theta0 within PE-constraints [theta1, theta2] [RSABE / ABEL]
❝ How to calculate scABEL.ad (alpha = 0.05, CV = 0.3043689, design = "2x3x3", theta0 = 0.77058, regulator = "EMA", n = c (15,15,15)) if the point estimate is not in [0.80 ; 1.25] (SWR = 0.298 [0.79755; 1.25385])?
Note the error message thrown by
scABEL.ad()
:library(PowerTOST)
scABEL.ad(alpha=0.05, CV=0.3043689, design="2x3x3", theta0=0.77058,
regulator="EMA", n=c(15,15,15))
Error in scABEL.ad(alpha = 0.05, CV = 0.3043689, design = "2x3x3", theta0 = 0.77058, :
theta0 must be within [theta1, theta2]
See the man-page of
scABEL.ad()
:theta0
‘True’ or assumed bioavailability ratio. Defaults to 0.90 if not given explicitly.
theta1
Conventional lower ABE limit to be applied in the mixed procedure if CVwR==CVswitch
. Also lower limit for the point estimate constraint. Defaults to 0.80 if not given explicitly.
theta2
Conventional upper ABE limit to be applied in the mixed procedure if CVwR==CVswitch
. Also upper limit for the point estimate constraint. Defaults to 1.25 if not given explicitly.
The purpose of
scABEL.ad()
is to iteratively adjust α to control the Type I Error either before the study (and see how power is affected with the argument details=TRUE
) or post hoc when the T/R-ratio is already known. In both cases ABEL cannot be shown if theta0
would be outside the PE-constraints theta1
, theta2
(0.80, 1.25). Hence, modifying your example the most extreme case (theta0=theta1=1/theta2
) would be:library(PowerTOST)
scABEL.ad(alpha=0.05, CV=0.3043689, design="2x3x3", theta0=0.8,
regulator="EMA", n=c(15,15,15))
+++++++++++ scaled (widened) ABEL ++++++++++++
iteratively adjusted alpha
(simulations based on ANOVA evaluation)
----------------------------------------------
Study design: 2x3x3 (TRR|RTR|RRT)
log-transformed data (multiplicative model)
1,000,000 studies in each iteration simulated.
CVwR 0.3044, n(i) 15|15|15 (N 45)
Nominal alpha : 0.05
True ratio : 0.8000
Regulatory settings : EMA (ABEL)
Switching CVwR : 0.3
Regulatory constant : 0.76
Expanded limits : 0.7975 ... 1.2538
Upper scaling cap : CVwR > 0.5
PE constraints : 0.8000 ... 1.2500
Empiric TIE for alpha 0.0500 : 0.06805
Power for theta0 0.8000 : 0.076
Iteratively adjusted alpha : 0.03616
Empiric TIE for adjusted alpha: 0.05000
Power for theta0 0.8000 : 0.056
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Science Quotes
Complete thread:
- alpha correction Yura 2017-12-08 08:48 [RSABE / ABEL]
- theta0 within PE-constraints [theta1, theta2]Helmut 2017-12-08 12:33
- theta0 within PE-constraints [theta1, theta2] Yura 2017-12-08 12:55
- theta0 within PE-constraints [theta1, theta2]Helmut 2017-12-08 12:33