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De-identifying survival or flexsurvreg objects in RAsked 2022-Mar-29 at 11:01
Please consider the following:
I need to provide some R code syntax to analyse data with the
flexsurv package. I am not allowed to receive/analyse directly or on-site. I am however allowed to receive the analysis results.
When we run the
flexsurvreg() function on some data (here
ovarian from the
flexsurv package), the created object (here
fitw) contains enough information to "re-create" or "back-engineer" the actual data. But then I would technically have access to the data I am not allowed to have.
# Load package library("flexsurv") #> Loading required package: survival # Run flexsurvreg with data = ovarian fitw <- flexsurvreg(formula = Surv(futime, fustat) ~ factor(rx) + age, data = ovarian, dist="weibull") # Look at first observation in ovarian ovarian[1, ] #> futime fustat age resid.ds rx ecog.ps #> 1 59 1 72.3315 2 1 1 # With the following from the survival object, the data could be re-created fitw$data$Y[1, ] #> time status start stop time1 time2 #> 59 1 0 59 59 Inf fitw$data$m[1, ] #> Surv(futime, fustat) factor(rx) age (weights) #> 1 59 1 72.3315 1
We could write the code so that it also sets all those data that might be used for this back-engineering to
NA as follows:
# Setting all survival object observation to NA fitw$data$Y <- NA fitw$data$m <- NA fitw$data$mml$scale <- NA fitw$data$mml$rate <- NA fitw$data$mml$mu <- NA
Created on 2021-08-27 by the reprex package (v2.0.0)
If I proceed as the above and set all these parameters to
NA, could I then receive the
fitw object (e.g. as an
.RDS file) without ever being able to "back-engineer" the original data? Or is there any other way to share
fitw without the attached data?
ANSWERAnswered 2022-Mar-29 at 11:01
fitw$data <- NULL will remove all the individual-level data from the fitted model object. Some of the output functions may not work with objects stripped of data however. In the current development version on github, printing the model object should work. Also summary and predict methods should work, as long as covariate values are supplied in
newdata - omitting them won't work, since the default is to take the covariate values from the observed data.
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