# Sample size for AUC based on mean and SD of raw data

I'm trying to estimate the sample size required to achieve 80% power at a 5% significance level for a superiority study comparing AUC from bioavailability data.

My challenge is the only previous pilot study I found reported mean AUC and standard errors for the raw data and using this with pwr.t.test in R gives the following error.

Error in uniroot(function(n) eval(p.body) - power, c(2 + 1e-10, 1e+09)) :
f() values at end points not of opposite sign


Below are means(SE) for the two groups from the previous study:

Group A: 6300(35.6); n=6

Group B: 16750(57.9); n=6

Here's the code I run:

library(pwr)
pwr.t.test(n = NULL , d = 119.84, sig.level =0.05 , power = 0.8, type = "two.sample")


Log transformation should be the way to go I reckon, but I'm not sure how to derive the log SD from the raw SD. Any help will be much appreciated.