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.ribb...@pharmetheus.com 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 <alhad...@umn.edu> wrote: > 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: alhad...@umn.edu | Cell:541-740-7991
