Doubly robust estimators of the coefficients in the two regression

Usage

onearmsurv.dr(ynew, dnew, trt, x.cate, tau0, weightsurv, ps, f.predictor)

Arguments

ynew: Truncated survival or censoring time; vector of size n.
dnew: The event indicator after truncation, 1 = event or censored after truncation, 0 = censored before truncation; vector of size n.
trt: Treatment received; vector of size n with treatment coded as 0/1.
x.cate: Matrix of p.cate baseline covariates specified in the outcome model; dimension n by p.cate.
tau0: The truncation time for defining restricted mean time lost.
weightsurv: Estimated inverse probability of censoring weights with truncation for all observations; vector of size n.
ps: Estimated propensity scores for all observations; vector of size n
f.predictor: Initial prediction of the outcome (restricted mean time loss) conditioned on the covariates x.cate for one treatment group r; mu_r(x.cate), step 1 in the two regression; vector of size n

Value

Doubly robust estimators of the two regression coefficients beta_r where r = 0, 1 is treatment received; vector of size p.cate + 1 (intercept included)