Ngrp Samp Script Download Pdf
Katerine Aldrige
With sampleN.TOST(..., print = FALSE) results areprovided as a data frame3 with nine columns Design,alpha, CV, theta0,theta1, theta2, Sample size,Achieved power, and Target power.
To access e.g., the sample size use eithersampleN.TOST(...)[7] orsampleN.TOST(...)[["Sample size"]]. We suggest to use thelatter in scripts for clarity.4
Note that the sample size is always rounded up to give balancedsequences (here a multiple of two). Since power is higher than ourtarget, likely this was the case here. Let us check that.
Which power will we get with a sample size of 39?
As another option (e.g., if the blood volume is limitedand/or there are concerns about a higher dropout-rate in amultiple-period study) we could stay with the 222 crossover but splitthe sample size into groups. In Europe (and for the FDA if certainconditions5 are fulfilled), there are no problemspooling the data and use the conventional model.
Since we have more terms in the model, we will loose some degrees offreedom. Let us explore in simulations how that would impact power. Bydefault function power.TOST.sds() performs 100,000simulations.
In the basic approach we concentratedmainly on the uncertainty of the CV. But this is not the end ofthe story. Clearly \(\small\theta_0\)is uncertain as well. With the function expsampleN.TOST()we can dive deeper into this matter. Let us start with the CVonly.
This sample size is almost twice the 28 your boss got from a popularExcel-Sheet.9 If you are not fired right away whensuggesting such a study, take it as a warning what mighthappen.
At least, if the pivotal study is performed in a lower sample size andfails, you know why.
Nevertheless, exploring power is useful when trying to understand whya study failed and to plan another study. Let us continue with the example from above. Ignoring our concerns, themanagement decided to perform the pivotal study with 28 subjects. TheT/R-ratio was slightly worse (0.90), the CV higher (0.25), andwe had one dropout in the first sequence and two in the second. Thefunction CI.BE() comes handy.
When planning the next study one can use the entire arsenal from above. Since we have more accurate estimates (from 25subjects instead of the 16 of the pilot) the situation is more clearnow.
As a further step we can take the information of both studies intoaccount with the function CVpooled().
This is an apples-and-oranges comparison. Red squares showCVs which were above the upper CL of the pooled CV. Given,only in two studies (#1, #6) their lower CL did not overlap the upper one of thepooled CV.
The method is exact if the subjects/sequence are known. In theliterature quite often only the total sample size is given and thefunction tries to keep sequences as balanced as possible. What if thestudy was imbalanced?
A total sample size of 26 was reported. The study was either balanced orimbalanced to an unknown degree: