From: Vincenzo Di Iorio <*vincenzo.2.di-iorio*>

Date: Fri, 26 Feb 2010 17:35:00 +0100

On behalf of Amit Taneja:

Dear Nonmem users,

I have been trying to fit a linear logistic model to a set of binary

response data in nonmem6. However, nonmem terminates and gives the following

message. My dataset has 36 subjects and 5 pd sample points per subject. On

the other hand, a logistic emax model fits the data well and consistently. I

even get the covariance step. One would expect a linear model to fit more

easily than an emax model?

My control stream is provided below as is a sample of the dataset, and the

output file is enclosed. I would appreciate your inputs on resolving this

vexing issue.

Thanks

Amit Taneja

Pharmacology

Leiden Amsterdam Center for Drug Research

Leiden

Netherlands

------------------

Relevant part of output file (Error message)=

MONITORING OF SEARCH:

0HESSIAN OF POSTERIOR DENSITY IS NON-POSITIVE-DEFINITE DURING SEARCH

----------------------------------

$PROB

$INPUT ID TIME PDB=DV PD CAUC MDV EVID DGRP

$DATA GpdV_od.csv IGNORE=

$PRED

;in this model we fit a linear model in the logit space

; Baseline Probabilities

PLAC = THETA(1)

; Drug Conc effect Emax model, assuming gamma = 1

SLOP = THETA(2)

;EC50 = THETA(3)

DRUG = SLOP*CAUC

; Logit

A = DRUG + PLAC + ETA(1) ;

; the term exp(drug+plac+eta) is expressed as A

P = EXP(A)/1+EXP(A)

;PB0 = P0/(1+P0) ; Probability of parameter=0 ; e**x/1+e**x; x=logit =

function

;PB1 = 1-PB0 ; probability of parameter=1

;IF (DV.EQ.0) PB = PB0

;IF (DV.EQ.1) PB = PB1

Y = -2*PDB*A-2*LOG(1-P)

$THETA

(1,157.9) ; PLAC Theta(1)

(1,10) ; SLOP Theta(2)

; EC50 EPER 1 Theta(3)

;15.85 ; L0 Theta(4)

$OMEGA

1

$ESTIM METHOD=COND LAPLACIAN -2LL

$COV MAT=R

$TABLE ID TIME P PD CAUC PLAC SLOP MDV EVID DGRP NOPRINT ONEHEADER =

FILE=Gp_lin15.tab

# SAMPLE OF THE INPUT DATASET

ID TIME PDB PD CAUC MDV EVID DGRP CONC

1 0 0 5.328576 0 0 0 0 0

1 1 0 0.499059 0 0 0 0 0

1 2 1 22.8096 0 0 0 0 0

1 3 0 0.299475 0 0 0 0 0

1 4 0 0.333036 0 0 0 0 0

8 0 0 2.168496 0 0 0 100 0

8 1 1 111.4464 1131.1 0 0 100 359.43

8 2 1 54.94748 3638.7 0 0 100 541.01

8 3 1 222.75 6887.6 0 0 100 636.91

8 4 1 222.75 10529.2 0 0 100 688.48

15 0 0 17.21501 0 0 0 100 0

15 1 1 65.75828 1131.1 0 0 100 356.33

15 2 1 222.75 3638.7 0 0 100 541

---------------------------------------------------------------------------=

--------

Vincenzo Luca Di Iorio

Consultant PME User support - GSK R&D Limited

---------------------------------------------------------------------------=

--------

-----------------------------------------------------------

This e-mail was sent by GlaxoSmithKline Services Unlimited

(registered in England and Wales No. 1047315), which is a

member of the GlaxoSmithKline group of companies. The

registered address of GlaxoSmithKline Services Unlimited

is 980 Great West Road, Brentford, Middlesex TW8 9GS.

-----------------------------------------------------------

Received on Fri Feb 26 2010 - 11:35:00 EST

Date: Fri, 26 Feb 2010 17:35:00 +0100

On behalf of Amit Taneja:

Dear Nonmem users,

I have been trying to fit a linear logistic model to a set of binary

response data in nonmem6. However, nonmem terminates and gives the following

message. My dataset has 36 subjects and 5 pd sample points per subject. On

the other hand, a logistic emax model fits the data well and consistently. I

even get the covariance step. One would expect a linear model to fit more

easily than an emax model?

My control stream is provided below as is a sample of the dataset, and the

output file is enclosed. I would appreciate your inputs on resolving this

vexing issue.

Thanks

Amit Taneja

Pharmacology

Leiden Amsterdam Center for Drug Research

Leiden

Netherlands

------------------

Relevant part of output file (Error message)=

MONITORING OF SEARCH:

0HESSIAN OF POSTERIOR DENSITY IS NON-POSITIVE-DEFINITE DURING SEARCH

----------------------------------

$PROB

$INPUT ID TIME PDB=DV PD CAUC MDV EVID DGRP

$DATA GpdV_od.csv IGNORE=

$PRED

;in this model we fit a linear model in the logit space

; Baseline Probabilities

PLAC = THETA(1)

; Drug Conc effect Emax model, assuming gamma = 1

SLOP = THETA(2)

;EC50 = THETA(3)

DRUG = SLOP*CAUC

; Logit

A = DRUG + PLAC + ETA(1) ;

; the term exp(drug+plac+eta) is expressed as A

P = EXP(A)/1+EXP(A)

;PB0 = P0/(1+P0) ; Probability of parameter=0 ; e**x/1+e**x; x=logit =

function

;PB1 = 1-PB0 ; probability of parameter=1

;IF (DV.EQ.0) PB = PB0

;IF (DV.EQ.1) PB = PB1

Y = -2*PDB*A-2*LOG(1-P)

$THETA

(1,157.9) ; PLAC Theta(1)

(1,10) ; SLOP Theta(2)

; EC50 EPER 1 Theta(3)

;15.85 ; L0 Theta(4)

$OMEGA

1

$ESTIM METHOD=COND LAPLACIAN -2LL

$COV MAT=R

$TABLE ID TIME P PD CAUC PLAC SLOP MDV EVID DGRP NOPRINT ONEHEADER =

FILE=Gp_lin15.tab

# SAMPLE OF THE INPUT DATASET

ID TIME PDB PD CAUC MDV EVID DGRP CONC

1 0 0 5.328576 0 0 0 0 0

1 1 0 0.499059 0 0 0 0 0

1 2 1 22.8096 0 0 0 0 0

1 3 0 0.299475 0 0 0 0 0

1 4 0 0.333036 0 0 0 0 0

8 0 0 2.168496 0 0 0 100 0

8 1 1 111.4464 1131.1 0 0 100 359.43

8 2 1 54.94748 3638.7 0 0 100 541.01

8 3 1 222.75 6887.6 0 0 100 636.91

8 4 1 222.75 10529.2 0 0 100 688.48

15 0 0 17.21501 0 0 0 100 0

15 1 1 65.75828 1131.1 0 0 100 356.33

15 2 1 222.75 3638.7 0 0 100 541

---------------------------------------------------------------------------=

--------

Vincenzo Luca Di Iorio

Consultant PME User support - GSK R&D Limited

---------------------------------------------------------------------------=

--------

-----------------------------------------------------------

This e-mail was sent by GlaxoSmithKline Services Unlimited

(registered in England and Wales No. 1047315), which is a

member of the GlaxoSmithKline group of companies. The

registered address of GlaxoSmithKline Services Unlimited

is 980 Great West Road, Brentford, Middlesex TW8 9GS.

-----------------------------------------------------------

Received on Fri Feb 26 2010 - 11:35:00 EST