R: Compute the predicted treatment effect on the true endpoint...

Predict.Treat.T0T1.ContCont {EffectTreat}

R Documentation

Compute the predicted treatment effect on the true endpoint of a patient based on his or her observed pretreatment predictor value in the continuous-continuous setting for a particular (single) value of `\rho_{T0T1}`.

Description

This function computes the predicted \Delta T_j of a patient based on the pretreatment value S_j of a patient in the continuous-continuous setting for a particular (single) value of rho_T0T1.

Usage

Predict.Treat.T0T1.ContCont(x, S, Beta, SS, mu_S, T0T1, alpha=0.05)

Arguments

`x`	An object of class `PCA.ContCont`. See `PCA.ContCont`.
`S`	The observed pretreatment value `S_j` for a patient.
`Beta`	The estimated treatment effect on the true endpoint (in the validation sample).
`SS`	The estimated variance of the pretreatment predictor endpoint.
`mu_S`	The estimated mean of the surrogate endpoint (in the validation sample).
`T0T1`	The `\rho_{T0T1}` value (used to compute the variance of `\Delta T_j`\|`S_j`.
`alpha`	The `\alpha`-level that is used to determine the confidence interval around `\Delta T_j`\|`S_j`. Default `0.05`.

Value

An object of class PCA.Predict.Treat.T0T1.ContCont with components,

`Pred_T`	The predicted `\Delta T_j`.
`Var_Delta.T`	The variance `\sigma_{\Delta_{T}}`.
`T0T1`	The correlation between the counterfactuals `T_{0}`, `T_{1}`.
`CI_low`	The lower border of the `1-\alpha`% confidence interval of `\Delta T_j`\|`S_j`.
`CI_high`	The upper border of the `1-\alpha`% confidence interval of `\Delta T_j`\|`S_j`.
`Var_Delta.T_S`	The variance `\sigma_{\Delta_{T}}`\|`S_j`.
`alpha`	The `\alpha`-level that is used to determine the confidence interval of `\Delta T_j`\|`S_j`.

Author(s)

Wim Van der Elst, Ariel Alonso, & Geert Molenberghs

References

Alonso, A., Van der Elst, W., & Molenberghs, G. (submitted). Validating predictors of therapeutic success: a causal inference approach.

Examples

# Generate the vector of PCA.ContCont values when rho_T0S=.3, rho_T1S=.9, 
# sigma_T0T0=2, sigma_T1T1=2,sigma_SS=2, and the grid of values {-1, -.99, 
# ..., 1} is considered for the correlations between T0 and T1:
PCA <- PCA.ContCont(T0S=.3, T1S=.9, T0T0=2, T1T1=2, SS=2, 
T0T1=seq(-1, 1, by=.01))

# Obtain the predicted value T for a patient who scores S = 10, using beta=5,
# SS=2, mu_S=4, assuming rho_T0T1=.6
indiv <- Predict.Treat.T0T1.ContCont(x=PCA, S=10, Beta=5, SS=2, mu_S=4, T0T1=.6)
summary(indiv)

# obtain a plot with the 95% CI around delta T_j | S_j (assuming rho_T0T1=.6)
plot(indiv)

[Package EffectTreat version 1.1 Index]

Compute the predicted treatment effect on the true endpoint of a patient based on his or her observed pretreatment predictor value in the continuous-continuous setting for a particular (single) value of \rho_{T0T1}.