Doubly robust estimators of the coefficients in the contrast regression as well as their covariance matrix and convergence information

Newton-Raphson algorithm is used to solve the estimating equation bar S_n (delta) = 0

Usage

twoarmsurv.dr(
  ynew,
  dnew,
  trt,
  x.cate,
  tau0,
  weightsurv,
  ps,
  f1.predictor,
  f0.predictor,
  error.maxNR = 0.001,
  max.iterNR = 100,
  tune = c(0.5, 2)
)

Arguments

ynew: Truncated survival time; vector of size n
dnew: Event indicator after 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
f1.predictor: Initial predictions of the outcome (restricted mean time loss) conditioned on the covariates x.cate for treatment group trt = 1; mu_1(x.cate), step 1 in the two regression; vector of size n
f0.predictor: Initial predictions of the outcome (restricted mean time loss) conditioned on the covariates x.cate for treatment group trt = 0; mu_0(x.cate), step 1 in the two regression; vector of size n
error.maxNR: A numerical value > 0 indicating the minimum value of the mean absolute error in Newton Raphson algorithm. Used only if score.method = 'contrastReg'. Default is 0.001.
max.iterNR: A positive integer indicating the maximum number of iterations in the Newton Raphson algorithm. Used only if score.method = 'contrastReg'. Default is 100.
tune: A vector of 2 numerical values > 0 specifying tuning parameters for the Newton Raphson algorithm. tune[1] is the step size, tune[2] specifies a quantity to be added to diagonal of the slope matrix to prevent singularity. Used only if score.method = 'contrastReg'. Default is c(0.5, 2).

Value

coef: Doubly robust estimators of the contrast regression coefficients delta_0; vector of size p.cate + 1 (intercept included) converge: Indicator that the Newton Raphson algorithm converged for delta_0; boolean