ipw.bootstrap.ph2: To generate the empirical distribution for estimated...

In xiaoli-mcw/PIcompete: Prevalence-Incidence mixture model for competing risks time-to-event outcomes

Description

To generate the empirical distribution for estimated sub-distribution hazard functions and regression coefficients, we adopted the two-phase weighted bootstrap procedure (Hyun et al, reference). We fit ipw.pi.competing function from main.R to the bootstrap weighted data.The most arguments except for three arguments, n.boot, parallel, n.core are necessary for fitting "ipw.pi.competing" function.

Usage

ipw.bootstrap.ph2(n.boot, Data, p.model, i.model1, i.model2,
  trans.r1 = 0, trans.r2 = 0, n.beta = 1, n.gamma1 = 0,
  n.gamma2 = 0, reg.initials = NULL, convergence.criteria = 0.001,
  iteration.limit = 250, time.interval, time.list = NULL,
  parallel = FALSE, n.core = NULL, ...)

Arguments

n.boot	the number of replications for bootstrap resampling
parallel	if parallel=TRUE, n.core should be specified. Default to FALSE.
n.core	the nummber of cores for parallel computing

Value

The output is a list of class ipw.bootstrap.ph2, which contains the following elements.

• reg.coef: estimated regression coefficients

• time finite time points at which subdistribution hazards are estimated

• subdist.hazard1 estimated subdistribution hazard function for event 1

• subdist.hazard2 estimated subdistribution hazard function for event 2

Author(s)

Noorie Hyun, [e-mail], Xiao Li [e-mail]

References

• Hyun N, Katki HA, Graubard BI. Sample-Weighted Semiparametric Estimation of Cause-Specific Cumulative Risk and Incidence Using Left or Interval-Censored Data from Electronic Health Records. Statistics in Medicine 2020; under the 2nd review.

xiaoli-mcw/PIcompete documentation built on May 20, 2020, 7:44 p.m.