Evaluation of Surrogate Endpoints in Clinical Trials

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Documentation for package ‘Surrogate’ version 3.2.5

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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

-- 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_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

-- 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_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
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

-- 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_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.

-- 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_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
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_ss_fit Constructor for vine copula model

-- O --

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 BinCont Plots the (Meta-Analytic) Individual Causal Association and related metrics when S is continuous and T is binary
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 SPF BinCont Plots the surrogate predictive function (SPF) in the binary-continuous setting.
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 Plots the (Meta-Analytic) Individual Causal Association and related metrics when S is continuous and T is binary
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.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 Plots the surrogate predictive function (SPF) in the binary-continuous setting.
plot.survbin 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 'survbin()' function.
plot.survcat 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 'survcat()' function.
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.survbin Prints all the elements of an object fitted with the 'survbin()' function.
print.survcat Prints all the elements of an object fitted with the 'survcat()' 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

-- 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_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 binary-continuous setting (sensitivity-analysis based approach)
summary.survbin Provides a summary of the surrogacy measures for an object fitted with the 'survbin()' function.
summary.survcat Provides a summary of the surrogacy measures for an object fitted with the 'survcat()' function.
summary_level_bootstrap_ICA Bootstrap based on the multivariate normal sampling distribution
survbin Compute surrogacy measures for a binary surrogate and a time-to-event true endpoint in the meta-analytic multiple-trial setting.
survcat Compute surrogacy measures for a categorical (ordinal) surrogate and a time-to-event true endpoint in the meta-analytic multiple-trial setting.
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)