Bioequivalence and Bioavailability Forum

ElMaestro ★★★ Denmark, 2020-08-14 22:54 (1715 d 14:35 ago) Posting: # 21882 Views: 5,140	EMA dataset I, can someone post results? [General Statistics] Post reply
	Hi all, can someone post REML results for dataset I of EMA? For example a WNL output and/or SAS output of variance components and fixed effects. I will be using it to verfiy my own scripts in R, or at least do some direct comparison. Thanks in advance if possible. — Pass or fail! ElMaestro

PharmCat
★

Russia,
2020-08-15 00:09
(1715 d 13:20 ago)

@ ElMaestro
Posting: # 21883
Views: 4,152

EMA dataset I, can someone post results?

Post reply

❝ Hi all,

Hi!

SPSS Code:


MIXED var1 BY sequence period formulation

  /CRITERIA=CIN(90) MXITER(8000) MXSTEP(200) SCORING(1) SINGULAR(0.0000000000001) HCONVERGE(0, 

    ABSOLUTE) LCONVERGE(0, ABSOLUTE) PCONVERGE(0.000000001, ABSOLUTE) 

  /FIXED=sequence period formulation  | SSTYPE(3) 

  /METHOD=REML

  /PRINT=G  

  /RANDOM=formulation  | SUBJECT(subject) COVTYPE(CSH) 

  /REPEATED=formulation  | SUBJECT(subject*period) COVTYPE(DIAG)

  /EMMEANS=TABLES(formulation) COMPARE ADJ(LSD).

Information Criteria


-2 Restricted Log Likelihood   530.145

Akaike's Information Criterion (AIC)   540.145

Hurvich and Tsai's Criterion (AICC)   540.354

Bozdogan's Criterion (CAIC)   563.528

Schwarz's Bayesian Criterion (BIC)   558.528

The information criteria are displayed in smaller-is-better form.

Estimates of Covariance Parametersa

      

Parameter      Estimate   Std. Error

Repeated Measures   

   Var: [formulation=1]   0.117394208   0.019006700

   Var: [formulation=2]   0.202118093   0.029362559

formulation [subject = subject]   

   Var: [formulation=1]   0.686257768   0.122572131

   Var: [formulation=2]   0.727599985   0.136391910

   CSH rho   1.000000000b   0.000000000

      

b This covariance parameter is redundant.


Random Effect Covariance Structure (G)

      [formulation=1] | subject   [formulation=2] | subject

[formulation=1] | subject   0.686257768   0.706626593

[formulation=2] | subject   0.706626593   0.727599985

Heterogeneous Compound Symmetry

Pairwise Comparisons

               

Mean Difference Std. Error   df        Sig.c   

(I-J)                                                90% CI Lower Bound   90% CI Upper Bound

                  

 0.145*            0.047         209.440         0.002         0.069         0.222

-0.145*            0.047         209.440         0.002        -0.222          -0.069

Julia code:


using ReplicateBE

julia> ReplicateBE.rbe!(df4, dvar = :var, subject = :subject, formulation = :formulation, period = :period, sequence = :sequence, g_tol = 1e-10)

       

Bioequivalence Linear Mixed Effect Model (status:converged)

Hessian not positive defined!

Rho ~ 1.0!



-2REML: 530.145    REML: -265.072



Fixed effect:

──────────────────────────────────────────────────────────────────────────────────────────

Effect           Value        SE          F           DF        t          P|t|

──────────────────────────────────────────────────────────────────────────────────────────

(Intercept)      7.77467      0.141753    3008.17     82.0026   54.8467    2.11367E-66*   

sequence: 2      0.0204707    0.197223    0.0107733   74.735    0.103795   0.91761        

period: 2        0.0446377    0.0636371   0.492019    214.16    0.701441   0.483789

period: 3        0.00659021   0.0644219   0.0104648   189.589   0.102298   0.918629

period: 4        0.0727201    0.0639714   1.29222     214.259   1.13676    0.256909

formulation: 2   -0.145464    0.0465012   9.78552     208.081   -3.12818   0.00201093*

──────────────────────────────────────────────────────────────────────────────────────────

Intra-individual variance:

formulation: 1   0.117394   CVᵂ:   35.29   %   

formulation: 2   0.202118   CVᵂ:   47.33   %



Inter-individual variance:

formulation: 1   0.686258

formulation: 2   0.7276

ρ:               1.0        Cov: 0.706627



formulation: 1 / formulation: 2

Ratio: 115.66, CI: 107.1 - 124.89 (%)

formulation: 2 / formulation: 1

Ratio: 86.46, CI: 80.07 - 93.37 (%)

ElMaestro ★★★ Denmark, 2020-08-15 00:34 (1715 d 12:55 ago) @ PharmCat Posting: # 21884 Views: 4,113	EMA dataset I, can someone post results? Post reply
	Thanks PharmCat - again, your help here is very valuable to me. — Pass or fail! ElMaestro

Helmut
★★★

Vienna, Austria,
2020-08-15 17:00
(1714 d 20:29 ago)

@ ElMaestro
Posting: # 21885
Views: 4,136

EMA dataset I (PHX/WNL 8.1)

Post reply

Hi ElMaestro,

second opinion:

Banded No-Diagonal factor Analytic (f=2)

FA0(2) 10 iterations -2REML LL 504.28342 AIC 526.28342 BIC 566.72771 lambda(1,1) 0.85299473 lambda(1,2) 0.8284068 lambda(2,2) 8.3425233E-07 Var(wR) 0.20211806 Var(wT) 0.11739425 Hypothesis Num_DF Denom_DF F p int 1 74.740719 6165.0288 0 sequence 1 74.725365 0.010773289 0.91761054 treatment 1 207.73496 9.785515 0.0020112901 period 3 192.94942 0.54470318 0.65228368 Diff Diff_SE DF 0.14546428 0.046501237 207.73496 PE 1.1565764 (90% CI: 1.0710441, 1.2489393)

Banded No-Diagonal factor Analytic (f=1)

FA0(1) 6 iterations -2REML LL 504.28342 AIC 524.28342 BIC 561.05095 lambda(1,1) 0.85299474 lambda(1,2) 0.82840679 Var(wR) 0.20211804 Var(wT) 0.11739424 Hypothesis Num_DF Denom_DF F p int 1 74.74072 6165.0288 0 sequence 1 74.725366 0.010773289 0.91761054 treatment 1 207.73498 9.7855157 0.0020112894 period 3 192.94944 0.54470322 0.65228366 Diff Diff_SE DF 0.14546428 0.046501236 207.73498 PE 1.1565764 (90% CI: 1.0710441, 1.2489393)

Heterogenous Compound Symmetry

CSH 8 iterations -2REML LL 504.28342 AIC 526.28282 BIC 566.72712 cshSD(1) 0.85274447 cshSD(2) 0.82831743 cshCorr 1.0005616 Var(wR) 0.20252423 Var(wT) 0.11754154 Hypothesis Num_DF Denom_DF F p int 1 71.172734 6165.1799 0 sequence 1 71.158119 0.010757919 0.91768302 treatment 1 75.532826 9.8154921 0.0024629865 period 3 111.51635 0.54613599 0.65176269 Diff Diff_SE DF 0.14545681 0.046427791 75.532826 PE 1.1565678 (90% CI: 1.0705169, 1.2495357)

A correlation >1 is funny.

Compound Symmetry

CS 4 iterations -2REML LL 504.51200 AIC 504.51200 BIC 561.28753 csDiag -0.00133219 csBlock 0.70154912 Var(wR) 0.20618088 Var(wT) 0.11635790 Hypothesis Num_DF Denom_DF F p int 1 75.415756 6210.7664 0 sequence 1 75.400233 0.010613701 0.91821838 treatment 1 76.346313 9.8473171 0.0024171494 period 3 113.68812 0.54662827 0.65141337 Diff Diff_SE DF 0.14515515 0.046256578 76.346313 PE 1.1562189 (90% CI: 1.0705101, 1.2487890)

Unstructured

UN 4 iterations -2REML LL 504.28282 AIC 526.28282 BIC 566.72712 un(1,1) 0.72717314 un(1,2) 0.70673981 un(2,2) 0.68610978 Var(wR) 0.20252423 Var(wT) 0.11754154 Hypothesis Num_DF Denom_DF F p int 1 74.737874 6165.1798 0 sequence 1 74.722533 0.010757919 0.91766913 treatment 1 75.567795 9.8154921 0.0024626145 period 3 111.56199 0.54613599 0.65176227 Diff Diff_SE DF 0.14545681 0.046427791 75.567795 PE 1.1565678 (90% CI: 1.0705174, 1.2495351)

EMA Method A

-2REML LL 218.14281 AIC 382.14281 BIC 659.29439 Var(w) 0.15999518 Hypothesis Num_DF Denom_DF F p int 1 217 109374.16 0 sequence 1 217 0.24365135 0.62208048 treatment 1 217 9.78364171 0.0020020741 period 3 217 0.78064221 0.50589993 subject(sequence) 75 217 17.844668 0 Diff Diff_SE DF 0.14547367 0.046508691 217 PE 1.1565873 (90% CI: 1.0710567, 1.2489481)

EMA Method B (residual df like SAS DDFM=CONTAIN)

VC 3 iterations -2REML LL 510.34005 AIC 526.34005 BIC 555.75408 Var(w) 0.16010032 Var(b) 0.70693799 Hypothesis Num_DF Denom_DF F p int 1 217 6161.8922 0 sequence 1 217 0.01197525 0.91296138 treatment 1 217 9.8646416 0.0019195216 period 3 217 0.82881025 0.47928487 Diff Diff_SE DF 0.14608818 0.046513007 217 PE 1.1572982 (90% CI: 1.0717074, 1.2497247)

EMA Method B (Satterthwaite’s df)

VC 3 iterations -2REML LL 510.34005 AIC 526.34005 BIC 555.75408 Var(w) 0.16010032 Var(b) 0.70693799 Hypothesis Num_DF Denom_DF F p int 1 74.737599 6161.8922 0 sequence 1 74.720846 0.01197525 0.91315361 treatment 1 216.93862 9.8646416 0.0019195905 period 3 217.11838 0.82881025 0.47928403 Diff Diff_SE DF 0.14608818 0.046513007 216.93862 PE 1.1572982 (90% CI: 1.0717073, 1.2497248)

—
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

PharmCat ★ Russia, 2020-08-16 02:57 (1714 d 10:32 ago) @ Helmut Posting: # 21886 Views: 4,102	EMA dataset I (PHX/WNL 8.1) Post reply
	Hi Helmut! ❝ -2REML LL 504.28342 I suppose Phoenix REML includes/exludes some constant. ❝ A correlation >1 is funny. I think CSH is most fair structure. But if otimize rho without limitaion or link function result cab be very srange

ElMaestro
★★★

Denmark,
2020-08-20 00:50
(1710 d 12:39 ago)

@ Helmut
Posting: # 21891
Views: 3,928

EMA dataset I (PHX/WNL 8.1)

Post reply

Hi Hötzi,

thanks for WNL outputs with various covariance structures.

❝ A correlation >1 is funny.

In a way you can compare it with the CoA of a reference standard which is labeled with purity = 101.2 %. It does not contain 101.2% - I guarantee it :-D

- but that may stil be the most viable estimate.
It is all about how we estimate / measure it. We are not guaranteed to get the same model maximum likelihood if you keep the rho controlled between (exactly) -1 to (exactly) 1 with any iterative method regardless of your tolerance setting on the x- or y-scale.

—
Pass or fail!
ElMaestro

Helmut
★★★

Vienna, Austria,
2020-08-20 03:05
(1710 d 10:24 ago)

@ ElMaestro
Posting: # 21892
Views: 3,969

EMA dataset I (PHX/WNL 8.1)

Post reply

Hi ElMaestro,

❝ ❝ A correlation >1 is funny.

❝

❝ In a way you can compare it with the CoA of a reference standard which is labeled with purity = 101.2 %. It does not contain 101.2% - I guarantee it :-D - but that may stil be the most viable estimate.

That’s another cup of tea. I guarantee that it can contain exactly 101.2% indeed – regardless what the CoA says. ;-)

❝ It is all about how we estimate / measure it. We are not guaranteed to get the same model maximum likelihood if you keep the rho controlled between (exactly) -1 to (exactly) 1 with any iterative method regardless of your tolerance setting on the x- or y-scale.

Here you are right despite some weirdos laments.* The largest correlation in my 500 simulated data sets was for #168 with 7.285 (‼)…

Schütz H. An extremely strange observation? [letter]. Europ J Drug Metabol Pharmacokin. 2004;29(1):69–71. doi:10.1007/BF03190576.

—
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

EMA dataset I, can someone post results? [General Sta­tis­tics]

EMA dataset I, can someone post results?

EMA dataset I, can someone post results?

EMA dataset I (PHX/WNL 8.1)

EMA dataset I (PHX/WNL 8.1)

EMA dataset I (PHX/WNL 8.1)

EMA dataset I (PHX/WNL 8.1)

EMA dataset I, can someone post results? [General Statistics]