Package: rpsftm 1.2.9

Simon Bond

rpsftm: Rank Preserving Structural Failure Time Models

Implements methods described by the paper Robins and Tsiatis (1991) <doi:10.1080/03610929108830654>. These use g-estimation to estimate the causal effect of a treatment in a two-armed randomised control trial where non-compliance exists and is measured, under an assumption of an accelerated failure time model and no unmeasured confounders.

Authors:Simon Bond [aut, cre], Annabel Allison [aut]

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NEWS

# Install 'rpsftm' in R:
install.packages('rpsftm', repos = 'https://cloud.r-project.org')
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.70 score 440 downloads 3 exports 29 dependencies

Last updated 1 years agofrom:2eafa03312. Checks:3 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 08 2025
R-4.5-linuxOKMar 08 2025
R-4.4-linuxOKMar 08 2025

Exports:cox.zphrandrpsftm

Dependencies:clicolorspacefansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerrlangscalessurvivaltibbleutf8vctrsviridisLitewithr

rpsftm: rank-preserving structural failure time models for survival data

Simon Bond, Annabel Allison

Rendered fromrpsftm_vignette.Rmdusingknitr::rmarkdownon Mar 08 2025.

Last update: 2024-03-13
Started: 2016-09-13

Citation

To cite package ‘rpsftm’ in publications use:

Bond S, Allison A (2024). rpsftm: Rank Preserving Structural Failure Time Models. R package version 1.2.9, https://CRAN.R-project.org/package=rpsftm.

Corresponding BibTeX entry: 

  @Manual{,
    title = {rpsftm: Rank Preserving Structural Failure Time Models},
    author = {Simon Bond and Annabel Allison},
    year = {2024},
    note = {R package version 1.2.9},
    url = {https://CRAN.R-project.org/package=rpsftm},
  }

Readme and manuals

This is an R package that implements the method of Rank Preserving Structural Failure Time models to estimate causal effects in failure time models in randomised control trials where participants do not comply with the treatment assigned.

As an example:

library(rpsftm)
?immdef
fit <- rpsftm(Surv(progyrs, prog)~rand(imm,1-xoyrs/progyrs), data = immdef, censor_time = censyrs)
summary(fit)
#>   arm   rx.Min. rx.1st Qu. rx.Median   rx.Mean rx.3rd Qu.   rx.Max.
#> 1   0 0.0000000  0.0000000 0.0000000 0.1574062  0.2547779 0.9770941
#> 2   1 1.0000000  1.0000000 1.0000000 1.0000000  1.0000000 1.0000000
#>         Length Class      Mode   
#> psi        1   -none-     numeric
#> fit       17   survfit    list   
#> CI         2   -none-     numeric
#> Sstar   1000   Surv       numeric
#> rand    2000   rand       numeric
#> ans        5   -none-     list   
#> eval_z     2   data.frame list   
#> n          2   table      numeric
#> obs        2   -none-     numeric
#> exp        2   -none-     numeric
#> var        4   -none-     numeric
#> chisq      1   -none-     numeric
#> pvalue     1   -none-     numeric
#> call       4   -none-     call   
#> formula    3   terms      call   
#> terms      3   terms      call   
#> 
#> psi: -0.1813226
#> exp(psi): 0.8341662
#> Confidence Interval, psi -0.34984 0.002287789
#> Confidence Interval, exp(psi)  0.7048008 1.00229
plot(fit)

The main function is rpsftm which returns an object that has print, summary, and plot S3 methods.

See the vignette rpsftm_vignette for further details, explanation and examples.