Bioequivalence and Bioavailability Forum

PowerTOST: sampleN.NTID() [Power / Sample Size]

posted by pharm07 – India, 2022-05-18 07:18 (702 d 03:41 ago) – Posting: # 22995
Views: 1,849

Hi Helmut,

❝ Although you could give values of theta1 and theta2, for the FDA keep the defaults of 0.8 and 1.25 (i.e., don’t specify anything): Additionally to passing RSABE and the variance-comparison you must pass conventional ABE.

OK. Noted.

❝ More examples are given there.

OK. I referred these examples.

❝ See the two supportive functions in the section about dropouts in the named article.

Ok, I have pasted one example which i seems to be work upon.

❝ That’s possible if you specify a low or high theta0.

OK.

❝ Yes, you are not alone.

❝ I can’t till you post an example which you consider problematic.

Please see below example,
Note : CV is not in scalar form.'

sampleN.NTID(CV = c(0.045,0.07), theta0 = 0.95, targetpower = 0.9)

+++++++++++ FDA method for NTIDs ++++++++++++
           Sample size estimation
---------------------------------------------
Study design:  2x2x4 (TRTR|RTRT)
log-transformed data (multiplicative model)
1e+05 studies for each step simulated.

alpha  = 0.05, target power = 0.9
CVw(T) = 0.045, CVw(R) = 0.07
True ratio     = 0.95
ABE limits     = 0.8 ... 1.25
Implied scABEL = 0.9290 ... 1.0764
Regulatory settings: FDA
- Regulatory const. = 1.053605
- 'CVcap'           = 0.2142

Sample size search
 n     power
92   0.875810
94   0.882090
96   0.888810
98   0.894060
100   0.898510
102   0.903540

# with 30% DO rate,

# as CV was specified as Vector,
# is following steps right?
balance <- function(n, n.seq) {
  # Round up to obtain balanced sequences
  return(as.integer(n.seq * (n %/% n.seq + as.logical(n %% n.seq))))
}
nadj <- function(n, do.rate, n.seq) {
  # Round up to compensate for anticipated dropout-rate
  return(as.integer(balance(n / (1 - do.rate), n.seq)))
}
CV      <- 0.045               # Assumed CV # how to specify vector CV here?
do.rate <- 0.30                # Anticipated dropout-rate 30%
n       <- sampleN.NTID(CV = CV, print = FALSE, details = FALSE)[["Sample size"]]
dosed   <- nadj(n, do.rate, 2) # Adjust the sample size
df      <- data.frame(dosed = dosed, eligible = dosed:(n - 2))
for (j in 1:nrow(df)) {
  df$dropouts[j] <- sprintf("%.1f%%", 100 * (1 - df$eligible[j] / df$dosed[j]))
  df$power[j]    <- suppressMessages( # We know that some are unbalanced
                      power.NTID(CV = CV, n = df$eligible[j]))
}
print(df, row.names = FALSE)
dosed eligible dropouts   power
   58       58     0.0% 0.92040
   58       57     1.7% 0.91575
   58       56     3.4% 0.91254
   58       55     5.2% 0.90722
   58       54     6.9% 0.90364
   58       53     8.6% 0.89761
   58       52    10.3% 0.89257
   58       51    12.1% 0.88767
   58       50    13.8% 0.88215
   58       49    15.5% 0.87672
   58       48    17.2% 0.86952
   58       47    19.0% 0.86376
   58       46    20.7% 0.85690
   58       45    22.4% 0.84922
   58       44    24.1% 0.84310
   58       43    25.9% 0.83486
   58       42    27.6% 0.82810
   58       41    29.3% 0.81915
   58       40    31.0% 0.81109
   58       39    32.8% 0.80105
   58       38    34.5% 0.79410

Kindly guide me with this example, i want to check whether i am making a mistake or not.

Complete thread:

• Estimation of sample size for NIT using ISCV By R pharm07 2022-05-03 11:23 
  • Estimation of sample size for NTI using ISCV By R dshah 2022-05-03 13:04
  • PowerTOST Helmut 2022-05-03 14:38
    • PowerTOST pharm07 2022-05-04 08:08
      • PowerTOST Helmut 2022-05-04 10:33
        • PowerTOST pharm07 2022-05-04 14:54
          • PowerTOST pharm07 2022-05-11 05:30
            • PowerTOST: sampleN.NTID() Helmut 2022-05-11 13:19
              • PowerTOST: sampleN.NTID()pharm07 2022-05-18 05:18
                • sampleN.NTID(): Example Helmut 2022-05-18 14:30
                  • sampleN.NTID(): Example pharm07 2022-05-19 05:36