FixedContContIT
(Package: Surrogate) :
Fits (univariate) fixed-effect models to assess surrogacy in the continuous-continuous case based on the Information-Theoretic framework
The function FixedContContIT uses the information-theoretic approach (Alonso & Molenberghs, 2007) to estimate trial- and individual-level surrogacy based on fixed-effect models when both S and T are continuous variables. The user can specify whether a (weighted or unweighted) full, semi-reduced, or reduced model should be fitted. See the Details section below.
TrialLevelMA
(Package: Surrogate) :
Estimates trial-level surrogacy in the meta-analytic framework
The function TrialLevelMA estimates trial-level surrogacy based on the vectors of treatment effects on S (i.e., α_{i}) and T (i.e., β_{i}) in the different trials. In particular, β_{i} is regressed on α_{i} and the classical coefficient of determination of the fitted model provides an estimate of R^2_{trial}. In addition, the standard error and CI are provided.
For some situations, the observable marginal probabilities contain sufficient information to exclude a particular monotonicity scenario. For example, under monotonicity for S and T, one of the restrictions that the data impose is π_{0111}<min(π_{0 cdot 1 cdot}, π_{cdot 1 cdot 1}). If the latter condition does not hold in the dataset at hand, monotonicity for S and T can be excluded.
● Data Source:
CranContrib
● Keywords: Monotonicity, Test Monotonicity
● Alias: Test.Mono
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MixedContContIT
(Package: Surrogate) :
Fits (univariate) mixed-effect models to assess surrogacy in the continuous-continuous case based on the Information-Theoretic framework
The function MixedContContIT uses the information-theoretic approach (Alonso & Molenberghs, 2007) to estimate trial- and individual-level surrogacy based on mixed-effect models when both S and T are continuous endpoints. The user can specify whether a (weighted or unweighted) full, semi-reduced, or reduced model should be fitted. See the Details section below.
plot FixedDiscrDiscrIT
(Package: Surrogate) :
Provides plots of trial-level surrogacy in the Information-Theoretic framework
Produces plots that provide a graphical representation of trial level surrogacy R^2_{ht} based on the Information-Theoretic approach of Alonso & Molenberghs (2007).
ICA.BinCont
(Package: Surrogate) :
Assess surrogacy in the causal-inference single-trial setting in the binary-continuous case
The function ICA.BinCont quantifies surrogacy in the single-trial causal-inference framework (individual causal association) when the surrogate endpoint is continuous (normally distributed) and the true endpoint is a binary outcome. For details, see Alonso et al. (2016).
This function generates an n by m array x, each of whose m columns contains n random values lying in the interval [a,b], subject to the condition that their sum be equal to s. The distribution of values is uniform in the sense that it has the conditional probability distribution of a uniform distribution over the whole n-cube, given that the sum of the x's is s. The function uses the randfixedsum algorithm, written by Roger Stafford and implemented in MatLab. For details, see http://www.mathworks.com/matlabcentral/fileexchange/9700-random-vectors-with-fixed-sum/content/randfixedsum.m
plot Information-Theoretic BinCombn
(Package: Surrogate) :
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)
Produces plots that provide a graphical representation of trial- and/or individual-level surrogacy (R2_ht and R2_hInd per cluster) based on the Information-Theoretic approach of Alonso & Molenberghs (2007).
plot Causal-Inference BinCont
(Package: Surrogate) :
Plots the (Meta-Analytic) Individual Causal Association and related metrics when S is continuous and T is binary
This function provides a plot that displays the frequencies, percentages, cumulative percentages or densities of the individual causal association (ICA; R^2_{H}) in the setting where S is continuous and T is binary.
plot Meta-Analytic
(Package: Surrogate) :
Provides plots of trial- and individual-level surrogacy in the meta-analytic framework
Produces plots that provide a graphical representation of trial- and/or individual-level surrogacy based on the meta-analytic approach of Buyse & Molenberghs (2000) in the single- and multiple-trial settings.
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