Yes, but why? [Power / Sample Size]

posted by Helmut Homepage – Vienna, Austria, 2017-12-28 12:47 (2677 d 07:52 ago) – Posting: # 18112
Views: 35,600

Hi kms,

❝ These are the results i'm finding maximum times.


THX for providing the data. I guess you mean maximum concentrations?
When doubting your calculations I stand corrected!

library(PowerTOST)
# assumed
CV.0     <- 0.2 # CV-intra
theta0.0 <- 1.1 # T/R-ratio
target   <- 0.8 # desired (target) power
dor      <- 10  # expected dropout rate in percent
# observed
CV.1     <- 0.1836
theta0.1 <- 0.96
n        <- 34
# sample size and power
plan     <- sampleN.TOST(CV=CV.0, theta0=theta0.0, targetpower=target)
# Adjust for expected dropout rate and round up to next even
N.ad     <- ceiling(plan[["Sample size"]]/(1-dor/100)/2)*2
# power for various number of dropouts #
elig     <- seq(N.ad, 2*plan[["Sample size"]]-N.ad, -1)
pwr1     <- data.frame(dosed=rep(N.ad, length(elig)),
                       dropouts=N.ad-elig, eligible=elig, power=NA)
for (j in seq_along(elig)) {
  pwr1$power[j] <- suppressMessages(power.TOST(CV=CV.0, theta0=theta0.0,
                                               n=elig[j]))
}
cat("Ante hoc power for various number of dropouts:\n"); print(signif(pwr1, 4), row.names=FALSE)
cat("Post hoc power:", signif(power.TOST(CV=CV.1, theta0=theta0.1, n=n), 4), "\n")

Gives:

+++++++++++ Equivalence test - TOST +++++++++++
            Sample size estimation
-----------------------------------------------
Study design:  2x2 crossover
log-transformed data (multiplicative model)

alpha = 0.05, target power = 0.8
BE margins = 0.8 ... 1.25
True ratio = 1.1,  CV = 0.2

Sample size (total)
 n     power
32   0.810068 ✔

Ante hoc power for various number of dropouts:
 dosed dropouts eligible  power
    36        0       36 0.8505
    36        1       35 0.8409
    36        2       34 0.8314
    36        3       33 0.8207
    36        4       32 0.8101
    36        5       31 0.7982
    36        6       30 0.7864
    36        7       29 0.7732
    36        8       28 0.7601

Post hoc power: 0.9917 ✔

Modifying my simulation code for theta0=1/1.1 and n=34 I got:
1e+05 simulated studies with “post hoc” power of
  ≥ target    : 59.75%
  ≥ achieved  : 57.18%
  ≥ 0.90      : 31.51%
  ≥ 0.95      : 15.18%
  [0.95, 0.99]: 12.59%
  ≥ 0.9917    :  2.11%

[image]

Hence, such a high power is unlikely but possible.

As usual power is more sensitive to changes in the GMR than in the CV:
pwr.0 <- power.TOST(CV=0.2, theta0=1/1.1, n=34)
cat("Relative increase in power due to",
    "\n  better CV :", sprintf("%5.2f%%",
    100*(power.TOST(CV=0.1836, theta0=1/1.1, n=34)-pwr.0)/pwr.0),
    "\n  better GMR:", sprintf("%5.2f%%",
    100*(power.TOST(CV=0.2, theta0=0.96, n=34)-pwr.0)/pwr.0),
    "\n  both      :", sprintf("%5.2f%%",
    100*(power.TOST(CV=0.1836, theta0=0.96, n=34)-pwr.0)/pwr.0), "\n")

Relative increase in power due to
  better CV :  6.16%
  better GMR: 17.95%
  both      : 19.28%

One question remains open:

❝ Can you explain to us why you performed a “posthoc analysis” at all? What did you want to achieve? To repeat ElMaestro:

❝ ❝ ❝ ❝ Try and ask yourself which question post-hoc power actually answers. Try and formulate it in a very specific sentence.

:confused:
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