knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" )
This R package implements a semi-parametric estimation method for the Cox model introduced in the paper A Pairwise Likelihood Augmented Cox Estimator for Left-truncated data by Wu et al. (2018). It gives more efficient estimate for left-truncated survival data using the marginal survival information up to the start of follow-up (when the subject enters the risk set). The independence between the underlying truncation time distribution and the covariates is the only additional assumption, which holds true for most applications of length-biased sampling problem and beyond.
The package can be installed from CRAN:
install.packages("plac")
You can also install the development version of it from GitHub with:
# install.packages("devtools") devtools::install_github("942kid/plac")
The main wrapper function PLAC()
calls the appropriate working function according to the covariate types in the dataset.
For example,
library(plac) # When only time-invariant covariates are involved dat1 <- sim.ltrc(n = 50)$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 computation details, please refer to the document of the main wrapper function:
help(PLAC)
Wu, F., Kim, S., Qin, J., Saran, R., & Li, Y. (2018). A pairwise likelihood augmented Cox estimator for leftâtruncated data. Biometrics, 74(1), 100-108.
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