Rank Preserving Structural Failure Time Models

Implements methods described by the paper Robins and Tsiatis (1991) . 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.


This is an R package that implements the method of Rank Preserving Strucutural 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)
#>     data = immdef, censor_time = censyrs)
#>       Length Class  Mode   
#> n     2      table  numeric
#> obs   2      -none- numeric
#> exp   2      -none- numeric
#> var   4      -none- numeric
#> chisq 1      -none- numeric
#> 
#> psi: -0.1811218
#> exp(psi): 0.8343337
#> Confidence Interval, psi -0.3497161 0.001993204
#> Confidence Interval, exp(psi)  0.7048882 1.001995
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.

News

rpsftm 1.0.2

adding "@keywords internal" to the documentation of functions that are not exported; this will lessen the visibility of the functions in the documentation to avoid confusion to users.

rpsftm 1.0.1

##Minor Changes

  • adding details of all contributors to the DESCRIPTION page.

rpsftm 1.0.0

This was the first release.

Reference manual

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install.packages("rpsftm")

1.1.0 by Simon Bond, 3 months ago


Browse source code at https://github.com/cran/rpsftm


Authors: Simon Bond [aut, cre] (primary author of code, secondary author of vignette), Annabel Allison [aut] (primary author of vignette, secondary author of code)


Documentation:   PDF Manual  


GPL-2 license


Imports survival, ggplot2, stats

Suggests testthat, knitr, rmarkdown, tableone


See at CRAN