From: Jakob Ribbing <*jakob.ribbing*>

Date: Tue, 2 Aug 2016 16:16:40 +0200

Hi Ali,

If drop out generally only occurs at visits (or as in your case, where =

the exact time of dropout is unknown) it can sometimes make sense to use =

interval-censored TTE, and I would agree in your case the best would be =

to assume that the time of dropout was between subjects last visit and =

the next planned visit.

In either case the dropout record should be placed after the last PD =

observation. For subjects that miss intermediate visits, but that are =

not discontinued from the study, such missing records do not count as a =

dropout event, so this does not pose a great problem.

Ideally, VPC evaluations should allow any simulated subject that do not =

drop out (in simulation) to present data until the planned end of study, =

so for such evaluations you may want to add dummy records (or depending =

on how simulations are preformed: one dummy record, at the planned end =

of study), in subjects that dropped out in your original analysis data =

set.

With regards to modelling dropout, do you need a simultaneous fit of PD =

and dropout, or is it sufficient to combine the two for simulations? =

(i.e. PD estimates are not much affected by the incorporation of a =

dropout model, whereas PD simulations are)

The IPRED for PD would then be fixed according to your final PD model, =

when you subsequently develop the TTE model for dropout.

This may simplify a great deal, and still allows realistic simulations =

(e.g. for a VPC evaluation), that can confirm that your final PD model =

is adequate for simulations.

Finally, I noticed in your data set LOCF contains the previous DV value =

(for DVID=1). Normally, by LOCF, you only carry forward the previous =

value if a planned measurement is missing, so this is not what normally =

is called LOCF.

But maybe this is just an unfortunate name, and what you need from this =

variable is the previous DV value?

I did not find time to check your control steam, but if there is any =

obvious error I am sure someone else will spot that.

Best wishes

Jakob

Jakob Ribbing, Ph.D.

Senior Consultant, Pharmetheus AB

Cell/Mobile: +46 (0)70 514 33 77

Jakob.Ribbing

www.pharmetheus.com

Phone, Office: +46 (0)18 513 328

Uppsala Science Park, Dag Hammarskjölds väg 52B

SE-752 37 Uppsala, Sweden

This communication is confidential and is only intended for the use of =

the individual or entity to which it is directed. It may contain =

information that is privileged and exempt from disclosure under =

applicable law. If you are not the intended recipient please notify us =

immediately. Please do not copy it or disclose its contents to any other =

person.

On 02 Aug 2016, at 14:51, Ali Alhadab <alhad009

*> Please post this Jointing Modeling of Dropout
*

*>
*

*>
*

*>
*

*> I am trying to jointly model PD and dropout and I have few questions =
*

for those who have experience with this model.

*>
*

*> Does it matter if I use right or interval censored event model? What =
*

is the advantage of using one over the other?

*>
*

*> The dropout record should be the last record for each patient in a =
*

dataset, isn’t it? If I want to do interval censored and I only know =

the last time a subject is known to be in the trial, can I use the last =

time observed and time of next scheduled visit for my interval? Or I =

only can do right censoring in this case.

*>
*

*> If dropout out data was not collected and I need to account for that, =
*

I can develop criteria to define dropout after the fact that study is =

over, can’t I? For example, subjects who did not show up for at least =

three visits (first thee visits) are considered missing, or subjects who =

have been in the study less than 14 days are considered missing. What if =

a subject made it to the 1st and 5th visit but missed those in between? =

Any suggestions how to do that?

*>
*

*> When I run my joint model, I get the following error: ”SQUARED" =
*

WEIGHTED INDIVIDUAL RESIDUALS IS INFINITE” or “NO. OF REQUIRED =

SIGNIFICANT DIGITS IN SOLUTION VECTOR TO DIFFERENTIAL EQUATIONS, 5, MAY =

BE TOO LARGE”. I tried to use different ADVAN (6,8,9) and reduce TOL =

(6,5,4,3,2,1) but that did not solve the problem. Any idea what the =

problem is?

*>
*

*> ID
*

*> TIME
*

*> DV
*

*> LOCF
*

*> DVID
*

*> CMT
*

*> 1
*

*> 0
*

*> 37
*

*> 0
*

*> 1
*

*> 1
*

*> 1
*

*> 14
*

*> 18
*

*> 37
*

*> 1
*

*> 1
*

*> 1
*

*> 30
*

*> 14
*

*> 18
*

*> 1
*

*> 1
*

*> 1
*

*> 58
*

*> 7
*

*> 14
*

*> 1
*

*> 1
*

*> 1
*

*> 62
*

*> 0
*

*> 7
*

*> 2
*

*> 1
*

*> 2
*

*> 0
*

*> 22
*

*> 0
*

*> 1
*

*> 1
*

*> 2
*

*> 0
*

*> 0
*

*> 0
*

*> 3
*

*> 2
*

*> 2
*

*> 25
*

*> 23
*

*> 22
*

*> 1
*

*> 1
*

*> 2
*

*> 34
*

*> 1
*

*> 22
*

*> 2
*

*> 1
*

*>
*

*> $SUB ADVAN=6 TOL=9
*

*> $MODEL COMP=(HAZARD)
*

*> $PK
*

*> ;;; PD COUNT MODEL ;;;
*

*> S0=THETA(1)*EXP(ETA(1))+THETA(10)*(SEX-1)
*

*> IF (REGION.EQ.6) THEN
*

*> REG=THETA(12)
*

*> ELSE
*

*> REG=0
*

*> ENDIF
*

*>
*

*> IF (TX.EQ.1) =
*

SSS=THETA(2)*EXP(ETA(2))+THETA(10)*(SEX-1)+REG+THETA(13)*MISSTOT

*> IF (TX.EQ.2) =
*

SSS=THETA(3)*EXP(ETA(2))+THETA(10)*(SEX-1)+REG+THETA(13)*MISSTOT

*> IF (TX.EQ.3) =
*

SSS=THETA(4)*EXP(ETA(2))+THETA(10)*(SEX-1)+REG+THETA(13)*MISSTOT

*> IF (TX.EQ.4) =
*

SSS=THETA(5)*EXP(ETA(2))+THETA(10)*(SEX-1)+REG+THETA(13)*MISSTOT

*>
*

*> IF (TX.EQ.1) =
*

TPROG=THETA(6)*EXP(ETA(3))+(B_INF/25.86)**THETA(11)+REG+SEASON**THETA(13=

)

*> IF (TX.EQ.2) =
*

TPROG=THETA(7)*EXP(ETA(3))+(B_INF/25.86)**THETA(11)+REG+SEASON**THETA(13=

)

*> IF (TX.EQ.3) =
*

TPROG=THETA(8)*EXP(ETA(3))+(B_INF/25.86)**THETA(11)+REG+SEASON**THETA(13=

)

*> IF (TX.EQ.4) =
*

TPROG=THETA(9)*EXP(ETA(3))+(B_INF/25.86)**THETA(11)+REG+SEASON**THETA(13=

)

*>
*

*> ;;; DROPOUT MODEL ;;;
*

*> BASE = THETA(14)
*

*> SHP = THETA(15)
*

*> LAM = BASE*SHP
*

*> BETA = SHP-1
*

*> BETA1 = THETA(16)
*

*>
*

*> $DES
*

*> ;;; PD COUNT MODEL ;;;
*

*> DCOUNT=S0+(SSS-S0)*(1-EXP(-LN2/TPROG*T))
*

*>
*

*> ;;; DROPOUT MODEL ;;;
*

*> IF(T.GT.0)THEN
*

*> DADT(1) = LAM*EXP(BETA*LOG(BASE*T)+DCOUNT*BETA1)
*

*> ELSE
*

*> DADT(1) = 0
*

*> ENDIF
*

*>
*

*> $ERROR
*

*> COUNT=S0+(SSS-S0)*(1-EXP(-LN2/TPROG*TIME)) ;RENAME IPRED
*

*>
*

*> CHZ = A(1) ;rename old cumulative hazard
*

*> SUR = EXP(-CHZ) ;survival probability
*

*>
*

*> IF(TIME.GT.0)THEN
*

*> HAZNOW=LAM*EXP(BETA*LOG(BASE*TIME)+COUNT*BETA1)
*

*> ELSE
*

*> HAZNOW = 0
*

*> ENDIF
*

*>
*

*> IF(DVID.EQ.1) THEN
*

*> F_FLAG=0
*

*> Y=COUNT+ERR(1) ;COUNT PREDICTION
*

*> ENDIF
*

*>
*

*> IF(DVID.EQ.2.AND.DV.EQ.1) THEN
*

*> F_FLAG=1
*

*> Y=SUR*HAZNOW ;DROP OUT EVENT
*

*> ENDIF
*

*>
*

*> IF(DVID.EQ.2.AND.DV.EQ.0) THEN
*

*> F_FLAG=1
*

*> Y=SUR ;RIGHT CENSORED EVENT
*

*> ENDIF
*

*>
*

*> $EST MAXEVAL=9990 METHOD=COND LAPLACIAN
*

*>
*

*>
*

*> Thanks
*

*>
*

*> Ali Alhadab, PharmD | PhD student
*

*> University of Minnesota College of Pharmacy
*

*> Department of Experimental & Clinical Pharmacology
*

*> E-mail: alhad009 *

Received on Tue Aug 02 2016 - 10:16:40 EDT

Date: Tue, 2 Aug 2016 16:16:40 +0200

Hi Ali,

If drop out generally only occurs at visits (or as in your case, where =

the exact time of dropout is unknown) it can sometimes make sense to use =

interval-censored TTE, and I would agree in your case the best would be =

to assume that the time of dropout was between subjects last visit and =

the next planned visit.

In either case the dropout record should be placed after the last PD =

observation. For subjects that miss intermediate visits, but that are =

not discontinued from the study, such missing records do not count as a =

dropout event, so this does not pose a great problem.

Ideally, VPC evaluations should allow any simulated subject that do not =

drop out (in simulation) to present data until the planned end of study, =

so for such evaluations you may want to add dummy records (or depending =

on how simulations are preformed: one dummy record, at the planned end =

of study), in subjects that dropped out in your original analysis data =

set.

With regards to modelling dropout, do you need a simultaneous fit of PD =

and dropout, or is it sufficient to combine the two for simulations? =

(i.e. PD estimates are not much affected by the incorporation of a =

dropout model, whereas PD simulations are)

The IPRED for PD would then be fixed according to your final PD model, =

when you subsequently develop the TTE model for dropout.

This may simplify a great deal, and still allows realistic simulations =

(e.g. for a VPC evaluation), that can confirm that your final PD model =

is adequate for simulations.

Finally, I noticed in your data set LOCF contains the previous DV value =

(for DVID=1). Normally, by LOCF, you only carry forward the previous =

value if a planned measurement is missing, so this is not what normally =

is called LOCF.

But maybe this is just an unfortunate name, and what you need from this =

variable is the previous DV value?

I did not find time to check your control steam, but if there is any =

obvious error I am sure someone else will spot that.

Best wishes

Jakob

Jakob Ribbing, Ph.D.

Senior Consultant, Pharmetheus AB

Cell/Mobile: +46 (0)70 514 33 77

Jakob.Ribbing

www.pharmetheus.com

Phone, Office: +46 (0)18 513 328

Uppsala Science Park, Dag Hammarskjölds väg 52B

SE-752 37 Uppsala, Sweden

This communication is confidential and is only intended for the use of =

the individual or entity to which it is directed. It may contain =

information that is privileged and exempt from disclosure under =

applicable law. If you are not the intended recipient please notify us =

immediately. Please do not copy it or disclose its contents to any other =

person.

On 02 Aug 2016, at 14:51, Ali Alhadab <alhad009

for those who have experience with this model.

is the advantage of using one over the other?

dataset, isn’t it? If I want to do interval censored and I only know =

the last time a subject is known to be in the trial, can I use the last =

time observed and time of next scheduled visit for my interval? Or I =

only can do right censoring in this case.

I can develop criteria to define dropout after the fact that study is =

over, can’t I? For example, subjects who did not show up for at least =

three visits (first thee visits) are considered missing, or subjects who =

have been in the study less than 14 days are considered missing. What if =

a subject made it to the 1st and 5th visit but missed those in between? =

Any suggestions how to do that?

WEIGHTED INDIVIDUAL RESIDUALS IS INFINITE” or “NO. OF REQUIRED =

SIGNIFICANT DIGITS IN SOLUTION VECTOR TO DIFFERENTIAL EQUATIONS, 5, MAY =

BE TOO LARGE”. I tried to use different ADVAN (6,8,9) and reduce TOL =

(6,5,4,3,2,1) but that did not solve the problem. Any idea what the =

problem is?

SSS=THETA(2)*EXP(ETA(2))+THETA(10)*(SEX-1)+REG+THETA(13)*MISSTOT

SSS=THETA(3)*EXP(ETA(2))+THETA(10)*(SEX-1)+REG+THETA(13)*MISSTOT

SSS=THETA(4)*EXP(ETA(2))+THETA(10)*(SEX-1)+REG+THETA(13)*MISSTOT

SSS=THETA(5)*EXP(ETA(2))+THETA(10)*(SEX-1)+REG+THETA(13)*MISSTOT

TPROG=THETA(6)*EXP(ETA(3))+(B_INF/25.86)**THETA(11)+REG+SEASON**THETA(13=

)

TPROG=THETA(7)*EXP(ETA(3))+(B_INF/25.86)**THETA(11)+REG+SEASON**THETA(13=

)

TPROG=THETA(8)*EXP(ETA(3))+(B_INF/25.86)**THETA(11)+REG+SEASON**THETA(13=

)

TPROG=THETA(9)*EXP(ETA(3))+(B_INF/25.86)**THETA(11)+REG+SEASON**THETA(13=

)

Received on Tue Aug 02 2016 - 10:16:40 EDT