Learn R Programming

⚠️There's a newer version (3.4.1) of this package.Take me there.

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")

Copy Link

Version

Install

install.packages('Surrogate')

Monthly Downloads

845

Version

3.2.5

License

GPL (>= 2)

Issues

Pull Requests

Stars

Forks

Repository

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

Maintainer

Wim der Elst

Last Published

March 19th, 2024

Functions in Surrogate (3.2.5)

Compute the multiple-surrogate adjusted association

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)

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

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)

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

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 \(\pi\).

Apply the Entropy Concentration Theorem

Dvine_ICA_confint

Confidence interval for the ICA given the unidentifiable parameters

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

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

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)

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

FixedContContIT

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

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.

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

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

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

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

MaxEntICABinBin

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

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

MaxEntSPFBinBin

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

FixedDiscrDiscrIT

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

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

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

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

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

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

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

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

MufixedContCont.MultS

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

ICA_given_model_constructor

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

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

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

Generate random vectors with a fixed sum

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

Restrictions.BinBin

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

MumixedContCont.MultS

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

plot FixedDiscrDiscrIT

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

The Ovarian dataset

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

Assess surrogacy in the causal-inference single-trial setting (Individual Causal Association, ICA) using a continuous univariate T and multiple continuous S

Data of five clinical trials in schizophrenia

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

Function factory for distribution functions

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

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

clayton_loglik_copula_scale

Loglikelihood on the Copula Scale for the Clayton Copula

Longitudinal PANSS data of five clinical trials in schizophrenia

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)

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

compute_ICA_SurvSurv

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

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

The Colorectal dataset with a binary surrogate.

delta_method_log_mutinfo

Variance of log-mutual information based on the delta method

plot TrialLevelIT

Provides a plots of trial-level surrogacy in the information-theoretic framework based on the output of the TrialLevelIT() function

The Colorectal dataset with an ordinal surrogate.

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)

Estimates trial-level surrogacy in the information-theoretic framework

Estimates trial-level surrogacy in the meta-analytic framework

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.

twostep_BinCont

Fit binary-continuous copula submodel with two-step estimator

twostep_SurvSurv

Fit survival-survival copula submodel with two-step estimator

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 TrialLevelMA

Provides a plots of trial-level surrogacy in the meta-analytic framework based on the output of the TrialLevelMA() function

fit_model_SurvSurv

Fit Survival-Survival model

log_likelihood_copula_model

Computes loglikelihood for a given copula model

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

loglik_copula_scale

Loglikelihood on the Copula Scale

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

compute_ICA_BinCont

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

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

gumbel_loglik_copula_scale

Loglikelihood on the Copula Scale for the Gumbel Copula

gaussian_loglik_copula_scale

Loglikelihood on the Copula Scale for the Gaussian Copula

plot ISTE.ContCont

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

Pos.Def.Matrices

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

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

frank_loglik_copula_scale

Loglikelihood on the Copula Scale for the Frank Copula

new_vine_copula_ss_fit

Constructor for vine copula model

marginal_distribution

Fit marginal distribution

model_fit_measures

Goodness of fit information for survival-survival model

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)

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

marginal_gof_plots_scr

Marginal survival function goodness of fit

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

plot MinSurrContCont

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

sensitivity_analysis_BinCont_copula

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

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

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 Information-Theoretic

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

TwoStageSurvSurv

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

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 ICA.ContCont.MultS

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

Single.Trial.RE.AA

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

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

plot TwoStageSurvSurv

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

UnifixedContCont

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

estimate_ICA_BinCont

Estimate ICA in Binary-Continuous Setting

estimate_mutual_information_SurvSurv

Estimate the Mutual Information in the Survival-Survival Setting

sensitivity_analysis_SurvSurv_copula

Sensitivity analysis for individual causal association

Sim.Data.Counterfactuals

Simulate a dataset that contains counterfactuals

marginal_gof_scr_S_plot

Goodness-of-fit plot for the marginal survival functions

sensitivity_intervals_Dvine

Compute Sensitivity Intervals

Sim.Data.CounterfactualsBinBin

Simulate a dataset that contains counterfactuals for binary endpoints

mean_S_before_T_plot_scr

Goodness of fit plot for the fitted copula

UnimixedContCont

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

binary_continuous_loglik

Loglikelihood function for binary-continuous copula model

plot.comb27.BinBin

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

fit_copula_model_BinCont

Fit copula model for binary true endpoint and continuous surrogate endpoint

fit_copula_submodel_BinCont

Fit binary-continuous copula submodel

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 MaxEntSPF BinBin

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

Function factory for density functions

plot Causal-Inference ContCont

Plots the (Meta-Analytic) Individual Causal Association when S and T are continuous outcomes

sample_deltas_BinCont

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

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

sample_copula_parameters

Sample Unidentifiable Copula Parameters

plot Meta-Analytic

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

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

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 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.

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

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

summary.survbin

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