A Pairwise Likelihood Augmented Cox Estimator for Truncated Data
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README.md

R package 'plac' version 0.1.1

Travis-CI Build Status Coverage Status CRAN version

A Pairwise Likelihood Augmented Estimator for the Cox Model under Left-Truncation

This R package implements a semi-parametric estimation method for the Cox model introduced in the paper A Pairwise Likelihood Augmented Cox Estimator for Left-truncated data by Wu et al. (2018). It gives more efficient estimate for left-truncated survival data using the marginal survival information upto the start of follow-up (when the subject enters the risk set). The independence between the underlying truncation time distribution and the covariates is the only additional assumption, which holds true for most applications of length-biased sampling problem and beyond.

Installation

This package can be installed from CRAN:

install.packages("plac")

or from github:

# install.packages("devtools")
devtools::install_github("942kid/plac")

Examples

The main wrapper function PLAC() calls the appropriate working function according to the covariate types in the dataset. For example,

library(plac)

# When only time-invariant covariates are involved
dat1 = sim.ltrc(n = 50)$dat
PLAC(ltrc.formula = Surv(As, Ys, Ds) ~ Z1 + Z2,
     ltrc.data = dat1, td.type = "none")

# When there is a time-dependent covariate that is independent of the truncation time
dat2 = sim.ltrc(n = 50, time.dep = TRUE,
               distr.A = "binomial", p.A = 0.8, Cmax = 5)$dat
PLAC(ltrc.formula = Surv(As, Ys, Ds) ~ Z,
     ltrc.data = dat2, td.type = "independent",
     td.var = "Zv", t.jump = "zeta")

# When there is a time-dependent covariate that depends on the truncation time
dat3 = sim.ltrc(n = 50, time.dep = TRUE, Zv.depA = TRUE, Cmax = 5)$dat
PLAC(ltrc.formula = Surv(As, Ys, Ds) ~ Z,
     ltrc.data = dat3, td.type = "post-trunc",
     td.var = "Zv", t.jump = "zeta")

For details, please refer to the document

help(PLAC)