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Thread: Sample size with G power

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    Sample size with G power




    Hello

    I ran some sample size calculations using Gpower, in order to find the minimum required sample size for the Fisher exact test, since I need to compare proportions between two groups, with rare events.

    This is the output I got:

    Exact - Proportions: Inequality, two independent groups (Fisher's exact test)
    Options: Exact distribution
    Analysis: A priori: Compute required sample size
    Input:
    Tail(s) = One
    Proportion p1 = 0.1
    Proportion p2 = 0.02
    α err prob = 0.05
    Power (1-β err prob) = 0.8
    Allocation ratio N2/N1 = 1
    Output:
    Sample size group 1 = 124
    Sample size group 2 = 124
    Total sample size = 248
    Actual power = 0.8019416
    Actual α = 0.0114122

    I noticed that the actual α is much lower than 0.05. Does it mean that I can choose a larger α, without crossing the 5% type I error probability ? Will regulation bodies like FDA accept this "trick" ?
    (am trying to reduce the sample size from 124 per group to around 100)

  2. #2
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    Re: Sample size with G power

    one more thing I found weird, and I guess you guys won't be able to explain it to me, when I ran this calculation with SAS, I got n=123 per group, and when I tried PASS, I got n=132

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    Re: Sample size with G power


    Hi, NN_Power,

    Typically when you see differences between statistical packages, it often stems from differences in default options. Note that in G*power, you've selected a one-sided test: in SAS 9.2 PROC POWER, the default is a two-sided test. I haven't used PASS, so I can't attest to its default options. Since the difference between G*Power and SAS is N=1, I'd imagine that difference may be round off error. If the options specified are the same and PASS still gives you a different result, the program may be using another approximation which is more conservative.

    If you do a simulation, you will actually find that Fisher's Exact Test may be a bit conservative: the real type I error rate is actually below the nominal rate. This is why there's a difference between the nominal rate you entered (0.05) and the actual rate (0.011). You could raise your nominal type I error rate while preserving the actual type I error rate, but I don't know if regulatory agencies will accept such practices.

    Hope that helps.

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