onearmsurv.dr: Doubly robust estimators of the coefficients in the two...

In precmed: Precision Medicine

Doubly robust estimators of the coefficients in the two regression

Description

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)

precmed documentation built on Oct. 6, 2024, 1:07 a.m.