PPE.BinBin {Surrogate} | R Documentation |
Evaluate a surrogate predictive value based on the minimum probability of a prediction error in the setting where both
and
are binary endpoints
Description
The function PPE.BinBin
assesses a surrogate predictive value using the probability of a prediction error in the single-trial causal-inference framework when both the surrogate and the true endpoints are binary outcomes. It additionally assesses the indivdiual causal association (ICA). See Details below.
Usage
PPE.BinBin(pi1_1_, pi1_0_, pi_1_1, pi_1_0,
pi0_1_, pi_0_1, M=10000, Seed=1)
Arguments
pi1_1_ |
A scalar that contains values for |
pi1_0_ |
A scalar that contains values for |
pi_1_1 |
A scalar that contains values for |
pi_1_0 |
A scalar that contains values for |
pi0_1_ |
A scalar that contains values for |
pi_0_1 |
A scalar that contains values for |
M |
The number of valid vectors that have to be obtained. Default |
Seed |
The seed to be used to generate |
Details
In the continuous normal setting, surroagacy can be assessed by studying the association between the individual causal effects on and
(see
ICA.ContCont
). In that setting, the Pearson correlation is the obvious measure of association.
When and
are binary endpoints, multiple alternatives exist. Alonso et al. (2016) proposed the individual causal association (ICA;
), which captures the association between the individual causal effects of the treatment on
(
) and
(
) using information-theoretic principles.
The function PPE.BinBin
computes using a grid-based approach where all possible combinations of the specified grids for the parameters that are allowed to vary freely are considered. It additionally computes the minimal probability of a prediction error (PPE) and the reduction on the PPE using information that
conveys on
. Both measures provide complementary information over the
and facilitate more straightforward clinical interpretation. No assumption about monotonicity can be made.
Value
An object of class PPE.BinBin
with components,
index |
count variable |
PPE |
The vector of the PPE values. |
RPE |
The vector of the RPE values. |
PPE_T |
The vector of the |
R2_H |
The vector of the |
H_Delta_T |
The vector of the entropies of |
H_Delta_S |
The vector of the entropies of |
I_Delta_T_Delta_S |
The vector of the mutual information of |
Author(s)
Paul Meyvisch, Wim Van der Elst, Ariel Alonso, Geert Molenberghs
References
Alonso A, Van der Elst W, Molenberghs G, Buyse M and Burzykowski T. (2016). An information-theoretic approach for the evaluation of surrogate endpoints based on causal inference.
Meyvisch P., Alonso A.,Van der Elst W, Molenberghs G. (2018). Assessing the predictive value of a binary surrogate for a binary true endpoint, based on the minimum probability of a prediction error.
See Also
Examples
# Conduct the analysis
## Not run: # time consuming code part
PPE.BinBin(pi1_1_=0.4215, pi0_1_=0.0538, pi1_0_=0.0538,
pi_1_1=0.5088, pi_1_0=0.0307,pi_0_1=0.0482,
Seed=1, M=10000)
## End(Not run)