration_calibration", "print.fs_declaration_calibration" ] }, { "page": "fs_dgm_feasibility", "title": "Design-time feasibility of a DGM's planted region", "topics": [ "fs_dgm_feasibility", "print.fs_dgm_feasibility" ] }, { "page": "fs_dgm_scale", "title": "Sampling Scale of the Difference-in-Means Estimator", "topics": [ "fs_dgm_scale" ] }, { "page": "fs_export_maxeff_family", "title": "Materialize the maxeff candidate family as 0/1 membership columns", "topics": [ "fs_export_maxeff_family" ] }, { "page": "fs_family_report", "title": "Report which stages of the candidate family are data-dependent", "topics": [ "fs_family_report", "print.fs_family_report" ] }, { "page": "fs_fdr_report", "title": "Operating False-Discovery Rate of a Fitted ForestSearch Analysis", "topics": [ "fs_fdr_report" ] }, { "page": "fs_focus_tag", "title": "Stem tag for a (subgroup_method, sg_focus) pair", "topics": [ "fs_focus_tag" ] }, { "page": "fs_identification_summary", "title": "Identification structure across a sweep", "topics": [ "fs_identification_summary" ] }, { "page": "fs_identification_table", "title": "Identification-structure table", "topics": [ "fs_identification_table" ] }, { "page": "FS_labels", "title": "Convert Factor Code to Label", "topics": [ "FS_labels" ] }, { "page": "fs_mr_oc_summary", "title": "Operating-Characteristics Summary of a Multiplier-Resampling Payload", "topics": [ "fs_mr_oc_summary" ] }, { "page": "fs_oc_family_enumerate", "title": "Enumerate the candidate family on a DGM's population frame", "topics": [ "fs_oc_family_enumerate" ] }, { "page": "fs_oc_grid", "title": "Sweep the declaration thresholds on one draw set per trial size", "topics": [ "fs_oc_grid" ] }, { "page": "fs_oc_invert", "title": "Invert the family declaration rate for a target", "topics": [ "fs_oc_invert" ] }, { "page": "fs_oc_predict", "title": "Predicted operating characteristics of the search over a candidate family", "topics": [ "fs_oc_predict" ] }, { "page": "fs_plot_bias_coverage", "title": "Bias-vs-coverage display (three panels)", "topics": [ "fs_plot_bias_coverage" ] }, { "page": "fs_rule_covariate_pairs", "title": "Covariate signatures of the realized rules", "topics": [ "fs_rule_covariate_pairs" ] }, { "page": "fs_scale_se", "title": "Estimator Standard Deviation at a Given Trial Size", "topics": [ "fs_scale_se" ] }, { "page": "fs_sim_bias_coverage", "title": "Bias and coverage summary for one simulation cell", "topics": [ "fs_sim_bias_coverage" ] }, { "page": "fs_sym_root", "title": "Symmetric Square Root of a Covariance Matrix", "topics": [ "fs_sym_root" ] }, { "page": "fs_to_guohe", "title": "Run Guo and He Algorithm 3 on a forestsearch maxeff fit", "topics": [ "fs_to_guohe" ] }, { "page": "generate_aft_dgm_flex", "title": "Generate Synthetic Survival Data using AFT Model with Flexible Subgroups", "topics": [ "generate_aft_dgm_flex" ] }, { "page": "generate_bootstrap_synthetic", "title": "Generate Synthetic Data using Bootstrap with Perturbation", "topics": [ "generate_bootstrap_synthetic" ] }, { "page": "generate_bootstrap_with_noise", "title": "Generate Bootstrap Sample with Added Noise", "topics": [ "generate_bootstrap_with_noise" ] }, { "page": "generate_detection_curve", "title": "Generate Detection Probability Curve", "topics": [ "generate_detection_curve" ] }, { "page": "generate_gbsg_bootstrap_general", "title": "Generate Synthetic GBSG Data using Generalized Bootstrap", "topics": [ "generate_gbsg_bootstrap_general" ] }, { "page": "generate_glm_dgm", "title": "Generate GLM-Based Data Generating Mechanism", "topics": [ "generate_glm_dgm" ] }, { "page": "get_combinations_info", "title": "Get all combinations of subgroup factors up to maxk", "topics": [ "get_combinations_info" ] }, { "page": "get_conf_force", "title": "Get forced cut expressions for variables", "topics": [ "get_conf_force" ] }, { "page": "get_conf_force_jq", "title": "Get J-quantile cut expressions for variables", "topics": [ "get_conf_force_jq" ] }, { "page": "get_covs_in", "title": "Get indicator vector for selected subgroup factors", "topics": [ "get_covs_in" ] }, { "page": "get_Cox_sg", "title": "Fit Cox Model for Subgroup", "topics": [ "get_Cox_sg" ] }, { "page": "get_dfpred", "title": "Generate Prediction Dataset with Subgroup Treatment Recommendation", "topics": [ "get_dfpred" ] }, { "page": "get_dfRes", "title": "Bootstrap Confidence Interval and Bias Correction Results", "topics": [ "get_dfRes" ] }, { "page": "get_FSdata", "title": "ForestSearch Data Preparation and Feature Selection", "topics": [ "get_FSdata" ] }, { "page": "get_leff", "title": "Get L_eff at a Given Sample Size", "topics": [ "get_leff" ] }, { "page": "get_split_hr_fast", "title": "Fast Cox Model HR Estimation", "topics": [ "get_split_hr_fast" ] }, { "page": "get_subgroup_membership", "title": "Get subgroup membership vector", "topics": [ "get_subgroup_membership" ] }, { "page": "gg_forest", "title": "ggplot2 / patchwork forest plot", "topics": [ "gg_forest" ] }, { "page": "glm_cs_fit", "title": "Fit GLM with Spline Treatment-Biomarker Interaction", "topics": [ "glm_cs_fit", "print.glm_cs_fit" ] }, { "page": "glm_effect_profile", "title": "GLM Treatment-Effect Profile for Continuous Biomarker Interaction", "topics": [ "glm_effect_profile" ] }, { "page": "grf.subg.eval", "title": "Evaluates the performance of GRF-identified subgroups, including hazard ratios, bias, and predictive values. This function is typically used in simulation studies to assess the performance of the GRF subgroup identification method.", "topics": [ "grf.subg.eval" ] }, { "page": "grf.subg.harm.glm", "title": "GRF Subgroup Identification for GLM Outcomes", "topics": [ "grf.subg.harm.glm" ] }, { "page": "grf.subg.harm.survival", "title": "GRF Subgroup Identification for Survival Data", "topics": [ "grf.subg.harm.survival" ] }, { "page": "guohe_adaptive_r", "title": "Cross-validated choice of the shrinkage tuning parameter r", "topics": [ "guohe_adaptive_r" ] }, { "page": "guohe_algorithm3", "title": "De-biased inference for the best subgroup over a large enumerated family", "topics": [ "guohe_algorithm3" ] }, { "page": "hrCI_format", "title": "Format Hazard Ratio and Confidence Interval", "topics": [ "hrCI_format" ] }, { "page": "inject_mrct_structure", "title": "Build an aft_dgm_flex with synthetic regions and paper-style effect structure.", "topics": [ "inject_mrct_structure" ] }, { "page": "interpret_estimation_table", "title": "Generate Narrative Interpretation of Estimation Properties", "topics": [ "interpret_estimation_table" ] }, { "page": "interpret_search_config", "title": "Interpret and Diagnose Search Configuration", "topics": [ "interpret_search_config" ] }, { "page": "lasso_selection", "title": "LASSO selection for Cox model", "topics": [ "lasso_selection" ] }, { "page": "latest_rds", "title": "Find the Most Recent .rds File Matching a Pattern", "topics": [ "latest_rds" ] }, { "page": "load_leff_calibration", "title": "Load L_eff Calibration Results", "topics": [ "load_leff_calibration" ] }, { "page": "make_effect_estimator", "title": "Make an Effect-Estimator Closure", "topics": [ "make_effect_estimator" ] }, { "page": "make_region_model", "title": "Build the logistic region model and draw Region on a population.", "topics": [ "make_region_model" ] }, { "page": "mr_estimates_table", "title": "Multiplier-resampling (MR) estimates table (bootstrap-table format)", "topics": [ "mr_estimates_table" ] }, { "page": "mrct_region_sims", "title": "MRCT Regional Subgroup Simulation", "topics": [ "mrct_region_sims" ] }, { "page": "mrct_truth_by_region", "title": "Truth by region on the super-population: prevalence, x_pred shift, causal AHR / CDE, and a large-sample Cox HR on UNCENSORED potential outcomes. Compare HR_cox_PO with the censored-trial HR from sim_region_metrics(): a gap between them isolates the prognostic-shift / follow-up-truncation mechanism (the Feb-2026 deck) from covariate shift (AHR itself differing by region).", "topics": [ "mrct_truth_by_region" ] }, { "page": "n_pcnt", "title": "Calculate n and percent", "topics": [ "n_pcnt" ] }, { "page": "pareto_frontier_table", "title": "Format Pareto Frontier of Candidate Subgroups", "topics": [ "pareto_frontier_table" ] }, { "page": "plot_detection_curve", "title": "Plot Detection Probability Curve", "topics": [ "plot_detection_curve" ] }, { "page": "plot_effect_distribution", "title": "Three-Panel Effect-Distribution Violin from Simulation Results", "topics": [ "plot_effect_distribution" ] }, { "page": "plot_fs_identification_involvement", "title": "Covariate involvement across the sweep", "topics": [ "plot_fs_identification_involvement" ] }, { "page": "plot_fs_identification_structure", "title": "Composition of the identified subgroup by structure", "topics": [ "plot_fs_identification_structure" ] }, { "page": "plot_fs_rule_pairs", "title": "Selected-covariate-pair frequencies", "topics": [ "plot_fs_rule_pairs" ] }, { "page": "plot_km_band_forestsearch", "title": "Plot Kaplan-Meier Survival Difference Bands for ForestSearch Subgroups", "topics": [ "plot_km_band_forestsearch" ] }, { "page": "plot_pareto_combined", "title": "Combined Pareto-Frontier Plot Across Configurations Sharing a Passing Set", "topics": [ "plot_pareto_combined" ] }, { "page": "plot_pareto_frontier", "title": "Plot the Pareto Frontier of Candidate Subgroups", "topics": [ "plot_pareto_frontier" ] }, { "page": "plot_region_modifier", "title": "True log-HR psi0(x) along x_pred with region histograms (paper Fig. 3 analogue)", "topics": [ "plot_region_modifier" ] }, { "page": "plot_sg_distribution", "title": "Plot Distribution of Identified Subgroups", "topics": [ "plot_sg_distribution" ] }, { "page": "plot_sg_glm_outcomes", "title": "Plot GLM Subgroup Outcomes", "topics": [ "plot.fs_binary_plot", "plot_sg_glm_outcomes", "print.fs_binary_plot" ] }, { "page": "plot_sg_results", "title": "Plot ForestSearch Subgroup Results", "topics": [ "plot_sg_results" ] }, { "page": "plot_sg_weighted_km", "title": "Plot Weighted Kaplan-Meier Curves for ForestSearch Subgroups", "topics": [ "plot_sg_weighted_km" ] }, { "page": "plot_spline_treatment_effect", "title": "Plot Spline Treatment Effect Function", "topics": [ "plot_spline_treatment_effect" ] }, { "page": "plot_subgroup_effects", "title": "Plot Subgroup Analysis Results", "topics": [ "plot_subgroup_effects" ] }, { "page": "plot_subgroup_results_forestplot", "title": "Plot Subgroup Results Forest Plot", "topics": [ "plot_subgroup_results_forestplot" ] }, { "page": "plot.forestsearch", "title": "Plot ForestSearch Results", "topics": [ "plot.forestsearch" ] }, { "page": "plot.fs_forestplot", "title": "Plot Method for ForestSearch Forest Plot", "topics": [ "plot.fs_forestplot" ] }, { "page": "plot.fs_sg_plot", "title": "Plot Method for fs_sg_plot Objects", "topics": [ "plot.fs_sg_plot" ] }, { "page": "plot.subgroup_sims_summary", "title": "Forest plot of an extreme-subgroups summary", "topics": [ "plot.subgroup_sims_summary" ] }, { "page": "predict_fpr_corrected", "title": "Predict Corrected Null FPR", "topics": [ "predict_fpr_corrected" ] }, { "page": "predict.dina", "title": "Predicted DINA values for new covariate data.", "topics": [ "predict.dina" ] }, { "page": "prepare_subgroup_data", "title": "Prepare subgroup data for analysis", "topics": [ "prepare_subgroup_data" ] }, { "page": "print_grf_details", "title": "Print detailed output for debugging", "topics": [ "print_grf_details" ] }, { "page": "print.cox_ahr_cde", "title": "Print method for cox_ahr_cde objects", "topics": [ "print.cox_ahr_cde" ] }, { "page": "print.dina_bagged", "title": "Print a bagged DINA fit.", "topics": [ "print.dina_bagged" ] }, { "page": "print.dina_subgroup", "title": "Print a 'dina_subgroup' result.", "topics": [ "print.dina_subgroup" ] }, { "page": "print.dina_subgroup_bootstrap", "title": "Print a 'dina_subgroup_bootstrap' result.", "topics": [ "print.dina_subgroup_bootstrap" ] }, { "page": "print.dina_subgroup_refit", "title": "Print a 'dina_subgroup_refit' result.", "topics": [ "print.dina_subgroup_refit" ] }, { "page": "print.forestsearch", "title": "Print Method for forestsearch Objects", "topics": [ "print.forestsearch" ] }, { "page": "print.forestsearch_comparison", "title": "Print Method for forestsearch_comparison", "topics": [ "print.forestsearch_comparison" ] }, { "page": "print.fpr_calibration", "title": "Print method for 'fpr_calibration' objects", "topics": [ "print.fpr_calibration" ] }, { "page": "print.fs_bootstrap", "title": "Print Method for ForestSearch Bootstrap Results", "topics": [ "print.fs_bootstrap" ] }, { "page": "print.fs_cv_summary", "title": "Print Method for fs_cv_summary Objects", "topics": [ "print.fs_cv_summary" ] }, { "page": "print.fs_dgm_scale", "title": "Print method for 'fs_dgm_scale' objects", "topics": [ "print.fs_dgm_scale" ] }, { "page": "print.fs_fdr_report", "title": "Print method for 'fs_fdr_report' objects", "topics": [ "print.fs_fdr_report" ] }, { "page": "print.fs_forest_theme", "title": "Print Method for ForestSearch Forest Theme", "topics": [ "print.fs_forest_theme" ] }, { "page": "print.fs_forestplot", "title": "Print Method for ForestSearch Forest Plot", "topics": [ "print.fs_forestplot" ] }, { "page": "print.fs_kfold", "title": "Print Method for K-Fold CV Results", "topics": [ "print.fs_kfold" ] }, { "page": "print.fs_mr_oc", "title": "Print method for 'fs_mr_oc' objects", "topics": [ "print.fs_mr_oc" ] }, { "page": "print.fs_sg_plot", "title": "Print Method for fs_sg_plot Objects", "topics": [ "print.fs_sg_plot" ] }, { "page": "print.fs_tenfold", "title": "Print Method for Repeated K-Fold CV Results", "topics": [ "print.fs_tenfold" ] }, { "page": "print.fs_weighted_km", "title": "Print Method for fs_weighted_km Objects", "topics": [ "print.fs_weighted_km" ] }, { "page": "print.gbsg_dgm", "title": "Print Method for gbsg_dgm Objects", "topics": [ "print.gbsg_dgm" ] }, { "page": "print.glm_dgm", "title": "Print method for 'glm_dgm' objects", "topics": [ "print.glm_dgm" ] }, { "page": "print.glm_effect_profile", "title": "Print method for glm_effect_profile objects", "topics": [ "print.glm_effect_profile" ] }, { "page": "print.guohe_a3", "title": "Print method for guohe_a3 objects", "topics": [ "print.guohe_a3" ] }, { "page": "print.guohe_ar", "title": "Print method for guohe_ar objects", "topics": [ "print.guohe_ar" ] }, { "page": "print.hte_test", "title": "Print method for 'hte_test' objects", "topics": [ "print.hte_test" ] }, { "page": "print.leff_calibration", "title": "Print method for 'leff_calibration' objects", "topics": [ "print.leff_calibration" ] }, { "page": "print.multi_survreg_comparison", "title": "Print method for survreg_comparison objects", "topics": [ "print.multi_survreg_comparison" ] }, { "page": "process_conf_force_expr", "title": "Process forced cut expression for a variable", "topics": [ "process_conf_force_expr" ] }, { "page": "quick_km_band_plot", "title": "Quick Plot KM Bands from ForestSearch", "topics": [ "quick_km_band_plot" ] }, { "page": "render_forestplot", "title": "Render ForestSearch Forest Plot", "topics": [ "render_forestplot" ] }, { "page": "render_reference_table", "title": "Render Reference Simulation Table as gt", "topics": [ "render_reference_table" ] }, { "page": "reset_workers", "title": "Reset a Parallel Worker Pool", "topics": [ "reset_workers" ] }, { "page": "rmst_calculation", "title": "RMST calculation for subgroup", "topics": [ "rmst_calculation" ] }, { "page": "run_null_calibration", "title": "Run Null Calibration Simulation", "topics": [ "run_null_calibration" ] }, { "page": "run_simulation_analysis", "title": "Run One Simulation Replicate", "topics": [ "run_simulation_analysis" ] }, { "page": "run_single_consistency_split", "title": "Run Single Consistency Split", "topics": [ "run_single_consistency_split" ] }, { "page": "run_subgroup_sims", "title": "Run the extreme-subgroups simulation study", "topics": [ "run_subgroup_sims" ] }, { "page": "save_forestplot", "title": "Save ForestSearch Forest Plot to File", "topics": [ "save_forestplot" ] }, { "page": "save_leff_calibration", "title": "Save L_eff Calibration Results", "topics": [ "save_leff_calibration" ] }, { "page": "select_best_subgroup", "title": "Select best subgroup based on criterion", "topics": [ "select_best_subgroup" ] }, { "page": "sensitivity_analysis_k_inter", "title": "Sensitivity Analysis of Hazard Ratios to k_inter", "topics": [ "sensitivity_analysis_k_inter" ] }, { "page": "setup_gbsg_dgm", "title": "Set Up a GBSG-Based AFT Data Generating Mechanism", "topics": [ "setup_gbsg_dgm" ] }, { "page": "setup_parallel_SGcons", "title": "Set up parallel processing for subgroup consistency", "topics": [ "setup_parallel_SGcons" ] }, { "page": "sg_consistency_out", "title": "Output Subgroup Consistency Results", "topics": [ "sg_consistency_out" ] }, { "page": "SG_tab_estimates", "title": "Subgroup summary table estimates", "topics": [ "SG_tab_estimates" ] }, { "page": "sg_tables", "title": "Enhanced Subgroup Summary Tables (gt output)", "topics": [ "sg_tables" ] }, { "page": "sgdist_fig_height", "title": "Adaptive Figure Height for a Subgroup-Distribution Plot", "topics": [ "sgdist_fig_height" ] }, { "page": "SGplot_estimates", "title": "Violin/Boxplot Visualization of HR Estimates", "topics": [ "SGplot_estimates" ] }, { "page": "sim_region_metrics", "title": "Region-consistency metrics on a simulated trial (ITT and optionally a subgroup)", "topics": [ "sim_region_metrics" ] }, { "page": "simulate_from_dgm", "title": "Simulate Survival Data from AFT Data Generating Mechanism", "topics": [ "simulate_from_dgm" ] }, { "page": "simulate_from_gbsg_dgm", "title": "Simulate Trial Data from GBSG DGM", "topics": [ "simulate_from_gbsg_dgm" ] }, { "page": "simulate_from_glm_dgm", "title": "Simulate Trial Data from a GLM Data Generating Mechanism", "topics": [ "simulate_from_glm_dgm" ] }, { "page": "sort_subgroups", "title": "Sort Subgroups by Focus (post-consistency)", "topics": [ "sort_subgroups" ] }, { "page": "subgroup_cox", "title": "Cox per-subgroup fit function", "topics": [ "subgroup_cox" ] }, { "page": "subgroup_glm", "title": "GLM per-subgroup fit function", "topics": [ "subgroup_glm" ] }, { "page": "subgroup.consistency", "title": "Evaluate Subgroup Consistency", "topics": [ "subgroup.consistency" ] }, { "page": "subgroup.search", "title": "Subgroup Search for Treatment Effect Heterogeneity (Improved, Parallelized)", "topics": [ "subgroup.search" ] }, { "page": "suggest_thresholds", "title": "Suggest Screening and Consistency Thresholds", "topics": [ "suggest_thresholds" ] }, { "page": 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