An evaluation of increasing sample size based on conditional power.
We evaluate properties of sample size re-estimation (SSR) designs similar to the promising zone design considered by Mehta and Pocock (2011). We evaluate these designs under the assumption of a true effect size of 1.1 down to 0.4 of the protocol-specified effect size by six measures: 1. The probability of a sample size increase, 2. The mean proportional increase in sample size given an increase; 3 and 4. The mean true conditional power with and without a sample size increase; 5 and 6. The expected increase in sample size and power due to the SSR procedure. These measures show the probability of a sample size increase and the cost/benefit for given true effect sizes, particularly when the SSR may either be pursuing a small effect size of little clinical importance or be unnecessary when the true effect size is close to the protocol-specified effect size. The results show the clear superiority of conducting the SSR late in the study and the inefficiency of a mid-study SSR. The results indicate that waiting until late in the study for the SSR yields a smaller, better targeted set of studies with a greater increase in overall power than a mid-study SSR.
a Statistical Research, Pfizer Inc , New York , New York , USA.
Michael
M
Gaffney
b Biostatistics, Harvard School of Public Health , Boston , Massachusetts , USA.
James H
JH
Ware
D035843:Biomedical Research / Q000706:statistics & numerical data*; D015987:Effect Modifier, Epidemiologic; D006801:Humans; D018401:Sample Size
Adaptive design; conditional power; sample size re-estimation