ICA.BinBin.Grid.Sample.Uncert: Assess surrogacy in the causal-inference single-trial setting...

In Surrogate: Evaluation of Surrogate Endpoints in Clinical Trials

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.

Description

The function ICA.BinBin.Grid.Sample.Uncert quantifies surrogacy in the single-trial causal-inference framework (individual causal association and causal concordance) when both the surrogate and the true endpoints are binary outcomes. This method provides an alternative for ICA.BinBin and ICA.BinBin.Grid.Full. It uses an alternative strategy to identify plausible values for \pi. The function allows to account for sampling variability in the marginal \pi. See Details below.

Usage

ICA.BinBin.Grid.Sample.Uncert(pi1_1_, pi1_0_, pi_1_1, pi_1_0, pi0_1_,
pi_0_1, Monotonicity=c("General"), M=100000,
Volume.Perc=0, Seed=sample(1:100000, size=1))

Arguments

pi1_1_	A vector that contains values for P(T=1,S=1|Z=0), i.e., the probability that S=T=1 when under treatment Z=0. A vector is specified to account for uncertainty, i.e., rather than keeping P(T=1,S=1|Z=0) fixed at one estimated value, a distribution can be specified (see examples below) from which a value is drawn in each run.
pi1_0_	A vector that contains values for P(T=1,S=0|Z=0).
pi_1_1	A vector that contains values for P(T=1,S=1|Z=1).
pi_1_0	A vector that contains values for P(T=1,S=0|Z=1).
pi0_1_	A vector that contains values for P(T=0,S=1|Z=0).
pi_0_1	A vector that contains values for P(T=0,S=1|Z=1).
Monotonicity	Specifies which assumptions regarding monotonicity should be made: Monotonicity=c("General"), Monotonicity=c("No"), Monotonicity=c("True.Endp"), Monotonicity=c("Surr.Endp"), or Monotonicity=c("Surr.True.Endp"). When a general analysis is requested (using Monotonicity=c("General") in the function call), all settings are considered (no monotonicity, monotonicity for S alone, for T alone, and for both for S and T. Default Monotonicity=c("General").
M	The number of random samples that have to be drawn for the freely varying parameters. Default M=100000. This argument is not used when Volume.Perc=0. Default M=10000.
Volume.Perc	Note that the marginals that are observable in the data set a number of restrictions on the unidentified correlations. For example, under montonicity for S and T, it holds that \pi_{0111}<=min(\pi_{0\cdot1\cdot}, \pi_{\cdot1\cdot1}) and \pi_{1100}<=min(\pi_{1\cdot0\cdot}, \pi_{\cdot1\cdot0}). For example, when min(\pi_{0\cdot1\cdot}, \pi_{\cdot1\cdot1})=0.10 and min(\pi_{1\cdot0\cdot}, \pi_{\cdot1\cdot0})=0.08, then all valid \pi_{0111}<=0.10 and all valid \pi_{1100}<=0.08. The argument Volume.Perc specifies the fraction of the 'volume' of the paramater space that is explored. This volume is computed based on the grids G={0, 0.01, ..., maximum possible value for the counterfactual probability at hand}. E.g., in the previous example, the 'volume' of the parameter space would be 11*9=99, and when e.g., the argument Volume.Perc=1 is used a total of 99 runs will be conducted. Notice that when monotonicity is not assumed, relatively high values of Volume.Perc will lead to a large number of runs and consequently a long analysis time.
Seed	The seed to be used to generate \pi_r. Default M=100000.

Details

In the continuous normal setting, surroagacy can be assessed by studying the association between the individual causal effects on S and T (see ICA.ContCont). In that setting, the Pearson correlation is the obvious measure of association.

When S and T are binary endpoints, multiple alternatives exist. Alonso et al. (2014) proposed the individual causal association (ICA; R_{H}^{2}), which captures the association between the individual causal effects of the treatment on S (\Delta_S) and T (\Delta_T) using information-theoretic principles.

The function ICA.BinBin.Grid.Full computes R_{H}^{2} using a grid-based approach where all possible combinations of the specified grids for the parameters that are allowed that are allowed to vary freely are considered. When it is not assumed that monotonicity holds for both S and T, the number of possible combinations become very high. The function ICA.BinBin.Grid.Sample.Uncert considers a random sample of all possible combinations.

Value

An object of class ICA.BinBin with components,

Pi.Vectors	An object of class data.frame that contains the valid \pi vectors.
R2_H	The vector of the R_H^2 values.
Theta_T	The vector of odds ratios for T.
Theta_S	The vector of odds ratios for S.
H_Delta_T	The vector of the entropies of \Delta_T.
Volume.No	The 'volume' of the parameter space when monotonicity is not assumed.
Volume.T	The 'volume' of the parameter space when monotonicity for T is assumed.
Volume.S	The 'volume' of the parameter space when monotonicity for S is assumed.
Volume.ST	The 'volume' of the parameter space when monotonicity for S and T is assumed.

Author(s)

Wim Van der Elst, Paul Meyvisch, Ariel Alonso & Geert Molenberghs

References

Alonso, A., Van der Elst, W., & Molenberghs, G. (2014). Validation of surrogate endpoints: the binary-binary setting from a causal inference perspective.

Buyse, M., Burzykowski, T., Aloso, A., & Molenberghs, G. (2014). Direct estimation of joint counterfactual probabilities, with application to surrogate marker validation.

See Also

ICA.ContCont, MICA.ContCont, ICA.BinBin, ICA.BinBin.Grid.Sample.Uncert

Examples

# Compute R2_H given the marginals (sample from uniform),
# assuming no monotonicity
ICA_No2 <- ICA.BinBin.Grid.Sample.Uncert(pi1_1_=runif(10000, 0.3562, 0.4868),
pi0_1_=runif(10000, 0.0240, 0.0837), pi1_0_=runif(10000, 0.0240, 0.0837),
pi_1_1=runif(10000, 0.4434, 0.5742), pi_1_0=runif(10000, 0.0081, 0.0533),
pi_0_1=runif(10000, 0.0202, 0.0763), Seed=1, Monotonicity=c("No"), M=1000)

summary(ICA_No2)

# obtain plot of R2_H
plot(ICA_No2)

Surrogate documentation built on June 22, 2024, 9:16 a.m.