Compute the multiple-surrogate adjusted association
Data of the Age-Related Macular Degeneration Study
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)
Bootstrap 95% CI around the maximum-entropy ICA and SPF (surrogate predictive function)
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.
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 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)
Fits (univariate) fixed-effect models to assess surrogacy in the binary-binary case 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 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 \(\pi\).
Apply the Entropy Concentration Theorem
Confidence interval for the ICA given the unidentifiable parameters
Evaluate the possibility of finding a good surrogate in the setting where both \(S\) and \(T\) are binary endpoints
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 (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
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)
Assess surrogacy in the causal-inference single-trial setting in the binary-continuous case
Fits (univariate) fixed-effect models to assess surrogacy in the continuous-continuous case based on the Information-Theoretic framework
Assess surrogacy in the causal-inference single-trial setting in the binary-binary case
ICA (binary-binary setting) that is obtaied when the counterfactual correlations are assumed to fall within some prespecified ranges.
Assess surrogacy in the causal-inference single-trial setting in the binary-continuous case with an additional bootstrap procedure before the assessment
Assess surrogacy in the causal-inference single-trial setting (Individual Causal Association, ICA) in the Continuous-continuous case
Data of the Age-Related Macular Degeneration Study with multiple candidate surrogates
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 a modified algorithm based on partial correlations
Use the maximum-entropy approach to compute ICA in the binary-binary setting
Use the maximum-entropy approach to compute ICA in the continuous-continuous sinlge-trial setting
Use the maximum-entropy approach to compute SPF (surrogate predictive function) in the binary-binary setting
Investigates surrogacy for binary or ordinal outcomes using the Information Theoretic framework
Reshapes a dataset from the 'long' format (i.e., multiple lines per patient) into the 'wide' format (i.e., one line per patient)
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 in the causal-inference multiple-trial setting (Meta-analytic Individual Causal Association; MICA) in the continuous-continuous case
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
Examine the plausibility of finding a good surrogate endpoint in the Continuous-continuous case
Fits (univariate) mixed-effect models to assess surrogacy in the continuous-continuous case based on the Information-Theoretic framework
PANSS subscales and total score based on the data of five clinical trials in schizophrenia
Computes marginal probabilities for a dataset where the surrogate and true endpoints are binary
Fits a multivariate fixed-effects model to assess surrogacy in the meta-analytic multiple-trial setting (Continuous-continuous case with multiple surrogates)
ICA_given_model_constructor
Constructor for the function that returns that ICA as a function of the
identifiable parameters
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
Individual-level surrogate threshold effect for continuous normally distributed surrogate and true endpoints.
Generate random vectors with a fixed sum
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 restrictions in \(\bold{\pi}_{f}\) under different montonicity assumptions for binary \(S\) and \(T\)
Fits a multivariate mixed-effects model to assess surrogacy in the meta-analytic multiple-trial setting (Continuous-continuous case with multiple surrogates)
Provides plots of trial-level surrogacy in the Information-Theoretic framework
The Ovarian dataset
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
Assess surrogacy in the causal-inference single-trial setting (Individual Causal Association, ICA) using a continuous univariate T and multiple continuous S
Data of five clinical trials in schizophrenia
Data of a clinical trial in Schizophrenia (with binary outcomes).
Function factory for distribution functions
Simulates a dataset that can be used to assess surrogacy in the multiple-trial setting
Data of a clinical trial in schizophrenia, with binary and continuous endpoints
clayton_loglik_copula_scale
Loglikelihood on the Copula Scale for the Clayton Copula
Longitudinal PANSS data of five clinical trials in schizophrenia
Compute the expected treatment effect on the true endpoint in a new trial (when both S and T are normally distributed continuous endpoints)
Evaluates surrogacy based on the Prentice criteria for continuous endpoints (single-trial setting)
Compute Individual Causal Association for a given D-vine copula model in the
Survival-Survival Setting
Simulates a dataset that can be used to assess surrogacy in the single-trial setting
The Colorectal dataset with a binary surrogate.
Variance of log-mutual information based on the delta method
Provides a plots of trial-level surrogacy in the information-theoretic framework based on the output of the TrialLevelIT() function
The Colorectal dataset with an ordinal surrogate.
Plots the expected treatment effect on the true endpoint in a new trial (when both S and T are normally distributed continuous endpoints)
Estimates trial-level surrogacy in the information-theoretic framework
Estimates trial-level surrogacy in the meta-analytic framework
Generates a plot of the estimated treatment effects for the surrogate endpoint versus the estimated treatment effects for the true endpoint for an object fitted with the 'survcat()' function.
Fit binary-continuous copula submodel with two-step estimator
Fit survival-survival copula submodel with two-step estimator
Generates a plot of the estimated treatment effects for the surrogate endpoint versus the estimated treatment effects for the true endpoint for an object fitted with the 'survbin()' function.
Provides a plots of trial-level surrogacy in the meta-analytic framework based on the output of the TrialLevelMA() function
Fit Survival-Survival model
log_likelihood_copula_model
Computes loglikelihood for a given copula model
Assess surrogacy for two survival endpoints based on information theory and a two-stage approach
Loglikelihood on the Copula Scale
Test whether the data are compatible with monotonicity for S and/or T (binary endpoints)
Compute Individual Causal Association for a given D-vine copula model in the
Binary-Continuous Setting
Simulates a dataset that can be used to assess surrogacy in the single trial setting when S and T are binary endpoints
Assesses the surrogate predictive value of each of the 27 prediction functions in the setting where both \(S\) and \(T\) are binary endpoints
gumbel_loglik_copula_scale
Loglikelihood on the Copula Scale for the Gumbel Copula
gaussian_loglik_copula_scale
Loglikelihood on the Copula Scale for the Gaussian Copula
Plots the individual-level surrogate threshold effect (STE) values and related metrics
Generate 4 by 4 correlation matrices and flag the positive definite ones
Evaluate the surrogate predictive function (SPF) in the binary-binary setting (sensitivity-analysis based approach)
frank_loglik_copula_scale
Loglikelihood on the Copula Scale for the Frank Copula
Constructor for vine copula model
Fit marginal distribution
Goodness of fit information for survival-survival model
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)
Provides plots of trial- and individual-level surrogacy in the Information-Theoretic framework when both S and T are time-to-event endpoints
Marginal survival function goodness of fit
Sample copula data from a given four-dimensional D-vine copula
Graphically illustrates the theoretical plausibility of finding a good surrogate endpoint in the continuous-continuous case
sensitivity_analysis_BinCont_copula
Perform Sensitivity Analysis for the Individual Causal Association with a
Continuous Surrogate and Binary True Endpoint
Provides a summary of the surrogacy measures for an object fitted with the 'survcat()' function.
summary_level_bootstrap_ICA
Bootstrap based on the multivariate normal sampling distribution
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 Information-Theoretic
Provides plots of trial- and individual-level surrogacy in the Information-Theoretic framework
Assess trial-level surrogacy for two survival endpoints using a two-stage approach
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
Conducts a surrogacy analysis based on the single-trial meta-analytic framework
Evaluate the surrogate predictive function (SPF) in the binary-continuous setting (sensitivity-analysis based approach)
Plots trial-level surrogacy in the meta-analytic framework when two survival endpoints are considered.
Fits univariate fixed-effect models to assess surrogacy in the meta-analytic multiple-trial setting (continuous-continuous case)
Estimate ICA in Binary-Continuous Setting
estimate_mutual_information_SurvSurv
Estimate the Mutual Information in the Survival-Survival Setting
sensitivity_analysis_SurvSurv_copula
Sensitivity analysis for individual causal association
Simulate a dataset that contains counterfactuals
Goodness-of-fit plot for the marginal survival functions
sensitivity_intervals_Dvine
Compute Sensitivity Intervals
Sim.Data.CounterfactualsBinBin
Simulate a dataset that contains counterfactuals for binary endpoints
Goodness of fit plot for the fitted copula
Fits univariate mixed-effect models to assess surrogacy in the meta-analytic multiple-trial setting
(continuous-continuous case)
Loglikelihood function for binary-continuous copula model
Plots the distribution of prediction error functions in decreasing order of appearance.
Fit copula model for binary true endpoint and continuous surrogate endpoint
fit_copula_submodel_BinCont
Fit binary-continuous copula submodel
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
Plots the sensitivity-based and maximum entropy based surrogate predictive function (SPF) when S and T are binary outcomes.
Function factory for density functions
plot Causal-Inference ContCont
Plots the (Meta-Analytic) Individual Causal Association when S and T are continuous outcomes
Sample individual casual treatment effects from given D-vine copula model in
binary continuous setting
Compute surrogacy measures for a categorical (ordinal) surrogate and a time-to-event true endpoint in the meta-analytic multiple-trial setting.
Sample Unidentifiable Copula Parameters
Provides plots of trial- and individual-level surrogacy in the meta-analytic framework
Compute surrogacy measures for a binary surrogate and a time-to-event true endpoint in the meta-analytic multiple-trial setting.
Plots the sensitivity-based and maximum entropy based Individual Causal Association when S and T are continuous outcomes in the single-trial setting
Plots the sensitivity-based and maximum entropy based Individual Causal Association when S and T are binary outcomes
Plots the surrogate predictive function (SPF) in the binary-binary settinf.
Plots the surrogate predictive function (SPF) in the binary-continuous setting.
Prints all the elements of an object fitted with the 'survcat()' function.
Prints all the elements of an object fitted with the 'survbin()' function.
Summary
Provides a summary of the surrogacy measures for an object fitted with the 'survbin()' function.