calcSigma: Calculate Variance-Covariance Matrix for MSM Parameters...

In tmle: Targeted Maximum Likelihood Estimation

Calculate Variance-Covariance Matrix for MSM Parameters (calcSigma)

Description

An internal function called by the tmleMSM function to calculate the variance-covariance matrix of the parameter estimates based on the influence curve of the specified MSM.

Usage

calcSigma(hAV, gAVW, Y, Q, mAV, covar.MSM, covar.MSMA0, covar.MSMA1, I.V, 
     Delta, ub, id, family)

Arguments

hAV	values used in numerator of weights applied to the estimation procedure
gAVW	P(A=a | V,W,T)*P(Delta=1 | A,V,W,T)
Y	continuous or binary outcome variable
Q	estimated P(Y | A, V, W, T, Delta=1), typically targeted values Q* are passed in
mAV	predicted values for EY1 from the MSM using the targeted estimates for psi
covar.MSM	covariate values used as predictors for the MSM when A=a
covar.MSMA0	covariate values used as predictors for the MSM when A=0
covar.MSMA1	covariate values used as predictors for the MSM when A=1
I.V	indicator that observation is in stratum of interest
Delta	indicator of missing outcome. 1 - observed, 0 - missing
ub	upper bound on weights
id	subject identifier
family	‘gaussian’ for continuous outcomes, ‘binomial’ for binary outcomes

Value

sigma

influence-curve based variance-covariance matrix. See Rosenblum&vanderLaan2010 for details.

Author(s)

Susan Gruber

See Also

tmle, estimateQ, estimateG, tmleMSM

tmle documentation built on May 29, 2024, 6:38 a.m.