{ "_id": "6a21652fcd65a98ecbd34b91", "Package": "Surrogate", "Type": "Package", "Title": "Evaluation of Surrogate Endpoints in Clinical Trials", "Version": "3.4.2.9000", "Authors@R": "c(\nperson(\"Wim\", \"Van Der Elst\", email = \"wim.vanderelst@gmail.com\", role = c(\"cre\", \"aut\")),\nperson(\"Florian\", \"Stijven\", email = \"florian.stijven@kuleuven.be\", role = \"aut\"),\nperson(\"Fenny\", \"Ong\", role = \"aut\"),\nperson(\"Dries\", \"De Witte\", role = \"aut\"),\nperson(\"Gokce\", \"Deliorman\", role = \"aut\"),\nperson(\"Paul\", \"Meyvisch\", role = \"aut\"),\nperson(\"Alvaro\", \"Poveda\", role = \"aut\"),\nperson(\"Ariel\", \"Alonso\", role = \"aut\"),\nperson(\"Hannah\", \"Ensor\", role = \"aut\"),\nperson(\"Christoper\", \"Weir\", role = \"aut\"),\nperson(\"Geert\", \"Molenberghs\", role = \"aut\")\n)", "Description": "In a clinical trial, it frequently occurs that the most\ncredible outcome to evaluate the effectiveness of a new therapy\n(the true endpoint) is difficult to measure. In such a\nsituation, it can be an effective strategy to replace the true\nendpoint by a (bio)marker that is easier to measure and that\nallows for a prediction of the treatment effect on the true\nendpoint (a surrogate endpoint). The package 'Surrogate' allows\nfor an evaluation of the appropriateness of a candidate\nsurrogate endpoint based on the meta-analytic,\ninformation-theoretic, and causal-inference frameworks. Part of\nthis software has been developed using funding provided from\nthe European Union's Seventh Framework Programme for research,\ntechnological development and demonstration (Grant Agreement no\n602552), the Special Research Fund (BOF) of Hasselt University\n(BOF-number: BOF2OCPO3), GlaxoSmithKline Biologicals, Baekeland\nMandaat (HBC.2022.0145), and Johnson & Johnson Innovative\nMedicine.", "License": "GPL (>= 2)", "NeedsCompilation": "no", "RoxygenNote": "7.3.3", "Encoding": "UTF-8", "Config/testthat/edition": "3", "Roxygen": "list(markdown = TRUE)", "URL": "https://github.com/florianstijven/Surrogate-development", "BugReports": "https://github.com/florianstijven/Surrogate-development/issues", "Config/pak/sysreqs": "cmake make libicu-dev libjpeg-dev libpng-dev\nlibuv1-dev libx11-dev zlib1g-dev", "Repository": "https://florianstijven.r-universe.dev", "Date/Publication": "2026-06-04 07:07:51 UTC", "RemoteUrl": "https://github.com/florianstijven/surrogate-development", "RemoteRef": 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and SPF (surrogate predictive function)", "topics": [ "Bootstrap.MEP.BinBin" ] }, { "page": "CausalDiagramBinBin", "title": "Draws a causal diagram depicting the median informational coefficients of correlation (or odds ratios) between the counterfactuals for a specified range of values of the ICA in the binary-binary setting.", "topics": [ "CausalDiagramBinBin" ] }, { "page": "CausalDiagramContCont", "title": "Draws a causal diagram depicting the median correlations between the counterfactuals for a specified range of values of ICA or MICA in the continuous-continuous setting", "topics": [ "CausalDiagramContCont" ] }, { "page": "cdf_fun", "title": "Function factory for distribution functions", "topics": [ "cdf_fun" ] }, { "page": "clayton_loglik_copula_scale", "title": "Loglikelihood on the Copula Scale for the Clayton Copula", "topics": [ "clayton_loglik_copula_scale" ] }, { "page": "colorectal", "title": "The Colorectal dataset with a binary surrogate.", "topics": [ "colorectal" ] }, { "page": "colorectal4", "title": "The Colorectal dataset with an ordinal surrogate.", "topics": [ "colorectal4" ] }, { "page": "comb27.BinBin", "title": "Assesses the surrogate predictive value of each of the 27 prediction functions in the setting where both S and T are binary endpoints", "topics": [ "comb27.BinBin" ] }, { "page": "compute_ICA", "title": "Compute Individual Causal Association for a given D-vine copula model in the setting of choice.", "topics": [ "compute_ICA" ] }, { "page": "compute_ICA_BinCont", "title": "Compute Individual Causal Association for a given D-vine copula model in the Binary-Continuous Setting", "topics": [ "compute_ICA_BinCont" ] }, { "page": "compute_ICA_ContCont", "title": "Compute Individual Causal Association for a given D-vine copula model in the Continuous-Continuous Setting", "topics": [ "compute_ICA_ContCont" ] }, { "page": "compute_ICA_OrdCont", "title": "Compute Individual Causal Association for a given D-vine copula model in the Ordinal-Continuous Setting", "topics": [ "compute_ICA_OrdCont" ] }, { "page": "compute_ICA_OrdOrd", "title": "Compute Individual Causal Association for a given D-vine copula model in the Ordinal-Ordinal Setting", "topics": [ "compute_ICA_OrdOrd" ] }, { "page": "compute_ICA_SurvSurv", "title": "Compute Individual Causal Association for a given D-vine copula model in the Survival-Survival Setting", "topics": [ "compute_ICA_SurvSurv" ] }, { "page": "constructor_ICA_estimator", "title": "Function constructor to estimate the ICA given a set of sampled patient-level treatment effects", "topics": [ "constructor_ICA_estimator" ] }, { "page": "continuous_continuous_loglik", "title": "Loglikelihood function for continuous-continuous copula model", "topics": [ "continuous_continuous_loglik" ] }, { "page": "delta_method_log_mutinfo", "title": "Variance of log-mutual information based on the delta method", "topics": [ "delta_method_log_mutinfo" ] }, { "page": "Dvine_ICA_confint", "title": "Confidence interval for the ICA given the unidentifiable parameters", "topics": [ "Dvine_ICA_confint" ] }, { "page": "ECT", "title": "Apply the Entropy Concentration Theorem", "topics": [ "ECT" ] }, { "page": "estimate_ICA_BinCont", "title": "Estimate ICA in Binary-Continuous Setting", "topics": [ "estimate_ICA_BinCont" ] }, { "page": "estimate_ICA_ContCont", "title": "Estimate ICA in Ordinal-Ordinal Setting", "topics": [ "estimate_ICA_ContCont" ] }, { "page": "estimate_ICA_OrdCont", "title": "Estimate ICA in Ordinal-Continuous Setting", "topics": [ "estimate_ICA_OrdCont" ] }, { "page": "estimate_ICA_OrdOrd", "title": "Estimate ICA in Ordinal-Ordinal Setting", "topics": [ "estimate_ICA_OrdOrd" ] }, { "page": "estimate_marginal", "title": "Estimate marginal distribution using ML", "topics": [ "estimate_marginal" ] }, { "page": "estimate_mutual_information_SurvSurv", "title": "Estimate the Mutual Information in the Survival-Survival Setting", "topics": [ "estimate_mutual_information_SurvSurv" ] }, { "page": "Fano.BinBin", "title": "Evaluate the possibility of finding a good surrogate in the setting where both S and T are binary endpoints", "topics": [ "Fano.BinBin" ] }, { "page": "FederatedApproachStage1", "title": "Fits the first stage model in the two-stage federated data analysis approach.", "topics": [ "FederatedApproachStage1" ] }, { "page": "FederatedApproachStage2", "title": "Fits the second stage model in the two-stage federated data analysis approach.", "topics": [ "FederatedApproachStage2" ] }, { "page": "fit_copula_ContCont", "title": "Fit continuous-continuous vine copula model", "topics": [ "fit_copula_ContCont" ] }, { "page": "fit_copula_model_BinCont", "title": "Fit copula model for binary true endpoint and continuous surrogate endpoint", "topics": [ "fit_copula_model_BinCont" ] }, { "page": "fit_copula_OrdCont", "title": "Fit ordinal-continuous vine copula model", "topics": [ "fit_copula_OrdCont" ] }, { "page": "fit_copula_OrdOrd", "title": "Fit ordinal-ordinal vine copula model", "topics": [ "fit_copula_OrdOrd" ] }, { "page": "fit_copula_submodel_BinCont", "title": "Fit binary-continuous copula submodel", "topics": [ "fit_copula_submodel_BinCont" ] }, { "page": "fit_copula_submodel_ContCont", "title": "Fit ordinal-continuous copula submodel", "topics": [ "fit_copula_submodel_ContCont" ] }, { "page": "fit_copula_submodel_OrdCont", "title": "Fit ordinal-continuous copula submodel", "topics": [ "fit_copula_submodel_OrdCont" ] }, { "page": "fit_copula_submodel_OrdOrd", "title": "Fit ordinal-continuous copula submodel", "topics": [ "fit_copula_submodel_OrdOrd" ] }, { "page": "fit_model_SurvSurv", "title": "Fit Survival-Survival model", "topics": [ "fit_model_SurvSurv" ] }, { "page": "FixedBinBinIT", "title": "Fits (univariate) fixed-effect models to assess surrogacy in the binary-binary case based on the Information-Theoretic framework", "topics": [ "FixedBinBinIT" ] }, { "page": "FixedBinContIT", "title": "Fits (univariate) fixed-effect models to assess surrogacy in the case where the true endpoint is binary and the surrogate endpoint is continuous (based on the Information-Theoretic framework)", "topics": [ "FixedBinContIT" ] }, { "page": "FixedContBinIT", "title": "Fits (univariate) fixed-effect models to assess surrogacy in the case where the true endpoint is continuous and the surrogate endpoint is binary (based on the Information-Theoretic framework)", "topics": [ "FixedContBinIT" ] }, { "page": "FixedContContIT", "title": "Fits (univariate) fixed-effect models to assess surrogacy in the continuous-continuous case based on the Information-Theoretic framework", "topics": [ "FixedContContIT" ] }, { "page": "FixedDiscrDiscrIT", "title": "Investigates surrogacy for binary or ordinal outcomes using the Information Theoretic framework", "topics": [ "FixedDiscrDiscrIT" ] }, { "page": "frank_loglik_copula_scale", "title": "Loglikelihood on the Copula Scale for the Frank Copula", "topics": [ "frank_loglik_copula_scale" ] }, { "page": "gaussian_loglik_copula_scale", "title": "Loglikelihood on the Copula Scale for the Gaussian Copula", "topics": [ "gaussian_loglik_copula_scale" ] }, { "page": "gumbel_loglik_copula_scale", "title": "Loglikelihood on the Copula Scale for the Gumbel Copula", "topics": [ "gumbel_loglik_copula_scale" ] }, { "page": "ICA_alpha_ContCont", "title": "Assess surrogacy using a Rényi divergence based family of metrics in the causal-inference single-trial setting in normal case", "topics": [ "ICA_alpha_ContCont" ] }, { "page": "ICA_contcont_long_cre", "title": "Assess surrogacy in the information-theoretic causal-inference framework (Individual Causal Association, ICA) for a continuous surrogate and true endpoint measured repeatedly over time in a single-trial setting", "topics": [ "ICA_contcont_long_cre" ] }, { "page": "ICA_contcont_long_galecki", "title": "Assess surrogacy in the information-theoretic causal-inference framework (Individual Causal Association, ICA) for a continuous surrogate and true endpoint measured repeatedly over time in a single-trial setting", "topics": [ "ICA_contcont_long_galecki" ] }, { "page": "ICA_contcont_long_ri", "title": "Assess surrogacy in the information-theoretic causal-inference framework (Individual Causal Association, ICA) for a continuous surrogate and true endpoint measured repeatedly over time in a single-trial setting", "topics": [ "ICA_contcont_long_ri" ] }, { "page": "ICA_given_model_constructor", "title": "Constructor for the function that returns that ICA as a function of the identifiable parameters", "topics": [ "ICA_given_model_constructor" ] }, { "page": "ICA_given_model_constructor_SurvSurv", "title": "Constructor for the function that returns that ICA as a function of the identifiable parameters for survival-survival", "topics": [ "ICA_given_model_constructor_SurvSurv" ] }, { "page": "ICA_t", "title": "ICA under the t-causal model", "topics": [ "ICA_t" ] }, { "page": "ICABinBin", "title": "Assess surrogacy in the causal-inference single-trial setting in the binary-binary case", "topics": [ "ICA.BinBin" ] }, { "page": "ICABinBinCounterAssum", "title": "ICA (binary-binary setting) that is obtaied when the counterfactual correlations are assumed to fall within some prespecified ranges.", "topics": [ "ICA.BinBin.CounterAssum" ] }, { "page": "ICA.BinBin.Grid.Full", "title": "Assess surrogacy in the causal-inference single-trial setting in the binary-binary case when monotonicity for S and T is assumed using the full grid-based approach", "topics": [ "ICA.BinBin.Grid.Full" ] }, { "page": "ICA.BinBin.Grid.Sample", "title": "Assess surrogacy in the causal-inference single-trial setting in the binary-binary case when monotonicity for S and T is assumed using the grid-based sample approach", "topics": [ "ICA.BinBin.Grid.Sample" ] }, { "page": "ICA.BinBin.Grid.Sample.Uncert", "title": "Assess surrogacy in the causal-inference single-trial setting in the binary-binary case when monotonicity for S and T is assumed using the grid-based sample approach, accounting for sampling variability in the marginal pi.", "topics": [ "ICA.BinBin.Grid.Sample.Uncert" ] }, { "page": "ICABinCont", "title": "Assess surrogacy in the causal-inference single-trial setting in the binary-continuous case", "topics": [ "ICA.BinCont" ] }, { "page": "ICA.BinCont.BS", "title": "Assess surrogacy in the causal-inference single-trial setting in the binary-continuous case with an additional bootstrap procedure before the assessment", "topics": [ "ICA.BinCont.BS" ] }, { "page": "ICAContCont", "title": "Assess surrogacy in the causal-inference single-trial setting (Individual Causal Association, ICA) in the Continuous-continuous case", "topics": [ "ICA.ContCont" ] }, { "page": "ICA.ContCont.MultS", "title": "Assess surrogacy in the causal-inference single-trial setting (Individual Causal Association, ICA) using a continuous univariate T and multiple continuous S", "topics": [ "ICA.ContCont.MultS" ] }, { "page": "ICA.ContCont.Mult_alt", "title": "Assess surrogacy in the causal-inference single-trial setting (Individual Causal Association, ICA) using a continuous univariate T and multiple continuous S, alternative approach", "topics": [ "ICA.ContCont.MultS_alt" ] }, { "page": "ICA.ContCont.MultS.MPC", "title": "Assess surrogacy in the causal-inference single-trial setting (Individual Causal Association, ICA) using a continuous univariate T and multiple continuous S, by simulating correlation matrices using a modified algorithm based on partial correlations", "topics": [ "ICA.ContCont.MultS.MPC" ] }, { "page": "ICA.ContCont.MultS.PC", "title": "Assess surrogacy in the causal-inference single-trial setting (Individual Causal Association, ICA) using a continuous univariate T and multiple continuous S, by simulating correlation matrices using an algorithm based on partial correlations", "topics": [ "ICA.ContCont.MultS.PC" ] }, { "page": "ICA.Sample.ContCont", "title": "Assess surrogacy in the causal-inference single-trial setting (Individual Causal Association, ICA) in the Continuous-continuous case using the grid-based sample approach", "topics": [ "ICA.Sample.ContCont" ] }, { "page": "ICA.Sample.ControlTreat", "title": "Assess surrogacy in the causal-inference single-trial setting (Individual Causal Association, ICA) in the Continuous-continuous case using the grid-based sample approach when data is only avalable for the control treatment", "topics": [ "ICA.Sample.ControlTreat" ] }, { "page": "ISTE.ContCont", "title": "Individual-level surrogate threshold effect for continuous normally distributed surrogate and true endpoints.", "topics": [ "ISTE.ContCont" ] }, { "page": "log_likelihood_copula_model", "title": "Computes loglikelihood for a given copula model", "topics": [ "log_likelihood_copula_model" ] }, { "page": "loglik_copula_scale", "title": "Loglikelihood on the Copula Scale", "topics": [ "loglik_copula_scale" ] }, { "page": "LongToWide", "title": "Reshapes a dataset from the 'long' format (i.e., multiple lines per patient) into the 'wide' format (i.e., one line per patient)", "topics": [ "LongToWide" ] }, { "page": "marginal_distribution", "title": "Fit marginal distribution", "topics": [ "marginal_distribution" ] }, { "page": "marginal_gof_copula", "title": "Produce marginal GoF plot", "topics": [ "marginal_gof_copula" ] }, { "page": "marginal_gof_plots_scr", "title": "Marginal survival function goodness of fit", "topics": [ "marginal_gof_plots_scr" ] }, { "page": "marginal_gof_scr_S_plot", "title": "Goodness-of-fit plot for the marginal survival functions", "topics": [ "marginal_gof_scr_S_plot", "marginal_gof_scr_T_plot" ] }, { "page": "MarginalProbs", "title": "Computes marginal probabilities for a dataset where the surrogate and true endpoints are binary", "topics": [ "MarginalProbs" ] }, { "page": "MaxEntICAContCont", "title": "Use the maximum-entropy approach to compute ICA in the continuous-continuous sinlge-trial setting", "topics": [ "MaxEntContCont" ] }, { "page": "MaxEntICABinBin", "title": "Use the maximum-entropy approach to compute ICA in the binary-binary setting", "topics": [ "MaxEntICABinBin" ] }, { "page": "MaxEntSPFBinBin", "title": "Use the maximum-entropy approach to compute SPF (surrogate predictive function) in the binary-binary setting", "topics": [ "MaxEntSPFBinBin" ] }, { "page": "mean_S_before_T_plot_scr", "title": "Goodness of fit plot for the fitted copula", "topics": [ "mean_S_before_T_plot_scr", "prob_dying_without_progression_plot" ] }, { "page": "MetaAnalyticSurvBin", "title": "Compute surrogacy measures for a binary surrogate and a time-to-event true endpoint in the meta-analytic multiple-trial setting.", "topics": [ "MetaAnalyticSurvBin" ] }, { "page": "MetaAnalyticSurvCat", "title": "Compute surrogacy measures for a categorical (ordinal) surrogate and a time-to-event true endpoint in the meta-analytic multiple-trial setting.", "topics": [ "MetaAnalyticSurvCat" ] }, { "page": "MetaAnalyticSurvCont", "title": "Compute surrogacy measures for a continuous (normally-distributed) surrogate and a time-to-event true endpoint in the meta-analytic multiple-trial setting.", "topics": [ "MetaAnalyticSurvCont" ] }, { "page": "MetaAnalyticSurvSurv", "title": "Compute surrogacy measures for a time-to-event surrogate and a time-to-event true endpoint in the meta-analytic multiple-trial setting.", "topics": [ "MetaAnalyticSurvSurv" ] }, { "page": "MICAContCont", "title": "Assess surrogacy in the causal-inference multiple-trial setting (Meta-analytic Individual Causal Association; MICA) in the continuous-continuous case", "topics": [ "MICA.ContCont" ] }, { "page": "MICA.Sample.ContCont", "title": "Assess surrogacy in the causal-inference multiple-trial setting (Meta-analytic Individual Causal Association; MICA) in the continuous-continuous case using the grid-based sample approach", "topics": [ "MICA.Sample.ContCont" ] }, { "page": "MinSurrContCont", "title": "Examine the plausibility of finding a good surrogate endpoint in the Continuous-continuous case", "topics": [ "MinSurrContCont" ] }, { "page": "MixedContContIT", "title": "Fits (univariate) mixed-effect models to assess surrogacy in the continuous-continuous case based on the Information-Theoretic framework", "topics": [ "MixedContContIT" ] }, { "page": "model_fit_measures", "title": "Goodness of fit information for survival-survival model", "topics": [ "model_fit_measures" ] }, { "page": "MufixedContCont.MultS", "title": "Fits a multivariate fixed-effects model to assess surrogacy in the meta-analytic multiple-trial setting (Continuous-continuous case with multiple surrogates)", "topics": [ "MufixedContCont.MultS" ] }, { "page": "MumixedContCont.MultS", "title": "Fits a multivariate mixed-effects model to assess surrogacy in the meta-analytic multiple-trial setting (Continuous-continuous case with multiple surrogates)", "topics": [ "MumixedContCont.MultS" ] }, { "page": "new_vine_copula_fit", "title": "Constructor for vine copula model", "topics": [ "new_vine_copula_fit" ] }, { "page": "new_vine_copula_ss_fit", "title": "Constructor for vine copula model", "topics": [ "new_vine_copula_ss_fit" ] }, { "page": "ordinal_continuous_loglik", "title": "Loglikelihood function for ordinal-continuous copula model", "topics": [ "ordinal_continuous_loglik" ] }, { "page": "ordinal_ordinal_loglik", "title": "Loglikelihood function for ordinal-ordinal copula model", "topics": [ "ordinal_ordinal_loglik" ] }, { "page": "ordinal_to_cutpoints", "title": "Convert Ordinal Observations to Latent Cutpoints", "topics": [ "ordinal_to_cutpoints" ] }, { "page": "Ovarian", "title": "The Ovarian dataset", "topics": [ "Ovarian" ] }, { "page": "PANSS", "title": "PANSS subscales and total score based on the data of five clinical trials in schizophrenia", "topics": [ "PANSS" ] }, { "page": "pdf_fun", "title": "Function factory for density functions", "topics": [ "pdf_fun" ] }, { "page": "plot.ICABinBin", "title": "Plots the (Meta-Analytic) Individual Causal Association and related metrics when S and T are binary outcomes", "topics": [ "plot Causal-Inference BinBin", "plot.ICA.BinBin" ] }, { "page": "plot.CausalInference", "title": "Plots the (Meta-Analytic) Individual Causal Association when S and T are continuous outcomes", "topics": [ "plot Causal-Inference ContCont", "plot.ICA.ContCont", "plot.MICA.ContCont" ] }, { "page": "Plot.FixedDiscrDiscrIT", "title": "Provides plots of trial-level surrogacy in the Information-Theoretic framework", "topics": [ "plot FixedDiscrDiscrIT", "plot.FixedDiscrDiscrIT" ] }, { "page": "plot.ICA.ContCont.Mult", "title": "Plots the Individual Causal Association in the setting where there are multiple continuous S and a continuous T", "topics": [ "plot.ICA.ContCont.MultS", "plot.ICA.ContCont.MultS_alt" ] }, { "page": "plot.InformationTheoretic", "title": "Provides plots of trial- and individual-level surrogacy in the Information-Theoretic framework", "topics": [ "plot Information-Theoretic", "plot.FixedContContIT", "plot.MixedContContIT" ] }, { "page": "plot.InformationTheoreticBinCombn", "title": "Provides plots of trial- and individual-level surrogacy in the Information-Theoretic framework when both S and T are binary, or when S is binary and T is continuous (or vice versa)", "topics": [ "plot Information-Theoretic BinCombn", "plot.FixedBinBinIT", "plot.FixedBinContIT", "plot.FixedContBinIT" ] }, { "page": "plot.ISTE.ContCont", "title": "Plots the individual-level surrogate threshold effect (STE) values and related metrics", "topics": [ "plot ISTE.ContCont", "plot.ISTE.ContCont" ] }, { "page": "plot.MaxEntContCont", "title": "Plots the sensitivity-based and maximum entropy based Individual Causal Association when S and T are continuous outcomes in the single-trial setting", "topics": [ "plot MaxEnt ContCont", "plot.MaxEntContCont" ] }, { "page": "plot.MaxEntICABinBin", "title": "Plots the sensitivity-based and maximum entropy based Individual Causal Association when S and T are binary outcomes", "topics": [ "plot MaxEntICA BinBin", "plot.MaxEntICA.BinBin" ] }, { "page": "plot.MaxEntSPFBinBin", "title": "Plots the sensitivity-based and maximum entropy based surrogate predictive function (SPF) when S and T are binary outcomes.", "topics": [ "plot MaxEntSPF BinBin", "plot.MaxEntSPF.BinBin" ] }, { "page": "plot.MetaAnalytic", "title": "Provides plots of trial- and individual-level surrogacy in the meta-analytic framework", "topics": [ "plot Meta-Analytic", "plot.BifixedContCont", "plot.BimixedContCont", "plot.UnifixedContCont", "plot.UnimixedContCont" ] }, { "page": "plot.MinSurrContCont", "title": "Graphically illustrates the theoretical plausibility of finding a good surrogate endpoint in the continuous-continuous case", "topics": [ "plot MinSurrContCont", "plot.MinSurrContCont" ] }, { "page": "Plot.PredTrialTContCont", "title": "Plots the expected treatment effect on the true endpoint in a new trial (when both S and T are normally distributed continuous endpoints)", "topics": [ "plot PredTrialTContCont", "plot.PredTrialTContCont" ] }, { "page": "plot.SPPBinBin", "title": "Plots the surrogate predictive function (SPF) in the binary-binary settinf.", "topics": [ "plot SPF BinBin", "plot.SPF.BinBin" ] }, { "page": "plot.TrialLevelIT", "title": "Provides a plots of trial-level surrogacy in the information-theoretic framework based on the output of the 'TrialLevelIT()' function", "topics": [ "plot.TrialLevelIT" ] }, { "page": "plot.TrialLevelMA", "title": "Provides a plots of trial-level surrogacy in the meta-analytic framework based on the output of the 'TrialLevelMA()' function", "topics": [ "plot.TrialLevelMA" ] }, { "page": "plot.TwoStageSurvSurv", "title": "Plots trial-level surrogacy in the meta-analytic framework when two survival endpoints are considered.", "topics": [ "plot.TwoStageSurvSurv" ] }, { "page": "plot.comb27.BinBin", "title": "Plots the distribution of prediction error functions in decreasing order of appearance.", "topics": [ "plot.comb27.BinBin" ] }, { "page": "plot.Fano.BinBin", "title": "Plots the distribution of R^2_{HL} either as a density or as function of pi_{10} in the setting where both S and T are binary endpoints", "topics": [ "plot.Fano.BinBin" ] }, { "page": "plot.ICA.BinCont", "title": "Plot the individual causal association (ICA) in the causal-inference single-trial setting in the binary-continuous case.", "topics": [ "plot.ICA.BinCont" ] }, { "page": "plot.MetaAnalyticSurvBin", "title": "Generates a plot of the estimated treatment effects for the surrogate endpoint versus the estimated treatment effects for the true endpoint for an object fitted with the 'MetaAnalyticSurvBin()' function.", "topics": [ "plot.MetaAnalyticSurvBin" ] }, { "page": "plot.MetaAnalyticSurvCat", "title": "Generates a plot of the estimated treatment effects for the surrogate endpoint versus the estimated treatment effects for the true endpoint for an object fitted with the 'MetaAnalyticSurvCat()' function.", "topics": [ "plot.MetaAnalyticSurvCat" ] }, { "page": "plot.MetaAnalyticSurvCont", "title": "Generates a plot of the estimated treatment effects for the surrogate endpoint versus the estimated treatment effects for the true endpoint for an object fitted with the 'MetaAnalyticSurvCont()' function.", "topics": [ "plot.MetaAnalyticSurvCont" ] }, { "page": "plot.MetaAnalyticSurvSurv", "title": "Generates a plot of the estimated treatment effects for the surrogate endpoint versus the estimated treatment effects for the true endpoint for an object fitted with the 'MetaAnalyticSurvSurv()' function.", "topics": [ "plot.MetaAnalyticSurvSurv" ] }, { "page": "plot.PPE.BinBin", "title": "Plots the distribution of either PPE, RPE or R^2_{H} either as a density or as a histogram in the setting where both S and T are binary endpoints", "topics": [ "plot.PPE.BinBin" ] }, { "page": "plot.SPF.BinCont", "title": "Plot the surrogate predictive function (SPF) in the causal-inference single-trial setting in the binary-continuous case.", "topics": [ "plot.SPF.BinCont" ] }, { "page": "plot.InformationTheoreticSurvSurv", "title": "Provides plots of trial- and individual-level surrogacy in the Information-Theoretic framework when both S and T are time-to-event endpoints", "topics": [ "plot.SurvSurv" ] }, { "page": "plot.vine_copula_fit", "title": "Goodness-of-fit plots for the fitted copula models", "topics": [ "plot.vine_copula_fit" ] }, { "page": "Pos.Def.Matrices", "title": "Generate 4 by 4 correlation matrices and flag the positive definite ones", "topics": [ "Pos.Def.Matrices" ] }, { "page": "PPE.BinBin", "title": "Evaluate a surrogate predictive value based on the minimum probability of a prediction error in the setting where both S and T are binary endpoints", "topics": [ "PPE.BinBin" ] }, { "page": "Pred.TrialT.ContCont", "title": "Compute the expected treatment effect on the true endpoint in a new trial (when both S and T are normally distributed continuous endpoints)", "topics": [ "Pred.TrialT.ContCont" ] }, { "page": "Prentice", "title": "Evaluates surrogacy based on the Prentice criteria for continuous endpoints (single-trial setting)", "topics": [ "Prentice" ] }, { "page": "print.MetaAnalyticSurvBin", "title": "Prints all the elements of an object fitted with the 'MetaAnalyticSurvBin()' function.", "topics": [ "print.MetaAnalyticSurvBin" ] }, { "page": "print.MetaAnalyticSurvCat", "title": "Prints all the elements of an object fitted with the 'MetaAnalyticSurvCat()' function.", "topics": [ "print.MetaAnalyticSurvCat" ] }, { "page": "print.MetaAnalyticSurvCont", "title": "Prints all the elements of an object fitted with the 'MetaAnalyticSurvCont()' function.", "topics": [ "print.MetaAnalyticSurvCont" ] }, { "page": "print.MetaAnalyticSurvSurv", "title": "Prints all the elements of an object fitted with the 'MetaAnalyticSurvSurv()' function.", "topics": [ "print.MetaAnalyticSurvSurv" ] }, { "page": "print.vine_copula_fit", "title": "Print summary of fitted copula model", "topics": [ "print.vine_copula_fit" ] }, { "page": "PROC.BinBin", "title": "Evaluate the individual causal association (ICA) and reduction in probability of a prediction error (RPE) in the setting where both S and T are binary endpoints", "topics": [ "PROC.BinBin" ] }, { "page": "prostate", "title": "The prostate dataset with a continuous surrogate.", "topics": [ "prostate" ] }, { "page": "RandVec", "title": "Generate random vectors with a fixed sum", "topics": [ "RandVec" ] }, { "page": "Restrictions.BinBin", "title": "Examine restrictions in pi_{f} under different montonicity assumptions for binary S and T", "topics": [ "Restrictions.BinBin" ] }, { "page": "sample_copula_parameters", "title": "Sample Unidentifiable Copula Parameters", "topics": [ "sample_copula_parameters" ] }, { "page": "sample_deltas_BinCont", "title": "Sample individual casual treatment effects from given D-vine copula model in binary continuous setting", "topics": [ "sample_deltas_BinCont" ] }, { "page": "sample_dvine", "title": "Sample copula data from a given four-dimensional D-vine copula", "topics": [ "sample_dvine" ] }, { "page": "Schizo", "title": "Data of five clinical trials in schizophrenia", "topics": [ "Schizo" ] }, { "page": "Schizo_Bin", "title": "Data of a clinical trial in Schizophrenia (with binary outcomes).", "topics": [ "Schizo_Bin" ] }, { "page": "Schizo_BinCont", "title": "Data of a clinical trial in schizophrenia, with binary and continuous endpoints", "topics": [ "Schizo_BinCont" ] }, { "page": "Schizo_PANSS", "title": "Longitudinal PANSS data of five clinical trials in schizophrenia", "topics": [ "Schizo_PANSS" ] }, { "page": "sensitivity_analysis_BinCont_copula", "title": "Perform Sensitivity Analysis for the Individual Causal Association with a Continuous Surrogate and Binary True Endpoint", 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