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Surrogate (version 1.1)

Evaluation of Surrogate Endpoints in Clinical Trials

Description

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 under Grant Agreement no 602552.

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Version

Install

install.packages('Surrogate')

Monthly Downloads

1,279

Version

1.1

License

GPL (>= 2)

Maintainer

Wim der Elst

Last Published

October 18th, 2018

Functions in Surrogate (1.1)

Data of the Collaborative Initial Glaucoma Treatment Study

BimixedContCont

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

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)

Bootstrap.MEP.BinBin

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

Data of the Age-Related Macular Degeneration Study

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

BifixedContCont

Fits a bivariate fixed-effects model to assess surrogacy in the meta-analytic multiple-trial setting (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

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

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

Apply the Entropy Concentration Theorem

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 case where the true endpoint is binary and the surrogate endpoint is continuous (based on the Information-Theoretic framework)

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

Compute the multiple-surrogate adjusted association

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

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

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

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

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

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)

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

Generate random vectors with a fixed sum

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

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

MixedContContIT

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

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

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.

FixedContContIT

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

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

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

Estimates trial-level surrogacy in the information-theoretic framework

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 \(\pi\).

Restrictions.BinBin

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

MaxEntSPFBinBin

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

plot Meta-Analytic

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

The Ovarian dataset

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

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

Data of five clinical trials in schizophrenia

MinSurrContCont

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

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

Longitudinal PANSS data of five clinical trials in schizophrenia

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

Sim.Data.Counterfactuals

Simulate a dataset that contains counterfactuals

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

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

Single.Trial.RE.AA

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

plot TrialLevelMA

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

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

plot MinSurrContCont

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

plot TwoStageSurvSurv

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

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

Pos.Def.Matrices

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

plot FixedDiscrDiscrIT

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

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.comb27.BinBin

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

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)

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

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

Sim.Data.CounterfactualsBinBin

Simulate a dataset that contains counterfactuals for binary endpoints

plot ICA.ContCont.MultS

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

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

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)

plot Information-Theoretic

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

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

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

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

plot Causal-Inference ContCont

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

plot MaxEntICA BinBin

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

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)

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

plot MaxEntSPF BinBin

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

plot SPF BinCont

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

plot TrialLevelIT

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

Estimates trial-level surrogacy in the meta-analytic framework

TwoStageSurvSurv

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

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

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 SPF BinBin

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

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

Data of the Collaborative Initial Glaucoma Treatment Study which contains binary and continuous endpoints