# Surrogate v1.7

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## Evaluation of Surrogate Endpoints in Clinical Trials

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.

## Functions in Surrogate

Name | Description | |

ECT | 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) | |

ARMD | 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. | |

AA.MultS | 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 | |

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

ARMD.MultS | 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 \(\pi\). | |

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

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

ISTE.ContCont | 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 | |

ICA.BinCont | Assess surrogacy in the causal-inference single-trial setting in the binary-continuous case | |

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

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) | |

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 | |

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

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) | |

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

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

SPF.BinBin | 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 \(T\) | |

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 | |

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 | |

SurvSurv | 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 | |

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 | |

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 | |

MaxEntContCont | 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) | |

ICA.BinBin | Assess surrogacy in the causal-inference single-trial setting in the binary-binary case | |

Ovarian | 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) | |

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

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

RandVec | Generate random vectors with a fixed sum | |

Sim.Data.CounterfactualsBinBin | Simulate a dataset that contains counterfactuals for binary endpoints | |

Schizo | Data of five clinical trials in schizophrenia | |

Prentice | 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) | |

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 | |

Sim.Data.Counterfactuals | Simulate a dataset that contains counterfactuals | |

Sim.Data.STS | Simulates a dataset that can be used to assess surrogacy in the single-trial setting | |

TrialLevelMA | Estimates trial-level surrogacy in the meta-analytic framework | |

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

Sim.Data.MTS | Simulates a dataset that can be used to assess surrogacy in the multiple-trial setting | |

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 | |

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.Mono | 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 | |

summary | Summary | |

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

TrialLevelIT | 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 | |

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.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. | |

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## Details

Type | Package |

License | GPL (>= 2) |

Repository | CRAN |

NeedsCompilation | no |

Packaged | 2020-03-22 12:49:34 UTC; wim |

Date/Publication | 2020-03-23 01:10:03 UTC |

imports | extraDistr , ks , lattice , latticeExtra , lme4 , logistf , MASS , mixtools , msm , nlme , OrdinalLogisticBiplot , parallel , rgl , rms , rootSolve , survival |

Contributors | Wim der Elst, Paul Meyvisch, Ariel Alonso, Hannah Ensor, Christopher Weir, Geert Molenberghs, Alvaro Poveda |

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