Evaluation of Surrogate Endpoints in Clinical Trials

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Documentation for package ‘Surrogate’ version 3.2.1

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A B C 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
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

-- 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
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_scr Marginal survival function goodness of fit
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
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

-- N --

new_vine_copula_ss_fit Constructor for vine copula model

-- O --

Ovarian The Ovarian dataset

-- P --

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.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)
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
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)
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)