Apply the Entropy Concentration Theorem
Fits a bivariate fixed-effects model to assess surrogacy in the meta-analytic multiple-trial setting (Continuous-continuous case)
Fits a bivariate mixed-effects model to assess surrogacy in the meta-analytic multiple-trial setting
(Continuous-continuous case)
Bootstrap 95% CI around the maximum-entropy ICA and SPF (surrogate predictive function)
Data of the Age-Related Macular Degeneration Study
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
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
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)
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.
Fits (univariate) fixed-effect models to assess surrogacy in the continuous-continuous case based on the Information-Theoretic framework
ICA (binary-binary setting) that is obtaied when the counterfactual correlations are assumed to fall within some prespecified ranges.
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)
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 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)
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)
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
Use the maximum-entropy approach to compute ICA in the binary-binary setting
Assess surrogacy in the causal-inference single-trial setting (Individual Causal Association, ICA) using a continuous univariate T and multiple continuous S
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
Conducts a surrogacy analysis based on the single-trial meta-analytic framework
Assess surrogacy in the causal-inference single-trial setting (Individual Causal Association, ICA) using a continuous univariate T and multiple continuous S, alternative approach
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
Examine the plausibility of finding a good surrogate endpoint in the Continuous-continuous case
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
Fits (univariate) mixed-effect models to assess surrogacy in the continuous-continuous case based on the Information-Theoretic framework
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
Provides plots of trial-level surrogacy in the Information-Theoretic framework
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)
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
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
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
Simulates a dataset that can be used to assess surrogacy in the single trial setting when S and T are binary endpoints
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)
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)
Provides a plots of trial-level surrogacy in the information-theoretic framework based on the output of the TrialLevelIT() function
Summary
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
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
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
Graphically illustrates the theoretical plausibility of finding a good surrogate endpoint in the continuous-continuous case
Plots the sensitivity-based and maximum entropy based surrogate predictive function (SPF) when S and T are binary outcomes.
Provides plots of trial- and individual-level surrogacy in the meta-analytic framework
Plots the individual-level surrogate threshold effect (STE) values and related metrics
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
Plots the Individual Causal Association in the setting where there are multiple continuous S and a continuous T
Plots trial-level surrogacy in the meta-analytic framework when two survival endpoints are considered.
Plots the distribution of prediction error functions in decreasing order of appearance.
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
Plots the surrogate predictive function (SPF) in the binary-continuous setting.