Evaluation of Surrogate Endpoints in Clinical Trials

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Documentation for package ‘Surrogate’ version 0.5

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A B C E F I L M O P R S T U

-- A --

AA.MultS Compute the multiple-surrogate adjusted association
ARMD Data of the Age-Related Macular Degeneration Study
ARMD.MultS Data of the Age-Related Macular Degeneration Study with multiple candidate surrogates

-- B --

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)

-- C --

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

-- E --

ECT Apply the Entropy Concentration Theorem

-- F --

Fano.BinBin Evaluate the possibility of finding a good surrogate in the setting where both S and T are binary endpoints
FixedBinBinIT Fits (univariate) fixed-effect models to assess surrogacy in the binary-binary case based on the Information-Theoretic framework
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)
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)
FixedContContIT Fits (univariate) fixed-effect models to assess surrogacy in the continuous-continuous case based on the Information-Theoretic framework
FixedDiscrDiscrIT Investigates surrogacy for binary or ordinal outcomes using the Information Theoretic framework

-- I --

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

-- L --

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

-- M --

MarginalProbs Computes marginal probabilities for a dataset where the surrogate and true endpoints are binary
MaxEntContCont 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
MaxEntSPFBinBin Use the maximum-entropy approach to compute SPF (surrogate predictive function) in the binary-binary setting
MICA.ContCont Assess surrogacy in the causal-inference multiple-trial setting (Meta-analytic Individual Causal Association; MICA) in the continuous-continuous 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
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

-- O --

Ovarian The Ovarian dataset

-- P --

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 Causal-Inference ContCont Plots the (Meta-Analytic) Individual Causal Association when S and T are continuous outcomes
plot FixedDiscrDiscrIT Provides plots of trial-level surrogacy in the Information-Theoretic framework
plot Information-Theoretic Provides plots of trial- and individual-level surrogacy in the Information-Theoretic framework
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 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 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 MinSurrContCont Graphically illustrates the theoretical plausibility of finding a good surrogate endpoint in the continuous-continuous case
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 SPF BinBin Plots the surrogate predictive function (SPF).
plot.BifixedContCont Provides plots of trial- and individual-level surrogacy in the meta-analytic framework
plot.BimixedContCont Provides plots of trial- and individual-level surrogacy in the meta-analytic framework
plot.comb27.BinBin Plots the distribution of prediction error functions in decreasing order of appearance.
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.FixedBinBinIT 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.FixedBinContIT 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.FixedContBinIT 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.FixedContContIT Provides plots of trial- and individual-level surrogacy in the Information-Theoretic framework
plot.FixedDiscrDiscrIT Provides plots of trial-level surrogacy in the Information-Theoretic framework
plot.ICA.BinBin Plots the (Meta-Analytic) Individual Causal Association and related metrics when S and T are binary outcomes
plot.ICA.BinCont Plots the (Meta-Analytic) Individual Causal Association and related metrics when S is continuous and T is binary
plot.ICA.ContCont Plots the (Meta-Analytic) Individual Causal Association when S and T are continuous outcomes
plot.ICA.ContCont.MultS Plots the Individual Causal Association in the setting where there are multiple continuous S and a continuous T
plot.ICA.ContCont.MultS_alt Plots the Individual Causal Association in the setting where there are multiple continuous S and a continuous T
plot.MaxEntContCont 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.MaxEntSPF.BinBin Plots the sensitivity-based and maximum entropy based surrogate predictive function (SPF) when S and T are binary outcomes.
plot.MICA.ContCont Plots the (Meta-Analytic) Individual Causal Association when S and T are continuous outcomes
plot.MinSurrContCont Graphically illustrates the theoretical plausibility of finding a good surrogate endpoint in the continuous-continuous case
plot.MixedContContIT Provides plots of trial- and individual-level surrogacy in the Information-Theoretic framework
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.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.Single.Trial.RE.AA Conducts a surrogacy analysis based on the single-trial meta-analytic framework
plot.SPF.BinBin Plots the surrogate predictive function (SPF).
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.TrialLevelIT Provides a plots of trial-level surrogacy in the information-theoretic framework based on the output of the 'TrialLevelIT()' function
plot.TrialLevelMA Provides a plots of trial-level surrogacy in the meta-analytic framework based on the output of the 'TrialLevelMA()' function
plot.TwoStageSurvSurv Plots trial-level surrogacy in the meta-analytic framework when two survival endpoints are considered.
plot.UnifixedContCont Provides plots of trial- and individual-level surrogacy in the meta-analytic framework
plot.UnimixedContCont Provides plots of trial- and individual-level surrogacy in the meta-analytic framework
Pos.Def.Matrices Generate 4 by 4 correlation matrices and flag the positive definite ones
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
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)
Prentice Evaluates surrogacy based on the Prentice criteria for continuous endpoints (single-trial setting)

-- R --

RandVec Generate random vectors with a fixed sum
Restrictions.BinBin Examine restrictions in pi_{f} under different montonicity assumptions for binary S and T

-- S --

Schizo Data of five clinical trials in schizophrenia
Schizo_Bin Data of a clinical trial in Schizophrenia (with binary outcomes).
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.CounterfactualsBinBin Simulate a dataset that contains counterfactuals for binary endpoints
Sim.Data.MTS Simulates a dataset that can be used to assess surrogacy in the multiple-trial setting
Sim.Data.STS Simulates a dataset that can be used to assess surrogacy in the single-trial 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
Single.Trial.RE.AA Conducts a surrogacy analysis based on the single-trial meta-analytic framework
SPF.BinBin Evaluate the surrogate predictive function (SPF) in the binary-binary setting (sensitivity-analysis based approach)
SurvSurv Assess surrogacy for two survival endpoints based on information theory and a two-stage approach

-- T --

Test.Mono Test whether the data are compatible with monotonicity for S and/or T (binary endpoints)
TrialLevelIT Estimates trial-level surrogacy in the information-theoretic framework
TrialLevelMA Estimates trial-level surrogacy in the meta-analytic framework
TwoStageSurvSurv Assess trial-level surrogacy for two survival endpoints using a two-stage approach

-- U --

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)