naive questions regarding new functions in Power2Stage [Two-Stage / GS Designs]

posted by mittyri – Russia, 2018-04-28 15:54  – Posting: # 18737
Views: 7,403

(edited by mittyri on 2018-04-28 16:08)

Hi Helmut,

sorry for naive questions raised from my hazelnut brain

1. I'm trying to compare the old function
power.tsd(method = c("B", "C", "B0"), alpha0 = 0.05, alpha = c(0.0294, 0.0294),
          n1, GMR, CV, targetpower = 0.8, pmethod = c("nct", "exact", "shifted"),
          usePE = FALSE, Nmax = Inf, min.n2 = 0, theta0, theta1, theta2,
          npct = c(0.05, 0.5, 0.95), nsims, setseed = TRUE, details = FALSE)

with a new one
power.tsd.in(alpha, weight, max.comb.test = TRUE, n1, CV, targetpower = 0.8,
             theta0, theta1, theta2, GMR, usePE = FALSE, min.n2 = 4, max.n = Inf,
             fCpower = targetpower, fCrit = "CI", fClower, fCupper, fCNmax,
             ssr.conditional = c("error_power", "error", "no"),
             pmethod = c("nct", "exact", "shifted"), npct = c(0.05, 0.5, 0.95),
             nsims, setseed = TRUE, details = FALSE)


So the old function was nice since the user can choose the method or specify 3 alphas.
In the new one I see the comment regarding alpha
If one element is given, the overall one-sided significance level. If two elements are given, the adjusted one-sided alpha levels for stage 1 and stage 2, respectively.
If missing, defaults to 0.05.

What about alpha0 for method C? Is it deprecated?

2. Why did you decide to include CI futility rule by default?

3. Regarding your flowchart:
isn't it possible that we get some value lower than 4?
power.tsd.in(CV=0.13, n1=12)
<...>
p(BE)    = 0.91149
p(BE) s1 = 0.83803
Studies in stage 2 = 9.71%

Distribution of n(total)
- mean (range) = 12.5 (12 ... 42)
- percentiles
 5% 50% 95%
 12  12  16

for example and after first stage CV=15%, CI=[0.7991897 1.0361745]:
sampleN2.TOST(CV=0.15, n1=12)
 Design  alpha   CV theta0 theta1
    2x2 0.0294 0.15   0.95    0.8
 theta2 n1 Sample size
   1.25 12           2
 Achieved power Target power
        0.82711          0.8


4. Is it possible to update the docs attached to the library?

5. I was confused with "2stage" 'aliased' with "tsd" and was looking for differences some time
Are there any reasons to double that functions?

PS:
regarding 3rd point:
I tried
interim.tsd.in(GMR1=sqrt(0.7991897 * 1.0361745), CV1=0.15,n1=12, fCrit="No")
TSD with 2x2 crossover
Inverse Normal approach
 - maximum combination test with weights for stage 1 = 0.5 0.25
 - significance levels (s1/s2) = 0.02635 0.02635
 - critical values (s1/s2) = 1.93741 1.93741
 - BE acceptance range = 0.8 ... 1.25
 - Observed point estimate from stage 1 is not used for SSR
 - with conditional error rates and conditional (estimated target) power

Interim analysis of first stage
- Derived key statistics:
  z1 = 1.87734, z2 = 3.54417,
  Repeated CI = (0.79604, 1.04028)
- No futility criterion met
- Test for BE not positive (not considering any futility rule)
- Calculated n2 = 4
- Decision: Continue to stage 2 with 4 subjects

oh, there's a default argument min.n2 = 4
OK, let's try to change that:
> interim.tsd.in(GMR1=sqrt(0.7991897 * 1.0361745), CV1=0.15,n1=12, fCrit="No", min.n2 = 2)
Error in interim.tsd.in(GMR1 = sqrt(0.7991897 * 1.0361745), CV1 = 0.15,  :
  min.n2 has to be at least 4.

Why couldn't I select a smaller one?

Kind regards,
Mittyri

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