Predict.Treat.Multivar.ContCont: Compute the predicted treatment effect on the true endpoint of a patient based on his or her observed vector of pretreatment predictor values in the continuous-continuous setting

Description

This function computes the predicted \(\Delta T_j\) of a patient based on the vector of pretreatment values \(\bold{S}_j\) of a patient in the continuous-continuous setting.

Usage

Predict.Treat.Multivar.ContCont(Sigma_TT, Sigma_TS, Sigma_SS, Beta, 
S, mu_S, T0T1=seq(-1, 1, by=.01))

Arguments

Sigma_TT

The variance-covariance matrix \(\bold{\Sigma}_{TT}=\left(\begin{array}{cc}\sigma_{T0T0} & \sigma_{T0T1} \\ \sigma_{T0T1} & \sigma_{T1T1}\end{array}\right)\).

Sigma_TS

The matrix that contains the covariances \(\sigma_{T0Sr}\), \(\sigma_{T1Sr}\). For example, when there are \(2\) pretreatment predictors \(\bold{\Sigma}_{TS}=\left(\begin{array}{cc}\sigma_{T0S1} & \sigma_{T0S2} \\ \sigma_{T1S1} & \sigma_{T1S2}\end{array}\right)\).

Sigma_SS

The variance-covariance matrix of the pretreatment predictors. For example, when there are \(2\) pretreatment predictors \(\bold{\Sigma}_{SS}=\left(\begin{array}{cc}\sigma_{S1S1} & \sigma_{S1S2} \\ \sigma_{S1S2} & \sigma_{S2S2}\end{array}\right)\).

Beta

The estimated treatment effect on the true endpoint (in the validation sample).

The vector of observed pretreatment values \(\bold{S}_j\) for a patient.

mu_S

The vector of estimated means of the pretreatment predictor (in the validation sample).

T0T1

A scalar or vector that contains the correlation(s) between the counterfactuals \(T_0\) and \(T_1\) that should be considered in the computation of \(\rho_{\psi}\). Default seq(-1, 1, by=.01), i.e., the values \(-1\), \(-0.99\), \(-0.98\), …, \(1\).

Value

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

Pred_T

The predicted \(\Delta T_j\).

Var_Delta.T_S

The variance \(\sigma_{\Delta_{T}}\)|\(S_j\).

T0T1

The correlation between the counterfactuals \(T_{0}\), \(T_{1}\).

References

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

Examples

Run this code

# NOT RUN {
# Specify the covariance matrices to be used 
Sigma_TT = matrix(c(177.870, NA, NA, 162.374), byrow=TRUE, nrow=2)
Sigma_TS = matrix(data = c(-45.140, -109.599, 11.290, -56.542,
-106.897, 20.490), byrow = TRUE, nrow = 2)
Sigma_SS = matrix(data=c(840.564, 73.936, -3.333, 73.936, 357.719,
-30.564, -3.333, -30.564, 95.063), byrow = TRUE, nrow = 3)

# Specify treatment effect (Beta), means of vector S (mu_s), and 
# observed pretreatment variable values for patient (S)
Beta <- -0.9581 # treatment effect
mu_S = matrix(c(66.8149, 84.8393, 25.1939), nrow=3) #means S_1--S_3
S = matrix(c(90, 180, 30), nrow=3) # S_1--S_3 values for a patient

# predict Delta_T based on S
Pred_S <- Predict.Treat.Multivar.ContCont(Sigma_TT=Sigma_TT, Sigma_TS=Sigma_TS,
Sigma_SS=Sigma_SS, Beta=Beta, S=S, mu_S=mu_S, T0T1=seq(-1, 1, by=.01))

# Explore results
summary(Pred_S)
plot(Pred_S)
# }