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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| 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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