Calculate pairwise comparisons between group levels with corrections for multiple testing.
pairwise_survdiff(formula, data, p.adjust.method = "BH", na.action, rho = 0)
formula | a formula expression as for other survival models, of the form Surv(time, status) ~ predictors. |
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data | a data frame in which to interpret the variables occurring in the formula. |
p.adjust.method | method for adjusting p values (see
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na.action | a missing-data filter function. Default is options()$na.action. |
rho | a scalar parameter that controls the type of test. Allowed values include 0 (for Log-Rank test) and 1 (for peto & peto test). |
Returns an object of class "pairwise.htest", which is a list containing the p values.
survival::survdiff
library(survival) library(survminer) data(myeloma) # Pairwise survdiff res <- pairwise_survdiff(Surv(time, event) ~ molecular_group, data = myeloma) res#> #> Pairwise comparisons using Log-Rank test #> #> data: myeloma and molecular_group #> #> Cyclin D-1 Cyclin D-2 Hyperdiploid Low bone disease MAF #> Cyclin D-2 0.723 - - - - #> Hyperdiploid 0.943 0.723 - - - #> Low bone disease 0.723 0.988 0.644 - - #> MAF 0.644 0.447 0.523 0.485 - #> MMSET 0.328 0.103 0.103 0.103 0.723 #> Proliferation 0.103 0.038 0.038 0.062 0.485 #> MMSET #> Cyclin D-2 - #> Hyperdiploid - #> Low bone disease - #> MAF - #> MMSET - #> Proliferation 0.527 #> #> P value adjustment method: BH# Symbolic number coding symnum(res$p.value, cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("****", "***", "**", "*", "+", " "), abbr.colnames = FALSE, na = "")#> Cyclin D-1 Cyclin D-2 Hyperdiploid Low bone disease MAF MMSET #> Cyclin D-2 #> Hyperdiploid #> Low bone disease #> MAF #> MMSET #> Proliferation * * + #> attr(,"legend") #> [1] 0 ‘****’ 1e-04 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1 \t ## NA: ‘’