Bioequivalence and Bioavailability Forum

MGR ★ India, 2014-05-22 09:45 (3991 d 07:16 ago) Posting: # 12981 Views: 12,438	SAS code for 2-stage design [Two-Stage / GS Designs] Post reply
	Hi All, We are going for a 2stage approach for EMA using Potvin B method. Is this type of analysis can be done by WinNonlin 5.2 or 6.3? Moreover is there any articles/guidelines for the SAS code? Thanks in advance, — Regards, MGR

d_labes
★★★

Berlin, Germany,
2014-05-22 10:17
(3991 d 06:44 ago)

@ MGR
Posting: # 12982
Views: 11,330

SAS code for 2-stage design

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Dear MGR,

❝ We are going for a 2stage approach for EMA using Potvin B method.

Welcome to the club :cool:

❝ Is this type of analysis can be done by WinNonlin 5.2 or 6.3?

Do not have this software, but I think Helmut has posted here about this.
You may find some material in his lectures also, some decided to the 2-stage evaluation.

❝ Moreover is there any articles/guidelines for the SAS code?

AFAIK there is no decided article/guideline containing SAS code in this respect. But it should not that hard to code it:

For stage 1 use the code for classical ABE for a 2x2 cross-over, but instead of calculating 90% CIs derive the CIs to the confidence level (1-2*0.0294) = 94.12%.
The power inspection step and the sample size estimation are a little bit hairy in SAS. Don't really know if Proc Power can be used for that. I suggest to use some tool external to SAS. You may guess what I recommend :-D

(if you ever have seen or heard about): R-package PowerTOST.

For stage 2 evaluation (if a second stage becomes necessary) use a model with the effects:

treatment
stage
period within stage
sequence
stage*sequence interaction (according to EMA Q&A)
subject nested within sequence and stage

and derive again 94.12% CIs.

Here my SAS code for that:

Proc GLM data=yourdata;

  class treatment period stage sequence subject;

  model &lnval = treatment stage period(stage) sequence 

                 stage*sequence /*acc. to EMA Q&A*/ 

                 subject(sequence*stage) / CLparm alpha=0.0588 ss3;

  ODS output estimates=_est2;

  estimate 'T-R' treatment -1 1; 

  where stage in(1 2);

run; quit;

That is it. Not so difficult, isn't it?
Hope this helps.

BTW: There is a new revision of the EMA Q&A quite recently (09-May-2014) out there:
EMA/618604/2008 Rev. 9

—
Regards,

Detlew

Helmut
★★★

Vienna, Austria,
2014-05-22 14:24
(3991 d 02:37 ago)

@ MGR
Posting: # 12986
Views: 12,397

Phoenix/WinNonlin code

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Hi MGR,

❝ Is this type of analysis can be done by WinNonlin 5.2 or 6.3?

WinNonlin 5.2 is history (released in 2007, end of support of v5.2.1 was already in June 2011). In the following the procedure in Phoenix/WinNonlin’s 6.3 BE tool (EMA’s all fixed effects method and the additional term in the second stage according to the Q&A-document Rev.7) and Example 2 from Potvin’s paper:

Stage 1
Map variables as usual.
Navigate to [Options] and change the Confidence Level to 94.12%.
Navigate to [Random 1] and delete Subject(Sequence).
Navigate to [Fixed Effects], modify the model specification to Sequence+Treatment+Period+Subject(Sequence).
You should get:
Ratio_%Ref: 108.76 CI_User_Lower: 92.93 CI_User_Upper: 127.28
With this coding we don’t get the intrasubject-CV in the output (I think it will be available in the next release this summer).
Output Data → Final Variance Parameters → Send To → Data → Data Wizard:
Add Transformation → Custom → New Column Name: CVintra → Formula: sqrt(exp(Estimate)-1)
You should get:
CVintra: 0.1821 (=18.21%)
Stage 2
Map variables as usual; additionally map Stage to Classification.
Navigate to [Options] and change the Confidence Level to 94.12%.
Navigate to [Random 1] and delete Subject(Sequence).
Navigate to [Fixed Effects], modify the model specification to Stage+Sequence+Sequence*Stage+Sequence*Stage*Subject+Period(Stage)+Treatment.
You should get:
Ratio_%Ref: 101.45 CI_User_Lower: 88.45 CI_User_Upper: 116.38

These results agree with the ones reported by Potvin et al.

Intermediate power after stage 1 and sample size estimation for stage 2 in [image]

with PowerTOST:

library(PowerTOST) power.TOST(alpha=0.0294, theta0=0.95, CV=0.1821, n=12, design='2x2')
You should get:
[1] 0.5253565
Since 52.54% < 80%, initiate the second stage (if ≥80% stop; study failed to show BE).
sampleN.TOST(alpha=0.0294, targetpower=0.80, theta0=0.95, CV=0.1821, design='2x2')
You should get:
+++++++++++ Equivalence test - TOST +++++++++++ Sample size estimation ----------------------------------------------- Study design: 2x2 crossover log-transformed data (multiplicative model) alpha = 0.0294, target power = 0.8 BE margins = 0.8 ... 1.25 Null (true) ratio = 0.95, CV = 0.1821 Sample size (total) n power 20 0.829292
Perform the second stage in 20–12=8 subjects like in the example.

BTW, recently it was shown* that EMA’s modification of the model is irrelevant for the BE assessment – which is not surprising, since Sequence*Stage is a between-subject term. If anybody has a clue why EMA introduced it, please enlighten me. Furthermore, there is not difference in results irrespective whether subjects are treated as a fixed or random effect (unless T/R >1.20).

Karalis V, Macheras P. On the Statistical Model of the Two-Stage Designs in Bioequivalence Assessment. J Pharm Pharmacol. 2014;66(1):48–52. doi 10.1111/jphp.12164

—
Dif-tor heh smusma 🖖🏼 Довге життя Україна!
Helmut Schütz

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yjlee168
★★★

Kaohsiung, Taiwan,
2014-05-23 23:17
(3989 d 17:44 ago)

(edited on 2014-05-24 08:35)
@ Helmut
Posting: # 13001
Views: 10,899

maybe R too?

Post reply

Dear Detlew & Helmut,

Sorry to cut in. Thank you both for the great summary of the two-stage approach. It sounds like that it is possible to implement all these using R or into bear together with PowerTOST.

❝ [...]

❝ You should get:

❝ CVintra: 0.1821 (=18.21%)

OK, the example is from WLN. CV_intra = 18.21% (for C_max?) with 12 subjects. So it is not a HVD, is it? Does EMA or FDA accept the two-stage BE if it is the case?

❝ [...]

❝ sampleN.TOST(alpha=0.0294, targetpower=0.80, theta0=0.95, CV=0.1821, design='2x2')

Nice explanations and thank you both again.

—
All the best,
-- Yung-jin Lee
bear v2.9.2:- created by Hsin-ya Lee & Yung-jin Lee
Kaohsiung, Taiwan https://www.pkpd168.com/bear
Download link (updated) -> here

Helmut
★★★

Vienna, Austria,
2014-05-24 00:39
(3989 d 16:22 ago)

@ yjlee168
Posting: # 13002
Views: 11,020

RSABE/ABEL in a TSD: Science fiction

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Hi Yung-jin,

❝ […] It sounds like that it is possible to implement all these using R or into bear together with PowerTOST.

Yes, why not? ;-)

❝ OK, the example is from WLN.

The example is from Potvin’s paper.

❝ CV_intra = 18.21% (for C_max?) with 12 subjects.

They didn’t state which PK metric the data covers.

❝ So it is not a HVD, isn't it?

The CV of the pooled data set (n=20) is 21.67%. So HVD is unlikely.

❝ Does EMA or FDA accept the two-stage BE if it is the case?

Hopefully only in such cases (conventional unscaled ABE). I have heard that some people already ~~naïvely~~ unreflecting submitted protocols in the EU with replicate designs for ABEL, believing that α_adj 0.0294 would maintain the overall risk type I ≤0.05. Crap.

—
Dif-tor heh smusma 🖖🏼 Довге життя Україна!
Helmut Schütz

The quality of responses received is directly proportional to the quality of the question asked. 🚮
Science Quotes

MGR
★

India,
2014-07-02 09:08
(3950 d 07:53 ago)

@ Helmut
Posting: # 13199
Views: 10,702

WinNonlin 5.2.1 setup

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Hi Helmut,

Here in our company WinNonlin 5.2.1 is installed, So i have to do analysis using the same version. Done the analysis, but the problem is that, 94.12% confidence interval values are coming same when subject(sequence) is treated as fixed effect and another with random effect.

Could be please explain why this happened?

I have attached the screen shot of the settings used.

Thank you in advance.

[image]

Edit: Subject line changed. [Helmut]

—
Regards,
MGR

Helmut
★★★

Vienna, Austria,
2014-07-02 12:40
(3950 d 04:21 ago)

@ MGR
Posting: # 13201
Views: 10,695

outdated software; mixed vs. fixed effects

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Hi MGR,

❝ Here in our company WinNonlin 5.2.1 is installed, So i have to do analysis using the same version.

I don’t know how inspectors would judge the fact that your company is using an obsolete software (which is no more supported by Pharsight since 2011). The upgrade to Phoenix was free of charge for years. Talk to your management!

❝ Done the analysis, but the problem is that, 94.12% confidence interval values are coming same when subject(sequence) is treated as fixed effect and another with random effect.

As expected…

❝ Could be please explain why this happened?

The CI is calculated from the geometric mean ratio, the residual error, and the degrees of freedom (in a 2×2 cross-over n–2, where n is the total number of subjects). These values are identical in both models.¹ My results for the first stage of Potvin’s Example 2, Method B in Phoenix / WinNonlin 6.3 – agreeing with what the authors reported:

Fixed Sequence + Treatment + Period Random Subject(Sequence)

Ratio: 108.76% 94.12% CI: 92.93 – 127.28%
Fixed Sequence + Treatment + Period + Subject(Sequence)

Ratio: 108.76% 94.12% CI: 92.93 – 127.28%

“Philosophically” speaking treating subjects as a random effect allows prediction of the population’s expected response based on the study’s sample. Treating subjects as a fixed effect makes only a statement about subjects in this particular study. See also this post.

EMEA. The European Medicines Evaluation Agency.
The drug regulatory agency of the European Union.
A statistician-free zone. Stephen Senn (2004)

However, for the EMA you have to stick to the all fixed effects model with Subject(Sequence).² My experiences from a last year’s MRP (mixed effects model, Method C passed BE in the first stage): Accepted by the RMS Germany; no comments from the CMS Austria, Denmark, Sweden, and The Netherlands. Nonetheless, Spain:

“Statistical analysis should be GLM. Please justify.”

Can you guess the outcome of the all fixed effects model presented in the response letter?

Identical both for balanced (n₁=n₂) and imbalanced (n₁≠n₂) studies. However, different results for incomplete data (missing periods). In SAS-lingo Proc GLM ≠ Proc Mixed. Not an issue here (according to the EMA incomplete subjects have to be excluded in 2×2 cross-over studies).
For aficionados: ElMaestro does not get tired pointing out that subjects are unambiguously coded to sequences in cross-over BE studies. Therefore, nesting Subjects into Sequence is superfluous. Homework: Try what happens with the more simple Sequence + Treatment + Period + Subject.

—
Dif-tor heh smusma 🖖🏼 Довге життя Україна!
Helmut Schütz

The quality of responses received is directly proportional to the quality of the question asked. 🚮
Science Quotes

MGR ★ India, 2014-07-03 08:32 (3949 d 08:29 ago) @ Helmut Posting: # 13206 Views: 10,404	outdated software; mixed vs. fixed effects Post reply
	Hi HS, Thank you very much for the explanation — Regards, MGR

SAS code for 2-stage design [Two-Stage / GS Designs]

SAS code for 2-stage design

Phoenix/WinNonlin code

maybe R too?

RSABE/ABEL in a TSD: Science fiction

WinNonlin 5.2.1 setup

outdated software; mixed vs. fixed effects

outdated software; mixed vs. fixed effects