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Surrogate

In a clinical trial, it frequently occurs that the most credible outcome to evaluate the effectiveness of a new therapy (the true endpoint) is difficult to measure. In such a situation, it can be an effective strategy to replace the true endpoint by a (bio)marker that is easier to measure and that allows for a prediction of the treatment effect on the true endpoint (a surrogate endpoint). The package ‘Surrogate’ allows for an evaluation of the appropriateness of a candidate surrogate endpoint based on the meta-analytic, information-theoretic, and causal-inference frameworks. Part of this software has been developed using funding provided from the European Union’s Seventh Framework Programme for research, technological development and demonstration (Grant Agreement no 602552), the Special Research Fund (BOF) of Hasselt University (BOF-number: BOF2OCPO3), GlaxoSmithKline Biologicals, Baekeland Mandaat (HBC.2022.0145), and Johnson & Johnson Innovative Medicine.

Installation

You can install the development version of Surrogate from GitHub with:

# install.packages("devtools")
devtools::install_github("florianstijven/Surrogate-development")

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Install

install.packages('Surrogate')

Monthly Downloads

1,496

Version

3.3.3

License

GPL (>= 2)

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Repository

https://github.com/florianstijven/Surrogate-development

Maintainer

Wim der Elst

Last Published

February 11th, 2025

Functions in Surrogate (3.3.3)

Compute the multiple-surrogate adjusted association

Bootstrap.MEP.BinBin

Bootstrap 95% CI around the maximum-entropy ICA and SPF (surrogate predictive function)

Data of the Age-Related Macular Degeneration Study

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)

Data of the Age-Related Macular Degeneration Study with multiple candidate surrogates

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

BifixedContCont

Fits a bivariate fixed-effects model 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)

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.

FixedDiscrDiscrIT

Investigates surrogacy for binary or ordinal outcomes using the Information Theoretic framework

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

Dvine_ICA_confint

Confidence interval for the ICA given the unidentifiable parameters

Apply the Entropy Concentration Theorem

FederatedApproachStage1

Fits the first stage model in the two-stage federated data analysis approach.

Evaluate the possibility of finding a good surrogate in the setting where both \(S\) and \(T\) are binary endpoints

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

Assess surrogacy in the causal-inference single-trial setting in the binary-continuous case with an additional bootstrap procedure before the assessment

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

Assess surrogacy in the causal-inference single-trial setting in the binary-binary case

FederatedApproachStage2

Fits the second stage model in the two-stage federated data analysis approach.

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.BinBin.CounterAssum

ICA (binary-binary setting) that is obtaied when the counterfactual correlations are assumed to fall within some prespecified ranges.

Fits (univariate) fixed-effect models to assess surrogacy in the binary-binary case based on the Information-Theoretic framework

Individual-level surrogate threshold effect for continuous normally distributed surrogate and true endpoints.

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)

MetaAnalyticSurvCont

Compute surrogacy measures for a continuous (normally-distributed) surrogate and a time-to-event true endpoint in the meta-analytic multiple-trial setting.

Reshapes a dataset from the 'long' format (i.e., multiple lines per patient) into the 'wide' format (i.e., one line per patient)

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 \(\pi\).

MetaAnalyticSurvSurv

Compute surrogacy measures for a time-to-event surrogate and a time-to-event true endpoint in the meta-analytic multiple-trial setting.

Evaluates surrogacy based on the Prentice criteria for continuous endpoints (single-trial setting)

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

ICA_given_model_constructor_SurvSurv

Constructor for the function that returns that ICA as a function of the identifiable parameters for survival-survival

Assess surrogacy in the causal-inference single-trial setting in the binary-continuous case

Assess surrogacy in the causal-inference single-trial setting (Individual Causal Association, ICA) in the Continuous-continuous case

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

Use the maximum-entropy approach to compute ICA in the continuous-continuous sinlge-trial setting

MetaAnalyticSurvBin

Compute surrogacy measures for a binary surrogate and a time-to-event true endpoint in the meta-analytic multiple-trial setting.

MaxEntSPFBinBin

Use the maximum-entropy approach to compute SPF (surrogate predictive function) in the binary-binary setting

Generate random vectors with a fixed sum

MetaAnalyticSurvCat

Compute surrogacy measures for a categorical (ordinal) surrogate and a time-to-event true endpoint in the meta-analytic multiple-trial setting.

Computes marginal probabilities for a dataset where the surrogate and true endpoints are binary

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

plot FixedDiscrDiscrIT

Provides plots of trial-level surrogacy in the Information-Theoretic framework

Pos.Def.Matrices

Generate 4 by 4 correlation matrices and flag the positive definite ones

MaxEntICABinBin

Use the maximum-entropy approach to compute ICA in the binary-binary setting

MinSurrContCont

Examine the plausibility of finding a good surrogate endpoint in the Continuous-continuous case

Assess surrogacy in the causal-inference multiple-trial setting (Meta-analytic Individual Causal Association; MICA) 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

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

fit_copula_OrdCont

Fit ordinal-continuous vine copula model

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

fit_copula_OrdOrd

Fit ordinal-ordinal vine copula 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)

loglik_copula_scale

Loglikelihood on the Copula Scale

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)

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

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)

MumixedContCont.MultS

Fits a multivariate mixed-effects model to assess surrogacy in the meta-analytic multiple-trial setting (Continuous-continuous case with multiple surrogates)

Sim.Data.CounterfactualsBinBin

Simulate a dataset that contains counterfactuals for binary endpoints

Simulates a dataset that can be used to assess surrogacy in the multiple-trial setting

The Ovarian dataset

Restrictions.BinBin

Examine restrictions in \(\bold{\pi}_{f}\) under different montonicity assumptions for binary \(S\) and \(T\)

marginal_distribution

Fit marginal distribution

TwoStageSurvSurv

Assess trial-level surrogacy for two survival endpoints using a two-stage approach

PANSS subscales and total score based on the data of five clinical trials in schizophrenia

UnifixedContCont

Fits univariate fixed-effect models to assess surrogacy in the meta-analytic multiple-trial setting (continuous-continuous case)

Evaluate the surrogate predictive function (SPF) in the binary-binary setting (sensitivity-analysis based approach)

ordinal_ordinal_loglik

Loglikelihood function for ordinal-ordinal copula model

Longitudinal PANSS data of five clinical trials in schizophrenia

Compute Individual Causal Association for a given D-vine copula model in the setting of choice.

ordinal_to_cutpoints

Convert Ordinal Observations to Latent Cutpoints

Data of five clinical trials in schizophrenia

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

Sim.Data.Counterfactuals

Simulate a dataset that contains counterfactuals

Evaluate the surrogate predictive function (SPF) in the causal-inference single-trial setting in the binary-continuous case

compute_ICA_BinCont

Compute Individual Causal Association for a given D-vine copula model in the Binary-Continuous Setting

plot MaxEntICA BinBin

Plots the sensitivity-based and maximum entropy based Individual Causal Association when S and T are binary outcomes

Simulates a dataset that can be used to assess surrogacy in the single-trial setting

estimate_ICA_OrdOrd

Estimate ICA in Ordinal-Ordinal 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

Estimates trial-level surrogacy in the information-theoretic framework

Estimates trial-level surrogacy in the meta-analytic framework

The Colorectal dataset with an ordinal surrogate.

Assesses the surrogate predictive value of each of the 27 prediction functions in the setting where both \(S\) and \(T\) are binary endpoints

estimate_marginal

Estimate marginal distribution using ML

Data of a clinical trial in Schizophrenia (with binary outcomes).

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.

Assess surrogacy for two survival endpoints based on information theory and a two-stage approach

Test whether the data are compatible with monotonicity for S and/or T (binary endpoints)

binary_continuous_loglik

Loglikelihood function for binary-continuous copula model

estimate_ICA_ContCont

Estimate ICA in Ordinal-Ordinal Setting

estimate_ICA_OrdCont

Estimate ICA in Ordinal-Continuous Setting

gumbel_loglik_copula_scale

Loglikelihood on the Copula Scale for the Gumbel Copula

fit_copula_model_BinCont

Fit copula model for binary true endpoint and continuous surrogate endpoint

UnimixedContCont

Fits univariate mixed-effect models to assess surrogacy in the meta-analytic multiple-trial setting (continuous-continuous case)

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.

Data of a clinical trial in schizophrenia, with binary and continuous endpoints

fit_copula_submodel_BinCont

Fit binary-continuous copula submodel

marginal_gof_copula

Produce marginal GoF plot

Function factory for distribution functions

marginal_gof_plots_scr

Marginal survival function goodness of fit

print.MetaAnalyticSurvCat

Prints all the elements of an object fitted with the 'MetaAnalyticSurvCat()' function.

log_likelihood_copula_model

Computes loglikelihood for a given copula model

Single.Trial.RE.AA

Conducts a surrogacy analysis based on the single-trial meta-analytic framework

print.MetaAnalyticSurvCont

Prints all the elements of an object fitted with the 'MetaAnalyticSurvCont()' function.

Surrogate-package

Surrogate: Evaluation of Surrogate Endpoints in Clinical Trials

delta_method_log_mutinfo

Variance of log-mutual information based on the delta method

association_gof_copula

Produce Associational GoF plot

compute_ICA_OrdOrd

Compute Individual Causal Association for a given D-vine copula model in the Ordinal-Ordinal Setting

The prostate dataset with a continuous surrogate.

estimate_ICA_BinCont

Estimate ICA in Binary-Continuous Setting

sample_copula_parameters

Sample Unidentifiable Copula Parameters

fit_copula_submodel_ContCont

Fit ordinal-continuous copula submodel

compute_ICA_SurvSurv

Compute Individual Causal Association for a given D-vine copula model in the Survival-Survival Setting

fit_copula_submodel_OrdCont

Fit ordinal-continuous copula submodel

clayton_loglik_copula_scale

Loglikelihood on the Copula Scale for the Clayton Copula

model_fit_measures

Goodness of fit information for survival-survival model

frank_loglik_copula_scale

Loglikelihood on the Copula Scale for the Frank Copula

The Colorectal dataset with a binary surrogate.

marginal_gof_scr_S_plot

Goodness-of-fit plot for the marginal survival functions

mean_S_before_T_plot_scr

Goodness of fit plot for the fitted copula

twostep_SurvSurv

Fit survival-survival copula submodel with two-step estimator

constructor_ICA_estimator

Function constructor to estimate the ICA given a set of sampled patient-level treatment effects

estimate_mutual_information_SurvSurv

Estimate the Mutual Information in the Survival-Survival Setting

gaussian_loglik_copula_scale

Loglikelihood on the Copula Scale for the Gaussian Copula

new_vine_copula_fit

Constructor for vine copula model

continuous_continuous_loglik

Loglikelihood function for continuous-continuous copula model

fit_copula_ContCont

Fit continuous-continuous vine copula model

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.

new_vine_copula_ss_fit

Constructor for vine copula model

fit_copula_submodel_OrdOrd

Fit ordinal-continuous copula submodel

ordinal_continuous_loglik

Loglikelihood function for ordinal-continuous copula model

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 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)

fit_model_SurvSurv

Fit Survival-Survival model

plot.vine_copula_fit

Goodness-of-fit plots for the fitted copula models

plot ICA.ContCont.MultS

Plots the Individual Causal Association in the setting where there are multiple continuous S and a continuous T

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

print.MetaAnalyticSurvBin

Prints all the elements of an object fitted with the 'MetaAnalyticSurvBin()' function.

Provides plots of trial- and individual-level surrogacy in the Information-Theoretic framework when both S and T are time-to-event endpoints

plot.SPF.BinCont

Plot the surrogate predictive function (SPF) in the causal-inference single-trial setting in the binary-continuous case.

plot SPF BinBin

Plots the surrogate predictive function (SPF) in the binary-binary settinf.

sensitivity_analysis_BinCont_copula

Perform Sensitivity Analysis for the Individual Causal Association with a Continuous Surrogate and Binary True Endpoint

plot ISTE.ContCont

Plots the individual-level surrogate threshold effect (STE) values and related metrics

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

sensitivity_analysis_SurvSurv_copula

Sensitivity analysis for individual causal association

plot.ICA.BinCont

Plot the individual causal association (ICA) in the causal-inference single-trial setting in the binary-continuous case.

plot MaxEntSPF BinBin

Plots the sensitivity-based and maximum entropy based surrogate predictive function (SPF) when S and T are binary outcomes.

sample_deltas_BinCont

Sample individual casual treatment effects from given D-vine copula model in binary continuous setting

print.MetaAnalyticSurvSurv

Prints all the elements of an object fitted with the 'MetaAnalyticSurvSurv()' function.

print.vine_copula_fit

Print summary of fitted copula model

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.

Sample copula data from a given four-dimensional D-vine copula

plot Information-Theoretic

Provides plots of trial- and individual-level surrogacy in the Information-Theoretic framework

plot MinSurrContCont

Graphically illustrates the theoretical plausibility of finding a good surrogate endpoint in the continuous-continuous case

sensitivity_analysis_copula

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

sensitivity_intervals_Dvine

Compute Sensitivity Intervals

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 Meta-Analytic

Provides plots of trial- and individual-level surrogacy in the meta-analytic framework

summary.MetaAnalyticSurvSurv

Provides a summary of the surrogacy measures for an object fitted with the 'MetaAnalyticSurvSurv()' function.

plot TwoStageSurvSurv

Plots trial-level surrogacy in the meta-analytic framework when two survival endpoints are considered.

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.comb27.BinBin

Plots the distribution of prediction error functions in decreasing order of appearance.

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_level_bootstrap_ICA

Bootstrap based on the multivariate normal sampling distribution

twostep_BinCont

Fit binary-continuous copula submodel with two-step estimator