I want to calculate the sample size for a survival study of patients with acute myeloid leukemia, with two cohorts, one receiving chemotherapy and the other not. I want an event-free survival (EFS) at 1 year of 40% with a margin of 5%. The difference between the two cohorts has to be 5-10%, with a power of 0.8 and alpha of 0.05.
I have considered a non-inferiority design, as the new treatment may not be as efficient but has lower toxicity, and I want to prove that the new treatment is not significantly worse than the reference treatment, which has already been tested. I want a maximum difference in efficiency of 10%. Does this make sense? Would it be better to do a superiority design? I have calculated the sample size for each cohort, which must be the same, using the epi.ssninfb function from the epiR package, but I have seen that there are many R packages (that take into account different types of distribution, constant or non-constant hazard ratios, among other assumptions) and many programs to perform the calculation, and I don't know which is the best method.
The hypotheses planned are:
Ho: prob. standard treatment - prob.new treatment >= 0.1
H1: prob. standard treatment - prob.new treatment < 0.1
And the R code:
install.packages("epiR")
library(epiR)
epi.ssninfb(treat = 0.4, control = 0.4, delta = 0.10, n = NA, r = 1,
power = 0.8, nfractional = TRUE, alpha = 0.05)
$n.total
[1] 593.5255
$n.treat
[1] 296.7627
$n.control
[1] 296.7627
$delta
[1] 0.1
$power
[1] 0.8
Has the sample size been correctly determined? I have also found other R functions to perform the calculation, such as power.t.test (survival package), sample.size.NI (dani package), power.prop.test (stats package), nSurvival (gsDesign package, which requires accrual and follow-up times that I don't have), etc.
I'm very confused. Can anyone help me?
Thank you very much! :)
EI_Stats