Bioequivalence and Bioavailability Forum • FDA group model in R

FDA group model in R [Two-Stage / GS Designs]

posted by VStus – Poland, 2016-10-17 15:32 – Posting: # 16730
Views: 28,255

Dear mittyri,
Dear zizou,

» The ANOVA type I is the same as in PHX, but PE and CIs are not. I'm in stuck....

Thank you very much for your help!

I have checked proposed model formula in R's lm() versus SAS output of some old study report with unbalanced dataset.
I found perfect much here for group*treatment!

1. Analysis started as FDA Group Model 1:

> anovadata <- lm(log(data2$Cmax)~group+seq+seq*group+subj%in%(seq)+prd%in%group+drug+drug*group, data=data2, na.action=na.exclude)

> anova(anovadata)

Analysis of Variance Table



Response: log(data2$Cmax)

           Df Sum Sq Mean Sq F value Pr(>F)    

group       1   0.12   0.120    1.23  0.272    

seq         1   0.05   0.046    0.47  0.497    

drug        1   0.18   0.182    1.87  0.177    

group:seq   1   0.46   0.460    4.71  0.034 *  

seq:subj   59  25.25   0.428    4.38  3e-08 ***

group:prd   2   0.33   0.164    1.68  0.195    

group:drug  1   0.01   0.007    0.07  0.797    

Residuals  59   5.76   0.098



> drop1(anovadata, test="F")

Single term deletions



Model:

log(data2$Cmax) ~ group + seq + seq * group + subj %in% (seq) + 

    prd %in% group + drug + drug * group

           Df Sum of Sq   RSS  AIC F value Pr(>F)    

<none>                   5.76 -255                   

group:seq   0      0.00  5.76 -255                   

seq:subj   59     25.25 31.01 -161    4.38  3e-08 ***

group:prd   2      0.32  6.09 -252    1.64    0.2    

group:drug  1      0.01  5.77 -256    0.07    0.8

F and P values for group:drug are exactly the same as obtained by SAS:


Param   Source        DF  F Value   P-value      Model to be used:

lCmax   Group*Treat   1   0.07   0.7966   N.S.   Remove interaction

2. I've got same F and P values (at least for some of the effects) while proceeding to FDA Group Model 2 (or EMA's only model with all effects fixed), not mentioning exactly the same outputs for MSE/ Intra-subject CV and PE / 90%CIs :

> anovadata2 <- lm(log(data2$Cmax)~group+seq+seq*group+subj%in%(seq)+prd%in%group+drug, data=data2, na.action=na.exclude)

> drop1(anovadata2, test="F")

Single term deletions



Model:

log(data2$Cmax) ~ group + seq + seq * group + subj %in% (seq) + 

    prd %in% group + drug

          Df Sum of Sq   RSS  AIC F value  Pr(>F)    

<none>                  5.77 -256                    

drug       1      0.21  5.98 -254    2.15    0.15    

group:seq  0      0.00  5.77 -256                    

seq:subj  59     25.25 31.02 -163    4.45 1.8e-08 ***

group:prd  2      0.33  6.10 -254    1.71    0.19

SAS output:


Param   Effect          DF   F Value   P-value

lCmax   Group           1    0.21      0.6475

lCmax   Seq             1    0.31      0.5822

lCmax   Group*Seq       1    1.08      0.3040

lCmax   Treat           1    2.15      0.1476

lCmax   Per(Group)      2    1.71      0.1896

lCmax   Subj(Group*Seq) 59   4.45      <.0001

Maybe lm() sometimes doing exactly the same as PROC GLM?
Regards, VStus

Complete thread:

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