A B C D E F G I L M N O P R S T U
AA.MultS | Compute the multiple-surrogate adjusted association |
ARMD | Data of the Age-Related Macular Degeneration Study |
ARMD.MultS | Data of the Age-Related Macular Degeneration Study with multiple candidate surrogates |
BifixedContCont | Fits a bivariate fixed-effects model to assess surrogacy in the meta-analytic multiple-trial setting (Continuous-continuous case) |
BimixedCbCContCont | Fits a bivariate mixed-effects model using the cluster-by-cluster (CbC) estimator to assess surrogacy in the meta-analytic multiple-trial setting (Continuous-continuous case) |
BimixedContCont | Fits a bivariate mixed-effects model to assess surrogacy in the meta-analytic multiple-trial setting (Continuous-continuous case) |
binary_continuous_loglik | Loglikelihood function for binary-continuous copula model |
Bootstrap.MEP.BinBin | Bootstrap 95% CI around the maximum-entropy ICA and SPF (surrogate predictive function) |
CausalDiagramBinBin | 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. |
CausalDiagramContCont | 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 |
cdf_fun | Function factory for distribution functions |
clayton_loglik_copula_scale | Loglikelihood on the Copula Scale for the Clayton Copula |
colorectal | The Colorectal dataset with a binary surrogate. |
colorectal4 | The Colorectal dataset with an ordinal surrogate. |
comb27.BinBin | Assesses the surrogate predictive value of each of the 27 prediction functions in the setting where both S and T are binary endpoints |
compute_ICA_BinCont | Compute Individual Causal Association for a given D-vine copula model in the Binary-Continuous Setting |
compute_ICA_SurvSurv | Compute Individual Causal Association for a given D-vine copula model in the Survival-Survival Setting |
delta_method_log_mutinfo | Variance of log-mutual information based on the delta method |
Dvine_ICA_confint | Confidence interval for the ICA given the unidentifiable parameters |
ECT | Apply the Entropy Concentration Theorem |
estimate_ICA_BinCont | Estimate ICA in Binary-Continuous Setting |
estimate_mutual_information_SurvSurv | Estimate the Mutual Information in the Survival-Survival Setting |
Fano.BinBin | Evaluate the possibility of finding a good surrogate in the setting where both S and T are binary endpoints |
FederatedApproachStage1 | Fits the first stage model in the two-stage federated data analysis approach. |
FederatedApproachStage2 | Fits the second stage model in the two-stage federated data analysis approach. |
fit_copula_model_BinCont | Fit copula model for binary true endpoint and continuous surrogate endpoint |
fit_copula_submodel_BinCont | Fit binary-continuous copula submodel |
fit_model_SurvSurv | Fit Survival-Survival model |
FixedBinBinIT | Fits (univariate) fixed-effect models to assess surrogacy in the binary-binary case based on the Information-Theoretic framework |
FixedBinContIT | 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) |
FixedContBinIT | 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) |
FixedContContIT | Fits (univariate) fixed-effect models to assess surrogacy in the continuous-continuous case based on the Information-Theoretic framework |
FixedDiscrDiscrIT | Investigates surrogacy for binary or ordinal outcomes using the Information Theoretic framework |
frank_loglik_copula_scale | Loglikelihood on the Copula Scale for the Frank Copula |
gaussian_loglik_copula_scale | Loglikelihood on the Copula Scale for the Gaussian Copula |
gumbel_loglik_copula_scale | Loglikelihood on the Copula Scale for the Gumbel Copula |
ICA.BinBin | Assess surrogacy in the causal-inference single-trial setting in the binary-binary case |
ICA.BinBin.CounterAssum | ICA (binary-binary setting) that is obtaied when the counterfactual correlations are assumed to fall within some prespecified ranges. |
ICA.BinBin.Grid.Full | 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 |
ICA.BinBin.Grid.Sample | 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 |
ICA.BinBin.Grid.Sample.Uncert | 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. |
ICA.BinCont | Assess surrogacy in the causal-inference single-trial setting in the binary-continuous case |
ICA.BinCont.BS | Assess surrogacy in the causal-inference single-trial setting in the binary-continuous case with an additional bootstrap procedure before the assessment |
ICA.ContCont | Assess surrogacy in the causal-inference single-trial setting (Individual Causal Association, ICA) in the Continuous-continuous case |
ICA.ContCont.MultS | Assess surrogacy in the causal-inference single-trial setting (Individual Causal Association, ICA) using a continuous univariate T and multiple continuous S |
ICA.ContCont.MultS.MPC | 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 |
ICA.ContCont.MultS.PC | 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 |
ICA.ContCont.MultS_alt | Assess surrogacy in the causal-inference single-trial setting (Individual Causal Association, ICA) using a continuous univariate T and multiple continuous S, alternative approach |
ICA.Sample.ContCont | Assess surrogacy in the causal-inference single-trial setting (Individual Causal Association, ICA) in the Continuous-continuous case using the grid-based sample approach |
ICA_given_model_constructor | Constructor for the function that returns that ICA as a function of the identifiable parameters |
ISTE.ContCont | Individual-level surrogate threshold effect for continuous normally distributed surrogate and true endpoints. |
loglik_copula_scale | Loglikelihood on the Copula Scale |
log_likelihood_copula_model | Computes loglikelihood for a given copula model |
LongToWide | Reshapes a dataset from the 'long' format (i.e., multiple lines per patient) into the 'wide' format (i.e., one line per patient) |
MarginalProbs | Computes marginal probabilities for a dataset where the surrogate and true endpoints are binary |
marginal_distribution | Fit marginal distribution |
marginal_gof_plots_scr | Marginal survival function goodness of fit |
marginal_gof_scr_S_plot | Goodness-of-fit plot for the marginal survival functions |
marginal_gof_scr_T_plot | Goodness-of-fit plot for the marginal survival functions |
MaxEntContCont | Use the maximum-entropy approach to compute ICA in the continuous-continuous sinlge-trial setting |
MaxEntICABinBin | Use the maximum-entropy approach to compute ICA in the binary-binary setting |
MaxEntSPFBinBin | Use the maximum-entropy approach to compute SPF (surrogate predictive function) in the binary-binary setting |
mean_S_before_T_plot_scr | Goodness of fit plot for the fitted copula |
MetaAnalyticSurvBin | Compute surrogacy measures for a binary surrogate and a time-to-event true endpoint in the meta-analytic multiple-trial setting. |
MetaAnalyticSurvCat | Compute surrogacy measures for a categorical (ordinal) surrogate and a time-to-event true endpoint in the meta-analytic multiple-trial setting. |
MetaAnalyticSurvCont | Compute surrogacy measures for a continuous (normally-distributed) surrogate and a time-to-event true endpoint in the meta-analytic multiple-trial setting. |
MetaAnalyticSurvSurv | Compute surrogacy measures for a time-to-event surrogate and a time-to-event true endpoint in the meta-analytic multiple-trial setting. |
MICA.ContCont | Assess surrogacy in the causal-inference multiple-trial setting (Meta-analytic Individual Causal Association; MICA) in the continuous-continuous case |
MICA.Sample.ContCont | 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 |
MinSurrContCont | Examine the plausibility of finding a good surrogate endpoint in the Continuous-continuous case |
MixedContContIT | Fits (univariate) mixed-effect models to assess surrogacy in the continuous-continuous case based on the Information-Theoretic framework |
model_fit_measures | Goodness of fit information for survival-survival model |
MufixedContCont.MultS | Fits a multivariate fixed-effects model to assess surrogacy in the meta-analytic multiple-trial setting (Continuous-continuous case with multiple surrogates) |
MumixedContCont.MultS | Fits a multivariate mixed-effects model to assess surrogacy in the meta-analytic multiple-trial setting (Continuous-continuous case with multiple surrogates) |
new_vine_copula_ss_fit | Constructor for vine copula model |
Ovarian | The Ovarian dataset |
PANSS | PANSS subscales and total score based on the data of five clinical trials in schizophrenia |
pdf_fun | Function factory for density functions |
plot Causal-Inference BinBin | Plots the (Meta-Analytic) Individual Causal Association and related metrics when S and T are binary outcomes |
plot Causal-Inference ContCont | Plots the (Meta-Analytic) Individual Causal Association when S and T are continuous outcomes |
plot FixedDiscrDiscrIT | Provides plots of trial-level surrogacy in the Information-Theoretic framework |
plot Information-Theoretic | Provides plots of trial- and individual-level surrogacy in the Information-Theoretic framework |
plot Information-Theoretic BinCombn | 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) |
plot ISTE.ContCont | Plots the individual-level surrogate threshold effect (STE) values and related metrics |
plot MaxEnt ContCont | Plots the sensitivity-based and maximum entropy based Individual Causal Association when S and T are continuous outcomes in the single-trial setting |
plot MaxEntICA BinBin | Plots the sensitivity-based and maximum entropy based Individual Causal Association when S and T are binary outcomes |
plot MaxEntSPF BinBin | Plots the sensitivity-based and maximum entropy based surrogate predictive function (SPF) when S and T are binary outcomes. |
plot Meta-Analytic | Provides plots of trial- and individual-level surrogacy in the meta-analytic framework |
plot MinSurrContCont | Graphically illustrates the theoretical plausibility of finding a good surrogate endpoint in the continuous-continuous case |
plot PredTrialTContCont | Plots the expected treatment effect on the true endpoint in a new trial (when both S and T are normally distributed continuous endpoints) |
plot SPF BinBin | Plots the surrogate predictive function (SPF) in the binary-binary settinf. |
plot.BifixedContCont | Provides plots of trial- and individual-level surrogacy in the meta-analytic framework |
plot.BimixedContCont | Provides plots of trial- and individual-level surrogacy in the meta-analytic framework |
plot.comb27.BinBin | Plots the distribution of prediction error functions in decreasing order of appearance. |
plot.Fano.BinBin | 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 |
plot.FixedBinBinIT | 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) |
plot.FixedBinContIT | 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) |
plot.FixedContBinIT | 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) |
plot.FixedContContIT | Provides plots of trial- and individual-level surrogacy in the Information-Theoretic framework |
plot.FixedDiscrDiscrIT | Provides plots of trial-level surrogacy in the Information-Theoretic framework |
plot.ICA.BinBin | Plots the (Meta-Analytic) Individual Causal Association and related metrics when S and T are binary outcomes |
plot.ICA.BinCont | Plot the individual causal association (ICA) in the causal-inference single-trial setting in the binary-continuous case. |
plot.ICA.ContCont | Plots the (Meta-Analytic) Individual Causal Association when S and T are continuous outcomes |
plot.ICA.ContCont.MultS | Plots the Individual Causal Association in the setting where there are multiple continuous S and a continuous T |
plot.ICA.ContCont.MultS_alt | Plots the Individual Causal Association in the setting where there are multiple continuous S and a continuous T |
plot.ISTE.ContCont | Plots the individual-level surrogate threshold effect (STE) values and related metrics |
plot.MaxEntContCont | Plots the sensitivity-based and maximum entropy based Individual Causal Association when S and T are continuous outcomes in the single-trial setting |
plot.MaxEntICA.BinBin | Plots the sensitivity-based and maximum entropy based Individual Causal Association when S and T are binary outcomes |
plot.MaxEntSPF.BinBin | Plots the sensitivity-based and maximum entropy based surrogate predictive function (SPF) when S and T are binary outcomes. |
plot.MetaAnalyticSurvBin | 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. |
plot.MetaAnalyticSurvCat | 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. |
plot.MetaAnalyticSurvCont | 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. |
plot.MetaAnalyticSurvSurv | 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. |
plot.MICA.ContCont | Plots the (Meta-Analytic) Individual Causal Association when S and T are continuous outcomes |
plot.MinSurrContCont | Graphically illustrates the theoretical plausibility of finding a good surrogate endpoint in the continuous-continuous case |
plot.MixedContContIT | Provides plots of trial- and individual-level surrogacy in the Information-Theoretic framework |
plot.PPE.BinBin | 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 |
plot.PredTrialTContCont | Plots the expected treatment effect on the true endpoint in a new trial (when both S and T are normally distributed continuous endpoints) |
plot.Single.Trial.RE.AA | Conducts a surrogacy analysis based on the single-trial meta-analytic framework |
plot.SPF.BinBin | Plots the surrogate predictive function (SPF) in the binary-binary settinf. |
plot.SPF.BinCont | Plot the surrogate predictive function (SPF) in the causal-inference single-trial setting in the binary-continuous case. |
plot.SurvSurv | Provides plots of trial- and individual-level surrogacy in the Information-Theoretic framework when both S and T are time-to-event endpoints |
plot.TrialLevelIT | Provides a plots of trial-level surrogacy in the information-theoretic framework based on the output of the 'TrialLevelIT()' function |
plot.TrialLevelMA | Provides a plots of trial-level surrogacy in the meta-analytic framework based on the output of the 'TrialLevelMA()' function |
plot.TwoStageSurvSurv | Plots trial-level surrogacy in the meta-analytic framework when two survival endpoints are considered. |
plot.UnifixedContCont | Provides plots of trial- and individual-level surrogacy in the meta-analytic framework |
plot.UnimixedContCont | Provides plots of trial- and individual-level surrogacy in the meta-analytic framework |
Pos.Def.Matrices | Generate 4 by 4 correlation matrices and flag the positive definite ones |
PPE.BinBin | 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 |
Pred.TrialT.ContCont | Compute the expected treatment effect on the true endpoint in a new trial (when both S and T are normally distributed continuous endpoints) |
Prentice | Evaluates surrogacy based on the Prentice criteria for continuous endpoints (single-trial setting) |
print.MetaAnalyticSurvBin | Prints all the elements of an object fitted with the 'MetaAnalyticSurvBin()' function. |
print.MetaAnalyticSurvCat | Prints all the elements of an object fitted with the 'MetaAnalyticSurvCat()' function. |
print.MetaAnalyticSurvCont | Prints all the elements of an object fitted with the 'MetaAnalyticSurvCont()' function. |
print.MetaAnalyticSurvSurv | Prints all the elements of an object fitted with the 'MetaAnalyticSurvSurv()' function. |
prob_dying_without_progression_plot | Goodness of fit plot for the fitted copula |
PROC.BinBin | 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 |
prostate | The prostate dataset with a continuous surrogate. |
RandVec | Generate random vectors with a fixed sum |
Restrictions.BinBin | Examine restrictions in pi_{f} under different montonicity assumptions for binary S and T |
sample_copula_parameters | Sample Unidentifiable Copula Parameters |
sample_deltas_BinCont | Sample individual casual treatment effects from given D-vine copula model in binary continuous setting |
sample_dvine | Sample copula data from a given four-dimensional D-vine copula |
Schizo | Data of five clinical trials in schizophrenia |
Schizo_Bin | Data of a clinical trial in Schizophrenia (with binary outcomes). |
Schizo_BinCont | Data of a clinical trial in schizophrenia, with binary and continuous endpoints |
Schizo_PANSS | Longitudinal PANSS data of five clinical trials in schizophrenia |
sensitivity_analysis_BinCont_copula | Perform Sensitivity Analysis for the Individual Causal Association with a Continuous Surrogate and Binary True Endpoint |
sensitivity_analysis_SurvSurv_copula | Sensitivity analysis for individual causal association |
sensitivity_intervals_Dvine | Compute Sensitivity Intervals |
Sim.Data.Counterfactuals | Simulate a dataset that contains counterfactuals |
Sim.Data.CounterfactualsBinBin | Simulate a dataset that contains counterfactuals for binary endpoints |
Sim.Data.MTS | Simulates a dataset that can be used to assess surrogacy in the multiple-trial setting |
Sim.Data.STS | Simulates a dataset that can be used to assess surrogacy in the single-trial setting |
Sim.Data.STSBinBin | Simulates a dataset that can be used to assess surrogacy in the single trial setting when S and T are binary endpoints |
Single.Trial.RE.AA | Conducts a surrogacy analysis based on the single-trial meta-analytic framework |
SPF.BinBin | Evaluate the surrogate predictive function (SPF) in the binary-binary setting (sensitivity-analysis based approach) |
SPF.BinCont | Evaluate the surrogate predictive function (SPF) in the causal-inference single-trial setting in the binary-continuous case |
summary.FederatedApproachStage2 | Provides a summary of the surrogacy measures for an object fitted with the 'FederatedApproachStage2()' function. |
summary.MetaAnalyticSurvBin | Provides a summary of the surrogacy measures for an object fitted with the 'MetaAnalyticSurvBin()' function. |
summary.MetaAnalyticSurvCat | Provides a summary of the surrogacy measures for an object fitted with the 'MetaAnalyticSurvCat()' function. |
summary.MetaAnalyticSurvCont | Provides a summary of the surrogacy measures for an object fitted with the 'MetaAnalyticSurvCont()' function. |
summary.MetaAnalyticSurvSurv | Provides a summary of the surrogacy measures for an object fitted with the 'MetaAnalyticSurvSurv()' function. |
summary_level_bootstrap_ICA | Bootstrap based on the multivariate normal sampling distribution |
SurvSurv | Assess surrogacy for two survival endpoints based on information theory and a two-stage approach |
Test.Mono | Test whether the data are compatible with monotonicity for S and/or T (binary endpoints) |
TrialLevelIT | Estimates trial-level surrogacy in the information-theoretic framework |
TrialLevelMA | Estimates trial-level surrogacy in the meta-analytic framework |
TwoStageSurvSurv | Assess trial-level surrogacy for two survival endpoints using a two-stage approach |
twostep_BinCont | Fit binary-continuous copula submodel with two-step estimator |
twostep_SurvSurv | Fit survival-survival copula submodel with two-step estimator |
UnifixedContCont | Fits univariate fixed-effect models to assess surrogacy in the meta-analytic multiple-trial setting (continuous-continuous case) |
UnimixedContCont | Fits univariate mixed-effect models to assess surrogacy in the meta-analytic multiple-trial setting (continuous-continuous case) |