Bioequivalence and Bioavailability Forum • sampleN.NTID(): Example

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

posted by Helmut – Vienna, Austria, 2022-05-18 16:30 (1073 d 11:43 ago) – Posting: # 23002
Views: 5,405

Hi pharm07,

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

In your second script you forgot to state theta0 = 0.95. Hence, the default theta = 0.975 was employed.

library(PowerTOST)

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      <- c(0.045, 0.07)      # First element CVwT, second CVwR

do.rate <- 0.30                # Anticipated dropout-rate 30%

n       <- sampleN.NTID(CV = CV, theta0 = 0.95, targetpower = 0.90,

                        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(

                      power.NTID(CV = CV, theta0 = 0.95, n = df$eligible[j]))

}

print(df, row.names = FALSE)



 dosed eligible dropouts   power

   146      146     0.0% 0.97046

   146      145     0.7% 0.96815

   146      144     1.4% 0.96887

   146      143     2.1% 0.96730

   146      142     2.7% 0.96592

   146      141     3.4% 0.96512

   146      140     4.1% 0.96470

   146      139     4.8% 0.96316

   146      138     5.5% 0.96322

   146      137     6.2% 0.96172

   146      136     6.8% 0.96048

   146      135     7.5% 0.95969

   146      134     8.2% 0.95892

   146      133     8.9% 0.95649

   146      132     9.6% 0.95497

   146      131    10.3% 0.95505

   146      130    11.0% 0.95347

   146      129    11.6% 0.95245

   146      128    12.3% 0.95101

   146      127    13.0% 0.94969

   146      126    13.7% 0.94851

   146      125    14.4% 0.94723

   146      124    15.1% 0.94594

   146      123    15.8% 0.94386

   146      122    16.4% 0.94258

   146      121    17.1% 0.94121

   146      120    17.8% 0.94039

   146      119    18.5% 0.93931

   146      118    19.2% 0.93649

   146      117    19.9% 0.93461

   146      116    20.5% 0.93393

   146      115    21.2% 0.93164

   146      114    21.9% 0.92946

   146      113    22.6% 0.92741

   146      112    23.3% 0.92519

   146      111    24.0% 0.92361

   146      110    24.7% 0.92132

   146      109    25.3% 0.91851

   146      108    26.0% 0.91725

   146      107    26.7% 0.91563

   146      106    27.4% 0.91393

   146      105    28.1% 0.91186

   146      104    28.8% 0.90848

   146      103    29.5% 0.90711

   146      102    30.1% 0.90354

   146      101    30.8% 0.90250

   146      100    31.5% 0.89851

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Helmut Schütz

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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(): ExampleHelmut 2022-05-18 14:30
        
        sampleN.NTID(): Example pharm07 2022-05-19 05:36