R: Evaluation of Surrogate Endpoints in Clinical Trials

# Evaluation of Surrogate Endpoints in Clinical Trials

## Documentation for package ‘Surrogate’ version 3.4.1

- DESCRIPTION file.
- Package NEWS.

## Help Pages

A B C D E F G I L M N O P R S T U

## -- A --

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
association_gof_copula | Produce Associational GoF plot

## -- B --

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)

## -- C --

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 | Compute Individual Causal Association for a given D-vine copula model in the setting of choice.
compute_ICA_BinCont | Compute Individual Causal Association for a given D-vine copula model in the Binary-Continuous Setting
compute_ICA_ContCont | Compute Individual Causal Association for a given D-vine copula model in the Continuous-Continuous Setting
compute_ICA_OrdCont | Compute Individual Causal Association for a given D-vine copula model in the Ordinal-Continuous Setting
compute_ICA_OrdOrd | Compute Individual Causal Association for a given D-vine copula model in the Ordinal-Ordinal Setting
compute_ICA_SurvSurv | Compute Individual Causal Association for a given D-vine copula model in the Survival-Survival Setting
constructor_ICA_estimator | Function constructor to estimate the ICA given a set of sampled patient-level treatment effects
continuous_continuous_loglik | Loglikelihood function for continuous-continuous copula model

## -- D --

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

## -- E --

ECT | Apply the Entropy Concentration Theorem
estimate_ICA_BinCont | Estimate ICA in Binary-Continuous Setting
estimate_ICA_ContCont | Estimate ICA in Ordinal-Ordinal Setting
estimate_ICA_OrdCont | Estimate ICA in Ordinal-Continuous Setting
estimate_ICA_OrdOrd | Estimate ICA in Ordinal-Ordinal Setting
estimate_marginal | Estimate marginal distribution using ML
estimate_mutual_information_SurvSurv | Estimate the Mutual Information in the Survival-Survival Setting

## -- F --

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_ContCont | Fit continuous-continuous vine copula model
fit_copula_model_BinCont | Fit copula model for binary true endpoint and continuous surrogate endpoint
fit_copula_OrdCont | Fit ordinal-continuous vine copula model
fit_copula_OrdOrd | Fit ordinal-ordinal vine copula model
fit_copula_submodel_BinCont | Fit binary-continuous copula submodel
fit_copula_submodel_ContCont | Fit ordinal-continuous copula submodel
fit_copula_submodel_OrdCont | Fit ordinal-continuous copula submodel
fit_copula_submodel_OrdOrd | Fit ordinal-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

## -- G --

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

## -- I --

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.Sample.ControlTreat | 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
ICA_alpha_ContCont | Assess surrogacy using a Rényi divergence based family of metrics in the causal-inference single-trial setting in normal case
ICA_given_model_constructor | Constructor for the function that returns that ICA as a function of the identifiable parameters
ICA_given_model_constructor_SurvSurv | Constructor for the function that returns that ICA as a function of the identifiable parameters for survival-survival
ICA_t | The function 'ICA_t()' is to evaluate surrogacy in the single-trial causal-inference framework.
ISTE.ContCont | Individual-level surrogate threshold effect for continuous normally distributed surrogate and true endpoints.

## -- L --

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)

## -- M --

MarginalProbs | Computes marginal probabilities for a dataset where the surrogate and true endpoints are binary
marginal_distribution | Fit marginal distribution
marginal_gof_copula | Produce marginal GoF plot
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)

## -- N --

new_vine_copula_fit | Constructor for vine copula model
new_vine_copula_ss_fit | Constructor for vine copula model

## -- O --

ordinal_continuous_loglik | Loglikelihood function for ordinal-continuous copula model
ordinal_ordinal_loglik | Loglikelihood function for ordinal-ordinal copula model
ordinal_to_cutpoints | Convert Ordinal Observations to Latent Cutpoints
Ovarian | The Ovarian dataset

## -- P --

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
plot.vine_copula_fit | Goodness-of-fit plots for the fitted copula models
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.
print.vine_copula_fit | Print summary of fitted copula model
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.

## -- R --

RandVec | Generate random vectors with a fixed sum
Restrictions.BinBin | Examine restrictions in pi_{f} under different montonicity assumptions for binary S and T

## -- S --

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_copula | Perform Sensitivity Analysis for the Individual Causal Association based on a D-vine copula model
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

## -- T --

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

## -- U --

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)