ISTE.ContCont: Individual-level surrogate threshold effect for continuous...

In Surrogate: Evaluation of Surrogate Endpoints in Clinical Trials

Individual-level surrogate threshold effect for continuous normally distributed surrogate and true endpoints.

Description

Computes the individual-level surrogate threshold effect in the causal-inference single-trial setting where both the surrogate and the true endpoint are continuous normally distributed variables. For details, see paper in the references section.

Usage

ISTE.ContCont(Mean_T1, Mean_T0, Mean_S1, Mean_S0, N, Delta_S=c(-10, 0, 10), 
zeta.PI=0.05, PI.Bound=0, PI.Lower=TRUE, Show.Prediction.Plots=TRUE, Save.Plots="No", 
T0S0, T1S1, T0T0=1, T1T1=1, S0S0=1, S1S1=1, T0T1=seq(-1, 1, by=.001), 
T0S1=seq(-1, 1, by=.001), T1S0=seq(-1, 1, by=.001),
S0S1=seq(-1, 1, by=.001), M.PosDef=500, Seed=123)

Arguments

Mean_T1	A scalar or vector that specifies the mean of the true endpoint in the experimental treatment condition (a vector is used to account for estimation uncertainty).
Mean_T0	A scalar or vector that specifies the mean of the true endpoint in the control condition (a vector is used to account for estimation uncertainty).
Mean_S1	A scalar or vector that specifies the mean of the surrogate endpoint in the experimental treatment condition (a vector is used to account for estimation uncertainty).
Mean_S0	A scalar or vector that specifies the mean of the surrogate endpoint in the control condition (a vector is used to account for estimation uncertainty).
N	The sample size of the clinical trial.
Delta_S	The vector or scalar of \Delta S values for which the expected \Delta T and its prediction error has to be computed.
zeta.PI	The alpha-level to be used in the computation of the prediction interval around E(\Delta T). Default zeta.PI=0.05, i.e., the 95\% prediction interval.
PI.Bound	The ISTE is defined as the value of \Delta S for which the lower (or upper) bound of the (1-\alpha)\% prediction interval around E(\Delta T) is 0. If another threshold value than 0 is desired, this can be requested by using the PI.Bound argument. For example, the argument PI.Bound=5 can be used in the function call to obtain the values of \Delta S for which the lower (or upper) bound of the (1-\alpha)\% prediction intervals (in the different runs of the algorithm)around \Delta T equal 5.
PI.Lower	Logical. Should a lower (PI.Lower=TRUE) or upper (PI.Lower=FALSE) prediction interval be used in the computation of ISTE? Default PI.Lower=TRUE.
Show.Prediction.Plots	Logical. Should plots that depict E(\Delta T) against \Delta S (prediction function), the prediction interval, and the ISTE for the different runs of the algorithm be shown? Default Show.Prediction.Plots=TRUE.
Save.Plots	Should the prediction plots (see previous item) be saved? If Save.Plots="No" is used (the default argument), the plots are not saved. If the plots have to be saved, replace "No" by the desired location, e.g., Save.Plots="C:/Analysis directory/" on a windows computer or Save.Plots="/Users/wim/Desktop/Analysis directory/" on macOS or Linux.
T0S0	A scalar or vector that specifies the correlation(s) between the surrogate and the true endpoint in the control treatment condition that should be considered in the computation of ISTE.
T1S1	A scalar or vector that specifies the correlation(s) between the surrogate and the true endpoint in the experimental treatment condition that should be considered in the computation of ISTE.
T0T0	A scalar that specifies the variance of the true endpoint in the control treatment condition that should be considered in the computation of ISTE. Default 1.
T1T1	A scalar that specifies the variance of the true endpoint in the experimental treatment condition that should be considered in the computation of ISTE. Default 1.
S0S0	A scalar that specifies the variance of the surrogate endpoint in the control treatment condition that should be considered in the computation of ISTE. Default 1.
S1S1	A scalar that specifies the variance of the surrogate endpoint in the experimental treatment condition that should be considered in the computation of ISTE. Default 1.
T0T1	A scalar or vector that contains the correlation(s) between the counterfactuals T0 and T1 that should be considered in the computation of ISTE. Default seq(-1, 1, by=.001).
T0S1	A scalar or vector that contains the correlation(s) between the counterfactuals T0 and S1 that should be considered in the computation of ISTE. Default seq(-1, 1, by=.001).
T1S0	A scalar or vector that contains the correlation(s) between the counterfactuals T1 and S0 that should be considered in the computation of ISTE. Default seq(-1, 1, by=.001).
S0S1	A scalar or vector that contains the correlation(s) between the counterfactuals S0 and S1 that should be considered in the computation of ISTE. Default seq(-1, 1, by=.001).
M.PosDef	The number of positive definite \Sigma matrices that should be identified. This will also determine the amount of ISTE values that are identified. Default M.PosDef=500.
Seed	The seed to be used in the analysis (for reproducibility). Default Seed=123.

Details

See paper in the references section.

Value

An object of class ICA.ContCont with components,

ISTE_Low_PI	The vector of individual surrogate threshold effect (ISTE) values, i.e., the values of \Delta S for which the lower bound of the (1-\alpha)\% prediction interval around \Delta T is 0 (or another threshold value, which can be requested by using the PI.Bound argument in the function call).
ISTE_Up_PI	Same as ISTE_Low_PI, but using the upper bound of the (1-\alpha)\% prediction interval.
MSE	The vector of mean squared error values that are obtained in the prediction of \Delta T based on \Delta S.
gamma0	The vector of intercepts that are obtained in the prediction of \Delta T based on \Delta S.
gamma1	The vector of slope that are obtained in the prediction of \Delta T based on \Delta S.
Delta_S_For_Which_Delta_T_equal_0	The vector of \Delta S values for which E(\Delta T = 0).
S_squared_pred	The vector of variances of the prediction errors for \Delta T.
Predicted_Delta_T	The vector/matrix of predicted values of \Delta T for the \Delta S values that were requested in the function call (argument Delta_S).
PI_Interval_Low	The vector/matrix of lower bound values of the (1-\alpha)\% prediction interval around \Delta T for the \Delta S values that were requested in the function call (argument Delta_S).
PI_Interval_Up	The vector/matrix of upper bound values of the (1-\alpha)\% prediction interval around \Delta T for the \Delta S values that were requested in the function call (argument Delta_S).
T0T0	The vector of variances of T0 (true endpoint in the control treatment) that are used in the computation (this is a constant if the variance is fixed in the function call).
T1T1	The vector of variances of T1 (true endpoint in the experimental treatment) that are used in the computations (this is a constant if the variance is fixed in the function call).
S0S0	The vector of variances of S0 (surrogate endpoint in the control treatment) that are used in the computations (this is a constant if the variance is fixed in the function call).
S1S1	The vector of variances of S1 (surrogate endpoint in the experimental treatment) that are used in the computations (this is a constant if the variance is fixed in the function call).
Mean_DeltaT	The vector of treatment effect values on the true endpoint that are used in the computations (this is a constant if the means of T0 and T1 are fixed in the function call).
Mean_DeltaS	The vector of treatment effect values on the surrogate endpoint that are used in the computations (this is a constant if the means of S0 and S1 are fixed in the function call).
Total.Num.Matrices	An object of class numeric that contains the total number of matrices that can be formed as based on the user-specified correlations in the function call.
Pos.Def	A data.frame that contains the positive definite matrices that can be formed based on the user-specified correlations. These matrices are used to compute the vector of the ISTE values.
ICA	Apart from ISTE, ICA is also computed (the individual causal association). For details, see ICA.ContCont.
zeta.PI	The zeta.PI value specified in the function call.
PI.Bound	The PI.Bound value specified in the function call.
PI.Lower	The PI.Lower value specified in the function call.
Delta_S	The Delta_S value(s) specified in the function call.

Author(s)

Wim Van der Elst, Ariel Alonso, & Geert Molenberghs

References

Van der Elst, W., Alonso, A. A., and Molenberghs, G. (submitted). The individual-level surrogate threshold effect in a causal-inference setting.

See Also

ICA.ContCont

Examples

# Define input for analysis using the Schizo dataset, 
# with S=BPRS and T = PANSS. 
# For each of the identifiable quantities,
# uncertainty is accounted for by specifying a uniform
# distribution with min, max values corresponding to
# the 95% confidence interval of the quantity.
T0S0 <- runif(min = 0.9524, max = 0.9659, n = 1000)
T1S1 <- runif(min = 0.9608, max = 0.9677, n = 1000)

S0S0 <- runif(min=160.811, max=204.5009, n=1000)
S1S1 <- runif(min=168.989, max = 194.219, n=1000)
T0T0 <- runif(min=484.462, max = 616.082, n=1000)
T1T1 <- runif(min=514.279, max = 591.062, n=1000)

Mean_T0 <- runif(min=-13.455, max=-9.489, n=1000)
Mean_T1 <- runif(min=-17.17, max=-14.86, n=1000)
Mean_S0 <- runif(min=-7.789, max=-5.503, n=1000)
Mean_S1 <- runif(min=-9.600, max=-8.276, n=1000)

# Do the ISTE analysis
## Not run: 
ISTE <- ISTE.ContCont(Mean_T1=Mean_T1, Mean_T0=Mean_T0, 
 Mean_S1=Mean_S1, Mean_S0=Mean_S0, N=2128, Delta_S=c(-50:50), 
 zeta.PI=0.05, PI.Bound=0, Show.Prediction.Plots=TRUE,
 Save.Plots="No", T0S0=T0S0, T1S1=T1S1, T0T0=T0T0, T1T1=T1T1, 
 S0S0=S0S0, S1S1=S1S1)

# Examine results:
summary(ISTE)

# Plots of results. 
  # Plot ISTE
plot(ISTE)
  # Other plots, see plot.ISTE.ContCont for details
plot(ISTE, Outcome="MSE")
plot(ISTE, Outcome="gamma0")
plot(ISTE, Outcome="gamma1")
plot(ISTE, Outcome="Exp.DeltaT")
plot(ISTE, Outcome="Exp.DeltaT.Low.PI")
plot(ISTE, Outcome="Exp.DeltaT.Up.PI")

## End(Not run)

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