F_est: Estimate the Conditional CDF of the Event Time given...

In truncAIPW: Doubly Robust Estimation under Covariate-Induced Dependent Left Truncation

Estimate the Conditional CDF of the Event Time given Covariates

Description

Estimate the conditional cumulative distribution function (CDF) of the event time given covariates evaluated at given time points. The options implemented in this function are: Cox proportional hazards regression using function coxph() from R package ‘survival’, and the hazard model with penalized splines using function survPen() from R package ‘survPen’.

Usage

F_est(
  dat.fit,
  dat.est = dat.fit,
  time.eval,
  model,
  time.name,
  Q.name,
  event.name,
  cov.names,
  trim = 0,
  formula.survPen = NA
)

Arguments

dat.fit	data frame that is used to fit the model for the full data conditional distribution of the event time given the covariates.
dat.est	data frame that contains the subjects for which the estimated conditional CDF is computed.
time.eval	vector of time points at which the conditional CDF is evaluated.
model	method used to estimate the conditional CDF. The options available are "Cox" and "spline", corresponding to Cox proportional hazards regression using function coxph() from R package ‘survival’, and the hazard model with penalized splines using function survPen() from R package ‘survPen’, respectively.
time.name	name of the event time variable.
Q.name	name of the left truncation time variable.
event.name	name of the event indicator.
cov.names	vector of the names of covariates.
trim	constant for bounding the estimated conditional CDF from 1.
formula.survPen	the formula when applying the hazard model with penalized splines implemented in survPen::survPen.

Value

F_est() returns a matrix of the estimated conditional CDF for subjects in 'data.est' evaluated at the time points in the vector 'time.eval'. Each row corresponds to a subject and each column corresponds to a time point. The column names of the matrix are the times in 'time.eval'.

See Also

G_est

Examples

data("simu")
u = c(1, 1.5, 2, 2.5, 3, 3.5, 4)
Fuz.mx = F_est(simu, simu[1:10,], u, "Cox", "time", "Q", "delta", c("Z1","Z2"))

truncAIPW documentation built on Aug. 31, 2023, 5:10 p.m.