Predict.Treat.ContCont: Compute the predicted treatment effect on the true endpoint...
In EffectTreat: Prediction of Therapeutic Success

Description Usage Arguments Value Author(s) References See Also Examples

This function computes the predicted Δ T_j of a patient based on the pretreatment value S_j of a patient in the continuous-continuous setting.

1	Predict.Treat.ContCont(x, S, Beta, SS, mu_S)

`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 pretreatment predictor (in the validation sample).

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

`Pred_T`	The predicted Δ T_j.
`Var_Delta.T`	The variance σ_{Δ_{T}}.
`T0T1`	The correlation between the counterfactuals T_{0}, T_{1}.
`PCA`	The vector of ρ_{ψ} values.
`Var_Delta.T_S`	The variance σ_{Δ_{T}}\|S_j.

Wim Van der Elst, Ariel Alonso, & Geert Molenberghs

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

PCA.ContCont

# 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
Predict <- Predict.Treat.ContCont(x=PCA, S=10, Beta=5, SS=2, mu_S=4)

# examine the results
summary(Predict)

# plot Delta_T_j given S_T, for the mean value of the valid rho_T0T1  
plot(Predict, Mean.T0T1=TRUE, Median.T0T1=FALSE)