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Surrogate (version 1.7)

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

704

Version

1.7

License

GPL (>= 2)

Maintainer

Wim der Elst

Last Published

March 23rd, 2020

Functions in Surrogate (1.7)

Apply the Entropy Concentration Theorem

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)

Bootstrap.MEP.BinBin

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

Data of the Age-Related Macular Degeneration Study

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.

Compute the multiple-surrogate adjusted association

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

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)

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

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

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

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 \(\pi\).

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

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

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

FixedContContIT

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

ICA.BinBin.CounterAssum

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

FixedDiscrDiscrIT

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

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

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

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.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

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

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)

MaxEntSPFBinBin

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

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

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

Restrictions.BinBin

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

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

MaxEntICABinBin

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

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

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

Single.Trial.RE.AA

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

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

MinSurrContCont

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

Pos.Def.Matrices

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

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

MixedContContIT

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

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)

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

The Ovarian dataset

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)

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

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

Generate random vectors with a fixed sum

Sim.Data.CounterfactualsBinBin

Simulate a dataset that contains counterfactuals for binary endpoints

Data of five clinical trials in schizophrenia

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

UnifixedContCont

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

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

Longitudinal PANSS data of five clinical trials in schizophrenia

Sim.Data.Counterfactuals

Simulate a dataset that contains counterfactuals

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

Estimates trial-level surrogacy in the meta-analytic framework

TwoStageSurvSurv

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

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 BinCont

Plots the (Meta-Analytic) Individual Causal Association and related metrics when S is continuous and T is binary

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 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 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

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 MaxEntICA BinBin

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

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

plot TrialLevelIT

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

UnimixedContCont

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

Estimates trial-level surrogacy in the information-theoretic framework

plot TrialLevelMA

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

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

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 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

plot ICA.ContCont.MultS

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

plot TwoStageSurvSurv

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

plot.comb27.BinBin

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

plot SPF BinBin

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

plot Information-Theoretic

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

plot SPF BinCont

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