drsurv: Doubly robust estimator of the average treatment effect with Cox model for survival data

Description

Doubly robust estimator of the average treatment effect between two treatments, which is the restricted mean time lost (RMTL) ratio of treatment 1 over treatment 0 for survival outcomes.

Usage

drsurv(
  y,
  d,
  x.cate,
  x.ps,
  x.ipcw,
  trt,
  yf = NULL,
  tau0,
  surv.min = 0.025,
  ps.method = "glm",
  minPS = 0.01,
  maxPS = 0.99,
  ipcw.method = "breslow"
)

Value

Return a list of 4 elements:

rmst1: A numeric value of the estimated restricted mean survival time n the group trt = 1.
rmst0: A numeric value of the estimated restricted mean survival time n the group trt = 0.
log.rmtl.ratio: A numeric value of the estimated log rmtl ratio.
log.hazard.ratio: A numeric value of the estimated log hazard ratio.

Arguments

y: Observed survival or censoring time; vector of size n.
d: The event indicator, normally 1 = event, 0 = censored; vector of size n.
x.cate: Matrix of p.cate baseline covariates specified in the outcome model; dimension n by p.cate.
x.ps: Matrix of p.ps baseline covariates specified in the propensity score model; dimension n by p.ps.
x.ipcw: Matrix of p.ipw baseline covariate specified in inverse probability of censoring weighting; dimension n by p.ipw.
trt: Treatment received; vector of size n with treatment coded as 0/1.
yf: Follow-up time, interpreted as the potential censoring time; vector of size n if the potential censoring time is known.
tau0: The truncation time for defining restricted mean time lost.
surv.min: Lower truncation limit for probability of being censored (positive and very close to 0).
ps.method: A character value for the method to estimate the propensity score. Allowed values include one of: 'glm' for logistic regression with main effects only (default), or 'lasso' for a logistic regression with main effects and LASSO penalization on two-way interactions (added to the model if interactions are not specified in ps.model). Relevant only when ps.model has more than one variable.
minPS: A numerical value (in `[0, 1]`) below which estimated propensity scores should be truncated. Default is 0.01.
maxPS: A numerical value (in `(0, 1]`) above which estimated propensity scores should be truncated. Must be strictly greater than minPS. Default is 0.99.
ipcw.method: The censoring model. Allowed values are: 'breslow' (Cox regression with Breslow estimator of the baseline survivor function), 'aft (exponential)', 'aft (weibull)', 'aft (lognormal)' or 'aft (loglogistic)'. Default is 'breslow'.