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I ran into the following error trying to compute 99% CI for model contrasts with standsurv():
Calculating bootstrap standard errors / confidence intervals
Error in `rename()`:
! Can't rename columns that don't exist.
✖ Column `2.5%` doesn't exist.
I built a minimal(ish) example from examples in the standsurv vignette [1]:
data(bc)
set.seed(236236)
## Age at diagnosis is correlated with survival time. A longer survival time
## gives a younger mean age
bc$age <- rnorm(dim(bc)[1], mean = 65 - scale(bc$recyrs, scale=F), sd = 5)
## Create age at diagnosis in days - used later for matching to expected rates
bc$agedays <- floor(bc$age * 365.25)
## Create some random diagnosis dates between 01/01/1984 and 31/12/1989
bc$diag <- as.Date(floor(runif(dim(bc)[1], as.Date("01/01/1984", "%d/%m/%Y"),
as.Date("31/12/1989", "%d/%m/%Y"))),
origin="1970-01-01")
## Create sex (assume all are female)
bc$sex <- factor("female")
## 2-level prognostic variable
bc$group2 <- ifelse(bc$group=="Good", "Good", "Medium/Poor")
mod <- flexsurvreg(
Surv(recyrs, censrec)~group2,
#anc = list(shape = ~ group2), dist="weibullPH"
dist="weibull", data=bc
)
## example works either if commenting out line `B=...` or using `cl=0.95`
mod.contr <- standsurv(mod,
at=list(list(group2='Good'), list(group2='Medium/Poor')),
B=1e4, boot=T,
t=20, cl=0.99, ci=T
)
Related: My original question was focused on family-wise error rates for model contrasts, similar to adjust="tukey" in emmeans() [2]. It seemed like a very simple adjustment would be to use wider CI (akin to a Bonferroni correction). I'd be interested to hear anyone's advice on using standsurv() for formal hypothesis testing (e.g. p-value of hazard ratio relative to 1).
Thank you @helmingstay for reporting this bug. I've now made the fix and created a pull request for @chjackson to incorporate. Thanks for spotting this!
I've not considered using standsurv for formal hypothesis testing so cannot help there.
I ran into the following error trying to compute 99% CI for model contrasts with
standsurv()
:I built a minimal(ish) example from examples in the standsurv vignette [1]:
Related: My original question was focused on family-wise error rates for model contrasts, similar to
adjust="tukey"
inemmeans()
[2]. It seemed like a very simple adjustment would be to use wider CI (akin to a Bonferroni correction). I'd be interested to hear anyone's advice on using standsurv() for formal hypothesis testing (e.g. p-value of hazard ratio relative to 1).[1] https://cran.r-project.org/web/packages/flexsurv/vignettes/standsurv.html
[2] https://cran.r-project.org/web/packages/emmeans/vignettes/comparisons.html#pairwise
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