More Levels of Random Effects

-----Original Message----- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Denney, William S. Sent: den 15 oktober 2008 14:39 To: [email protected] Subject: [NMusers] More Levels of Random Effects Hello, I'm trying to build a model where I need to have ETAs generated on separately for the ID and another variable (MACH). What I have is a PD experiment that was run on several different machines (MACH). Each machine appears to have a different slope per day and a different calibration. I still need to keep some ETAs on the ID column, so I can't just assign MACH=ID. I've heard that there are ways to do similar to this, but I have been unable to find examples of how to set etas to key off of different columns. Thanks, Bill Notice: This e-mail message, together with any attachments, contains information of Merck & Co., Inc. (One Merck Drive, Whitehouse Station, New Jersey, USA 08889), and/or its affiliates (which may be known outside the United States as Merck Frosst, Merck Sharp & Dohme or MSD and in Japan, as Banyu - direct contact information for affiliates is available at http://www.merck.com/contact/contacts.html) that may be confidential, proprietary copyrighted and/or legally privileged. It is intended solely for the use of the individual or entity named on this message. If you are not the intended recipient, and have received this message in error, please notify us immediately by reply e-mail and then delete it from your system.

> -----Original Message----- > From: owner-nmusers > Behalf Of Johan Wallin > Sent: Wednesday, October 15, 2008 9:17 AM > To: nmusers > Subject: RE: [NMusers] More Levels of Random Effects > > Bill, > Is it really an eta you want, or is this rather solved by different error > models for the different machines? > > If still want etas, one way would be to model in the same way as IOV. In the > case of intermachine-variability you would have to assume the variability > between all machines are the same... > Or would you rather assume interindividual variability is different with > different machine, and you then would want one eta for TH(X) for every > machine...? It depends on what you mean by different slope every day, > regarding on what your experiments like, but calibration differences should > perhaps be taken care of by looking into your error model, eta on epsilon > for starters... > > Without knowing your structure of data, a short example of IOV-like > variability would be: > > MA1=0 > MA2=0 > IF(MACH=1)MA1=1 > IF(MACH=2)MA2=1 > ;Intermachine variability: > ETAM = MA1*ETA(Y)+MA2*ETA(Z) > > PAR= TH(X) *EXP(ETA(X)+ETAM) > > $OMEGA > value1 > $OMEGA BLOCK(1) value2 > $OMEGA BLOCK(1) same > > /Johan > > > > _________________________________________ > Johan Wallin, M.Sci./Ph.D.-student > Pharmacometrics Group > Div. of Pharmacokinetics and Drug therapy > Uppsala University > _________________________________________ > > > -----Original Message----- > From: owner-nmusers > Behalf Of Denney, William S. > Sent: den 15 oktober 2008 14:39 > To: nmusers > Subject: [NMusers] More Levels of Random Effects > > Hello, > > I'm trying to build a model where I need to have ETAs generated on > separately for the ID and another variable (MACH). What I have is a PD > experiment that was run on several different machines (MACH). Each > machine appears to have a different slope per day and a different > calibration. I still need to keep some ETAs on the ID column, so I > can't just assign MACH=ID. > > I've heard that there are ways to do similar to this, but I have been > unable to find examples of how to set etas to key off of different > columns. > > Thanks, > > Bill > Notice: This e-mail message, together with any attachments, contains > information of Merck & Co., Inc. (One Merck Drive, Whitehouse Station, > New Jersey, USA 08889), and/or its affiliates (which may be known > outside the United States as Merck Frosst, Merck Sharp & Dohme or > MSD and in Japan, as Banyu - direct contact information for affiliates is > available at http://www.merck.com/contact/contacts.html) that may be > confidential, proprietary copyrighted and/or legally privileged. It is > intended solely for the use of the individual or entity named on this > message. If you are not the intended recipient, and have received this > message in error, please notify us immediately by reply e-mail and > then delete it from your system. > > > -- Nick Holford, Dept Pharmacology & Clinical Pharmacology University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New Zealand n.holford http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford

>> From: owner-nmusers >> [mailto:owner-nmusers >> Behalf Of Johan Wallin >> Sent: Wednesday, October 15, 2008 9:17 AM >> To: nmusers >> Subject: RE: [NMusers] More Levels of Random Effects >> >> Bill, >> Is it really an eta you want, or is this rather solved by different error >> models for the different machines? >> >> If still want etas, one way would be to model in the same way as IOV. >> In the >> case of intermachine-variability you would have to assume the variability >> between all machines are the same... Or would you rather assume >> interindividual variability is different with >> different machine, and you then would want one eta for TH(X) for every >> machine...? It depends on what you mean by different slope every day, >> regarding on what your experiments like, but calibration differences >> should >> perhaps be taken care of by looking into your error model, eta on epsilon >> for starters... >> >> Without knowing your structure of data, a short example of IOV-like >> variability would be: >> >> MA1=0 >> MA2=0 >> IF(MACH=1)MA1=1 >> IF(MACH=2)MA2=1 >> ;Intermachine variability: >> ETAM = MA1*ETA(Y)+MA2*ETA(Z) >> >> PAR= TH(X) *EXP(ETA(X)+ETAM) >> >> $OMEGA value1 >> $OMEGA BLOCK(1) value2 >> $OMEGA BLOCK(1) same >> >> /Johan >> >> >> _________________________________________ >> Johan Wallin, M.Sci./Ph.D.-student >> Pharmacometrics Group >> Div. of Pharmacokinetics and Drug therapy >> Uppsala University >> _________________________________________ >> >> >> -----Original Message----- >> From: owner-nmusers >> [mailto:owner-nmusers >> Behalf Of Denney, William S. >> Sent: den 15 oktober 2008 14:39 >> To: nmusers >> Subject: [NMusers] More Levels of Random Effects >> >> Hello, >> >> I'm trying to build a model where I need to have ETAs generated on >> separately for the ID and another variable (MACH). What I have is a PD >> experiment that was run on several different machines (MACH). Each >> machine appears to have a different slope per day and a different >> calibration. I still need to keep some ETAs on the ID column, so I >> can't just assign MACH=ID. >> >> I've heard that there are ways to do similar to this, but I have been >> unable to find examples of how to set etas to key off of different >> columns. >> >> Thanks, >> >> Bill >> Notice: This e-mail message, together with any attachments, contains >> information of Merck & Co., Inc. (One Merck Drive, Whitehouse Station, >> New Jersey, USA 08889), and/or its affiliates (which may be known >> outside the United States as Merck Frosst, Merck Sharp & Dohme or >> MSD and in Japan, as Banyu - direct contact information for affiliates is >> available at http://www.merck.com/contact/contacts.html) that may be >> confidential, proprietary copyrighted and/or legally privileged. It is >> intended solely for the use of the individual or entity named on this >> message. If you are not the intended recipient, and have received this >> message in error, please notify us immediately by reply e-mail and >> then delete it from your system. >> >> >> >

-----Original Message----- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Johan Wallin Sent: Wednesday, October 15, 2008 9:17 AM To: [email protected] Subject: RE: [NMusers] More Levels of Random Effects Bill, Is it really an eta you want, or is this rather solved by different error models for the different machines? If still want etas, one way would be to model in the same way as IOV. In the case of intermachine-variability you would have to assume the variability between all machines are the same... Or would you rather assume interindividual variability is different with different machine, and you then would want one eta for TH(X) for every machine...? It depends on what you mean by different slope every day, regarding on what your experiments like, but calibration differences should perhaps be taken care of by looking into your error model, eta on epsilon for starters... Without knowing your structure of data, a short example of IOV-like variability would be: MA1=0 MA2=0 IF(MACH=1)MA1=1 IF(MACH=2)MA2=1 ;Intermachine variability: ETAM = MA1*ETA(Y)+MA2*ETA(Z) PAR= TH(X) *EXP(ETA(X)+ETAM) $OMEGA value1 $OMEGA BLOCK(1) value2 $OMEGA BLOCK(1) same /Johan _________________________________________ Johan Wallin, M.Sci./Ph.D.-student Pharmacometrics Group Div. of Pharmacokinetics and Drug therapy Uppsala University _________________________________________ -----Original Message----- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Denney, William S. Sent: den 15 oktober 2008 14:39 To: [email protected] Subject: [NMusers] More Levels of Random Effects Hello, I'm trying to build a model where I need to have ETAs generated on separately for the ID and another variable (MACH). What I have is a PD experiment that was run on several different machines (MACH). Each machine appears to have a different slope per day and a different calibration. I still need to keep some ETAs on the ID column, so I can't just assign MACH=ID. I've heard that there are ways to do similar to this, but I have been unable to find examples of how to set etas to key off of different columns. Thanks, Bill Notice: This e-mail message, together with any attachments, contains information of Merck & Co., Inc. (One Merck Drive, Whitehouse Station, New Jersey, USA 08889), and/or its affiliates (which may be known outside the United States as Merck Frosst, Merck Sharp & Dohme or MSD and in Japan, as Banyu - direct contact information for affiliates is available at http://www.merck.com/contact/contacts.html) that may be confidential, proprietary copyrighted and/or legally privileged. It is intended solely for the use of the individual or entity named on this message. If you are not the intended recipient, and have received this message in error, please notify us immediately by reply e-mail and then delete it from your system.

> -----Original Message----- > From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On > Behalf Of Johan Wallin > Sent: Wednesday, October 15, 2008 9:17 AM > To: [email protected] > Subject: RE: [NMusers] More Levels of Random Effects > > Bill, > Is it really an eta you want, or is this rather solved by different error > models for the different machines? > > If still want etas, one way would be to model in the same way as IOV. In the > case of intermachine-variability you would have to assume the variability > > between all machines are the same... Or would you rather assume interindividual variability is different with > > different machine, and you then would want one eta for TH(X) for every > machine...? It depends on what you mean by different slope every day, > regarding on what your experiments like, but calibration differences should > perhaps be taken care of by looking into your error model, eta on epsilon > for starters... > > Without knowing your structure of data, a short example of IOV-like > variability would be: > > MA1=0 > MA2=0 > IF(MACH=1)MA1=1 > IF(MACH=2)MA2=1 > ;Intermachine variability: > ETAM = MA1*ETA(Y)+MA2*ETA(Z) > > PAR= TH(X) *EXP(ETA(X)+ETAM) > > $OMEGA value1 > > $OMEGA BLOCK(1) value2 > $OMEGA BLOCK(1) same > > /Johan > > _________________________________________ > Johan Wallin, M.Sci./Ph.D.-student > Pharmacometrics Group > Div. of Pharmacokinetics and Drug therapy > Uppsala University > _________________________________________ > > -----Original Message----- > From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On > Behalf Of Denney, William S. > Sent: den 15 oktober 2008 14:39 > To: [email protected] > Subject: [NMusers] More Levels of Random Effects > > Hello, > > I'm trying to build a model where I need to have ETAs generated on > separately for the ID and another variable (MACH). What I have is a PD > experiment that was run on several different machines (MACH). Each > machine appears to have a different slope per day and a different > calibration. I still need to keep some ETAs on the ID column, so I > can't just assign MACH=ID. > > I've heard that there are ways to do similar to this, but I have been > unable to find examples of how to set etas to key off of different > columns. > > Thanks, > > Bill > Notice: This e-mail message, together with any attachments, contains > information of Merck & Co., Inc. (One Merck Drive, Whitehouse Station, > New Jersey, USA 08889), and/or its affiliates (which may be known > outside the United States as Merck Frosst, Merck Sharp & Dohme or > MSD and in Japan, as Banyu - direct contact information for affiliates is > available at http://www.merck.com/contact/contacts.html) that may be > confidential, proprietary copyrighted and/or legally privileged. It is > intended solely for the use of the individual or entity named on this > message. If you are not the intended recipient, and have received this > message in error, please notify us immediately by reply e-mail and > then delete it from your system. -- Nick Holford, Dept Pharmacology & Clinical Pharmacology University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New Zealand [EMAIL PROTECTED] tel:+64(9)923-6730 fax:+64(9)373-7090 http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford

> > From: [EMAIL PROTECTED] [ mailto:[EMAIL PROTECTED] On > > > > Behalf Of Johan Wallin > > Sent: Wednesday, October 15, 2008 9:17 AM > > To: [email protected] > > Subject: RE: [NMusers] More Levels of Random Effects > > > > Bill, > > Is it really an eta you want, or is this rather solved by different error > > models for the different machines? > > > > If still want etas, one way would be to model in the same way as IOV. In the > > > > case of intermachine-variability you would have to assume the variability > > > > between all machines are the same... Or would you rather assume interindividual variability is different with > > > > different machine, and you then would want one eta for TH(X) for every > > machine...? It depends on what you mean by different slope every day, > > > > regarding on what your experiments like, but calibration differences should > > > > perhaps be taken care of by looking into your error model, eta on epsilon > > for starters... > > > > Without knowing your structure of data, a short example of IOV-like > > variability would be: > > > > MA1=0 > > MA2=0 > > IF(MACH=1)MA1=1 > > IF(MACH=2)MA2=1 > > ;Intermachine variability: > > ETAM = MA1*ETA(Y)+MA2*ETA(Z) > > > > PAR= TH(X) *EXP(ETA(X)+ETAM) > > > > $OMEGA value1 > > $OMEGA BLOCK(1) value2 > > $OMEGA BLOCK(1) same > > > > /Johan > > > > _________________________________________ > > Johan Wallin, M.Sci./Ph.D.-student > > Pharmacometrics Group > > Div. of Pharmacokinetics and Drug therapy > > Uppsala University > > _________________________________________ > > > > -----Original Message----- > > > > From: [EMAIL PROTECTED] [ mailto:[EMAIL PROTECTED] On > > > > Behalf Of Denney, William S. > > Sent: den 15 oktober 2008 14:39 > > To: [email protected] > > Subject: [NMusers] More Levels of Random Effects > > > > Hello, > > > > I'm trying to build a model where I need to have ETAs generated on > > separately for the ID and another variable (MACH). What I have is a PD > > experiment that was run on several different machines (MACH). Each > > machine appears to have a different slope per day and a different > > calibration. I still need to keep some ETAs on the ID column, so I > > can't just assign MACH=ID. > > > > I've heard that there are ways to do similar to this, but I have been > > unable to find examples of how to set etas to key off of different > > columns. > > > > Thanks, > > > > Bill > > Notice: This e-mail message, together with any attachments, contains > > information of Merck & Co., Inc. (One Merck Drive, Whitehouse Station, > > New Jersey, USA 08889), and/or its affiliates (which may be known > > outside the United States as Merck Frosst, Merck Sharp & Dohme or > > MSD and in Japan, as Banyu - direct contact information for affiliates is > > available at http://www.merck.com/contact/contacts.html) that may be > > confidential, proprietary copyrighted and/or legally privileged. It is > > intended solely for the use of the individual or entity named on this > > message. If you are not the intended recipient, and have received this > > message in error, please notify us immediately by reply e-mail and > > then delete it from your system.

>>>> From: owner-nmusers >>>> [mailto:owner-nmusers >>>> Behalf Of Johan Wallin >>>> Sent: Wednesday, October 15, 2008 9:17 AM >>>> To: nmusers >>>> Subject: RE: [NMusers] More Levels of Random Effects >>>> >>>> Bill, >>>> Is it really an eta you want, or is this rather solved by different >>>> error >>>> models for the different machines? >>>> >>>> If still want etas, one way would be to model in the same way as >>>> IOV. In the >>>> case of intermachine-variability you would have to assume the >>>> variability >>>> between all machines are the same... Or would you rather assume >>>> interindividual variability is different with >>>> different machine, and you then would want one eta for TH(X) for every >>>> machine...? It depends on what you mean by different slope every day, >>>> regarding on what your experiments like, but calibration differences >>>> should >>>> perhaps be taken care of by looking into your error model, eta on >>>> epsilon >>>> for starters... >>>> >>>> Without knowing your structure of data, a short example of IOV-like >>>> variability would be: >>>> >>>> MA1=0 >>>> MA2=0 >>>> IF(MACH=1)MA1=1 >>>> IF(MACH=2)MA2=1 >>>> ;Intermachine variability: >>>> ETAM = MA1*ETA(Y)+MA2*ETA(Z) >>>> >>>> PAR= TH(X) *EXP(ETA(X)+ETAM) >>>> >>>> $OMEGA value1 >>>> $OMEGA BLOCK(1) value2 >>>> $OMEGA BLOCK(1) same >>>> >>>> /Johan >>>> >>>> >>>> _________________________________________ >>>> Johan Wallin, M.Sci./Ph.D.-student >>>> Pharmacometrics Group >>>> Div. of Pharmacokinetics and Drug therapy >>>> Uppsala University >>>> _________________________________________ >>>> >>>> >>>> -----Original Message----- >>>> From: owner-nmusers >>>> [mailto:owner-nmusers >>>> Behalf Of Denney, William S. >>>> Sent: den 15 oktober 2008 14:39 >>>> To: nmusers >>>> Subject: [NMusers] More Levels of Random Effects >>>> >>>> Hello, >>>> >>>> I'm trying to build a model where I need to have ETAs generated on >>>> separately for the ID and another variable (MACH). What I have is a PD >>>> experiment that was run on several different machines (MACH). Each >>>> machine appears to have a different slope per day and a different >>>> calibration. I still need to keep some ETAs on the ID column, so I >>>> can't just assign MACH=ID. >>>> >>>> I've heard that there are ways to do similar to this, but I have been >>>> unable to find examples of how to set etas to key off of different >>>> columns. >>>> >>>> Thanks, >>>> >>>> Bill >>>> Notice: This e-mail message, together with any attachments, contains >>>> information of Merck & Co., Inc. (One Merck Drive, Whitehouse Station, >>>> New Jersey, USA 08889), and/or its affiliates (which may be known >>>> outside the United States as Merck Frosst, Merck Sharp & Dohme or >>>> MSD and in Japan, as Banyu - direct contact information for >>>> affiliates is >>>> available at http://www.merck.com/contact/contacts.html) that may be >>>> confidential, proprietary copyrighted and/or legally privileged. It is >>>> intended solely for the use of the individual or entity named on this >>>> message. If you are not the intended recipient, and have received this >>>> message in error, please notify us immediately by reply e-mail and >>>> then delete it from your system. >>>> >>>> >>>> >>> >> >

>>>> From: [EMAIL PROTECTED] >>>> [mailto:[EMAIL PROTECTED] On >>>> Behalf Of Johan Wallin >>>> Sent: Wednesday, October 15, 2008 9:17 AM >>>> To: [email protected] >>>> Subject: RE: [NMusers] More Levels of Random Effects >>>> >>>> Bill, >>>> Is it really an eta you want, or is this rather solved by different >>>> error >>>> models for the different machines? >>>> >>>> If still want etas, one way would be to model in the same way as >>>> IOV. In the >>>> case of intermachine-variability you would have to assume the >>>> variability >>>> between all machines are the same... Or would you rather assume >>>> interindividual variability is different with >>>> different machine, and you then would want one eta for TH(X) for every >>>> machine...? It depends on what you mean by different slope every day, >>>> regarding on what your experiments like, but calibration differences >>>> should >>>> perhaps be taken care of by looking into your error model, eta on >>>> epsilon >>>> for starters... >>>> >>>> Without knowing your structure of data, a short example of IOV-like >>>> variability would be: >>>> >>>> MA1=0 >>>> MA2=0 >>>> IF(MACH=1)MA1=1 >>>> IF(MACH=2)MA2=1 >>>> ;Intermachine variability: >>>> ETAM = MA1*ETA(Y)+MA2*ETA(Z) >>>> >>>> PAR= TH(X) *EXP(ETA(X)+ETAM) >>>> >>>> $OMEGA value1 >>>> $OMEGA BLOCK(1) value2 >>>> $OMEGA BLOCK(1) same >>>> >>>> /Johan >>>> >>>> >>>> _________________________________________ >>>> Johan Wallin, M.Sci./Ph.D.-student >>>> Pharmacometrics Group >>>> Div. of Pharmacokinetics and Drug therapy >>>> Uppsala University >>>> _________________________________________ >>>> >>>> >>>> -----Original Message----- >>>> From: [EMAIL PROTECTED] >>>> [mailto:[EMAIL PROTECTED] On >>>> Behalf Of Denney, William S. >>>> Sent: den 15 oktober 2008 14:39 >>>> To: [email protected] >>>> Subject: [NMusers] More Levels of Random Effects >>>> >>>> Hello, >>>> >>>> I'm trying to build a model where I need to have ETAs generated on >>>> separately for the ID and another variable (MACH). What I have is a PD >>>> experiment that was run on several different machines (MACH). Each >>>> machine appears to have a different slope per day and a different >>>> calibration. I still need to keep some ETAs on the ID column, so I >>>> can't just assign MACH=ID. >>>> >>>> I've heard that there are ways to do similar to this, but I have been >>>> unable to find examples of how to set etas to key off of different >>>> columns. >>>> >>>> Thanks, >>>> >>>> Bill >>>> Notice: This e-mail message, together with any attachments, contains >>>> information of Merck & Co., Inc. (One Merck Drive, Whitehouse Station, >>>> New Jersey, USA 08889), and/or its affiliates (which may be known >>>> outside the United States as Merck Frosst, Merck Sharp & Dohme or >>>> MSD and in Japan, as Banyu - direct contact information for >>>> affiliates is >>>> available at http://www.merck.com/contact/contacts.html) that may be >>>> confidential, proprietary copyrighted and/or legally privileged. It is >>>> intended solely for the use of the individual or entity named on this >>>> message. If you are not the intended recipient, and have received this >>>> message in error, please notify us immediately by reply e-mail and >>>> then delete it from your system. >>>> >>>> >>>> >>> >> >

-----Original Message----- From: [EMAIL PROTECTED] [ mailto:[EMAIL PROTECTED] ] On Behalf Of Johan Wallin Sent: Wednesday, October 15, 2008 9:17 AM To: [email protected] Subject: RE: [NMusers] More Levels of Random Effects Bill, Is it really an eta you want, or is this rather solved by different error models for the different machines? If still want etas, one way would be to model in the same way as IOV. In the case of intermachine-variability you would have to assume the variability between all machines are the same... Or would you rather assume interindividual variability is different with different machine, and you then would want one eta for TH(X) for every machine...? It depends on what you mean by different slope every day, regarding on what your experiments like, but calibration differences should perhaps be taken care of by looking into your error model, eta on epsilon for starters... Without knowing your structure of data, a short example of IOV-like variability would be: MA1=0 MA2=0 IF(MACH=1)MA1=1 IF(MACH=2)MA2=1 ;Intermachine variability: ETAM = MA1*ETA(Y)+MA2*ETA(Z) PAR= TH(X) *EXP(ETA(X)+ETAM) $OMEGA value1 $OMEGA BLOCK(1) value2 $OMEGA BLOCK(1) same /Johan _________________________________________ Johan Wallin, M.Sci./Ph.D.-student Pharmacometrics Group Div. of Pharmacokinetics and Drug therapy Uppsala University _________________________________________ -----Original Message----- From: [EMAIL PROTECTED] [ mailto:[EMAIL PROTECTED] ] On Behalf Of Denney, William S. Sent: den 15 oktober 2008 14:39 To: [email protected] Subject: [NMusers] More Levels of Random Effects Hello, I'm trying to build a model where I need to have ETAs generated on separately for the ID and another variable (MACH). What I have is a PD experiment that was run on several different machines (MACH). Each machine appears to have a different slope per day and a different calibration. I still need to keep some ETAs on the ID column, so I can't just assign MACH=ID. I've heard that there are ways to do similar to this, but I have been unable to find examples of how to set etas to key off of different columns. Thanks, Bill Notice: This e-mail message, together with any attachments, contains information of Merck & Co., Inc. (One Merck Drive, Whitehouse Station, New Jersey, USA 08889), and/or its affiliates (which may be known outside the United States as Merck Frosst, Merck Sharp & Dohme or MSD and in Japan, as Banyu - direct contact information for affiliates is available at http://www.merck.com/contact/contacts.html ) that may be confidential, proprietary copyrighted and/or legally privileged. It is intended solely for the use of the individual or entity named on this message. If you are not the intended recipient, and have received this message in error, please notify us immediately by reply e-mail and then delete it from your system. -- Paul R. Hutson, Pharm.D. Associate Professor UW School of Pharmacy 777 Highland Avenue Madison WI 53705-2222 Tel 608.263.2496 Fax 608.265.5421 Pager 608.265.7000, p7856

-----Original Message----- From: [EMAIL PROTECTED] on behalf of [EMAIL PROTECTED] Sent: Fri 10/17/2008 10:26 To: Leonid Gibiansky Cc: nmusers; Nick Holford; [EMAIL PROTECTED] Subject: Re: [NMusers] More Levels of Random Effects I suppose it really comes down to what you are going to do with the model. Many times I have checked the SAME assumption when modeling inter-occasional variability, and found that sometimes, removing it does indeed improve the fit significantly. In almost every case I've retained it (despite the better fit) for the exact reasons Leonid cites: it makes your model completely data-dependent. I suppose if the model was meant as a description or summary of the data, then it would not matter, but I make all of my models work for a living... There is a related topic which I'd be interested in hearing from the group about. Many times, we take several Phase 1 studies and put them together in order to develop a population model early in development. I've learned through experience to be careful when doing this, because often, one or more studies will appear to have a different mean response for some parameter, e.g., CL or V2. Of course, you can introduce study as a covariate, but this intrduces the same problem as above; in a simulation context, which CL value is correct? There is a work-around for this (use both values) but this doubles the number of simulations you have to do, and from a scientific stand-point it is not very satisfying. What we need is another level of random effects at the STUDY level, similar to what is routinely done when performing hierarchical modeling in something like WinBUGS. I'd love to see this feature in a future version of NONMEM. "Leonid Gibiansky" <[EMAIL PROTECTED]> Sent by: [EMAIL PROTECTED] 17-Oct-2008 09:30 To "Nick Holford" <[EMAIL PROTECTED]> cc "nmusers" <[email protected]> Subject Re: [NMusers] More Levels of Random Effects Nick, This is exactly what I meant. If you have a model for English, Irish and Welsh, you may at least extrapolate it to Australians and New Zealanders (of British descent :) ). With occasion treated as non-ordered categorical covariate, you cannot extrapolate the model at all because time cannot be repeated, so your covariate (occasion) will have different value (level) at any future trial. Leonid -------------------------------------- Leonid Gibiansky, Ph.D. President, QuantPharm LLC web: www.quantpharm.com e-mail: LGibiansky at quantpharm.com tel: (301) 767 5566 Nick Holford wrote: > Leonid, > > I dont understand what you mean by "we lose predictive power of the > model: we do not know what will be > the variability on the next occasion.". > > Or are you concerned about the situation where you have say 3 occasions > and the IOV seems to be different on each occasion but you now want to > predict the IOV for a future study on the 4th occasion? > > I agree it is hard to extrapolate to future occasions but this seems to > be just like any other non-ordered categorical covariate - e.g. if we > see differences between English, Irish and Welsh what difference would > you expect for Russians? :-) > > Nick > > > Leonid Gibiansky wrote: >> Hi Xia, Nick >> Technically, one can use different variances on different occasions but >> then we loose predictive power of the model: we do not know what will be >> the variability on the next occasion. One can use occasion-dependent IOV >> variance to check for trends (for example, to investigate the time >> dependence of the IOV variability, or to check whether the first >> occasion (e.g., after the first dose of a long-term study) is for some >> reasons different from the others) but the final model should have some >> condition that specifies the relations of IOV variances at different >> occasion (SAME being the simplest, most reasonable and the most-often >> used option). >> >> Thanks >> Leonid >> >> -------------------------------------- >> Leonid Gibiansky, Ph.D. >> President, QuantPharm LLC >> web: www.quantpharm.com >> e-mail: LGibiansky at quantpharm.com >> tel: (301) 767 5566 >> >> >> >> >> Nick Holford wrote: >>> Xia, >>> >>> There is no requirement to use the SAME option. However, it is a >>> reasonable model for IOV that it has the same variability on each >>> occasion. >>> >>> If you dont use the SAME option then you just need to estimate an >>> extra OMEGA parameter for each occasion you dont use SAME. You can >>> test if the SAME assumption is supported by your data or not by >>> comparing models with and without SAME. >>> >>> Nick >>> >>> PS Your computer clock seems to be more than 2 years out of date. >>> Your email claimed it was sent in 17 Jan 2006. >>> >>> Xia Li wrote: >>>> Dear All, >>>> Do we have to assume the variability between all occasions are the >>>> same when >>>> we estimate IOV? What will happen if I don't use the 'same' >>>> constrain in the >>>> $OMEGA BLOCK statement? Any input will be appreciated. >>>> >>>> Best, >>>> >>>> Xia Li >>>> >>>> -----Original Message----- >>>> From: [EMAIL PROTECTED] >>>> [mailto:[EMAIL PROTECTED] On >>>> Behalf Of Johan Wallin >>>> Sent: Wednesday, October 15, 2008 9:17 AM >>>> To: [email protected] >>>> Subject: RE: [NMusers] More Levels of Random Effects >>>> >>>> Bill, >>>> Is it really an eta you want, or is this rather solved by different >>>> error >>>> models for the different machines? >>>> >>>> If still want etas, one way would be to model in the same way as >>>> IOV. In the >>>> case of intermachine-variability you would have to assume the >>>> variability >>>> between all machines are the same... Or would you rather assume >>>> interindividual variability is different with >>>> different machine, and you then would want one eta for TH(X) for every >>>> machine...? It depends on what you mean by different slope every day, >>>> regarding on what your experiments like, but calibration differences >>>> should >>>> perhaps be taken care of by looking into your error model, eta on >>>> epsilon >>>> for starters... >>>> >>>> Without knowing your structure of data, a short example of IOV-like >>>> variability would be: >>>> >>>> MA1=0 >>>> MA2=0 >>>> IF(MACH=1)MA1=1 >>>> IF(MACH=2)MA2=1 >>>> ;Intermachine variability: >>>> ETAM = MA1*ETA(Y)+MA2*ETA(Z) >>>> >>>> PAR= TH(X) *EXP(ETA(X)+ETAM) >>>> >>>> $OMEGA value1 >>>> $OMEGA BLOCK(1) value2 >>>> $OMEGA BLOCK(1) same >>>> >>>> /Johan >>>> >>>> >>>> _________________________________________ >>>> Johan Wallin, M.Sci./Ph.D.-student >>>> Pharmacometrics Group >>>> Div. of Pharmacokinetics and Drug therapy >>>> Uppsala University >>>> _________________________________________ >>>> >>>> >>>> -----Original Message----- >>>> From: [EMAIL PROTECTED] >>>> [mailto:[EMAIL PROTECTED] On >>>> Behalf Of Denney, William S. >>>> Sent: den 15 oktober 2008 14:39 >>>> To: [email protected] >>>> Subject: [NMusers] More Levels of Random Effects >>>> >>>> Hello, >>>> >>>> I'm trying to build a model where I need to have ETAs generated on >>>> separately for the ID and another variable (MACH). What I have is a PD >>>> experiment that was run on several different machines (MACH). Each >>>> machine appears to have a different slope per day and a different >>>> calibration. I still need to keep some ETAs on the ID column, so I >>>> can't just assign MACH=ID. >>>> >>>> I've heard that there are ways to do similar to this, but I have been >>>> unable to find examples of how to set etas to key off of different >>>> columns. >>>> >>>> Thanks, >>>> >>>> Bill >>>> Notice: This e-mail message, together with any attachments, contains >>>> information of Merck & Co., Inc. (One Merck Drive, Whitehouse Station, >>>> New Jersey, USA 08889), and/or its affiliates (which may be known >>>> outside the United States as Merck Frosst, Merck Sharp & Dohme or >>>> MSD and in Japan, as Banyu - direct contact information for >>>> affiliates is >>>> available at http://www.merck.com/contact/contacts.html) that may be >>>> confidential, proprietary copyrighted and/or legally privileged. It is >>>> intended solely for the use of the individual or entity named on this >>>> message. If you are not the intended recipient, and have received this >>>> message in error, please notify us immediately by reply e-mail and >>>> then delete it from your system. >>>> >>>> >>>> >>> >> >

-----Original Message----- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Leonid Gibiansky Sent: Friday, October 17, 2008 10:35 AM To: [EMAIL PROTECTED] Cc: nmusers; Nick Holford Subject: Re: [NMusers] More Levels of Random Effects The next level of random effects can be introduced in NONMEM using PRIOR subroutine. It existed as undocumented feature even in Nonmem V. Now (in Nonmem VI) it is official (see help). Leonid -------------------------------------- Leonid Gibiansky, Ph.D. President, QuantPharm LLC web: www.quantpharm.com e-mail: LGibiansky at quantpharm.com tel: (301) 767 5566 [EMAIL PROTECTED] wrote: > > > I suppose it really comes down to what you are going to do with the > model. Many times I have checked the SAME assumption when modeling > inter-occasional variability, and found that sometimes, removing it does > indeed improve the fit significantly. In almost every case I've > retained it (despite the better fit) for the exact reasons Leonid cites: > it makes your model completely data-dependent. I suppose if the model > was meant as a description or summary of the data, then it would not > matter, but I make all of my models work for a living... > > There is a related topic which I'd be interested in hearing from the > group about. Many times, we take several Phase 1 studies and put them > together in order to develop a population model early in development. > I've learned through experience to be careful when doing this, because > often, one or more studies will appear to have a different mean response > for some parameter, e.g., CL or V2. Of course, you can introduce study > as a covariate, but this intrduces the same problem as above; in a > simulation context, which CL value is correct? There is a work-around > for this (use both values) but this doubles the number of simulations > you have to do, and from a scientific stand-point it is not very > satisfying. What we need is another level of random effects at the STUDY > level, similar to what is routinely done when performing hierarchical > modeling in something like WinBUGS. I'd love to see this feature in a > future version of NONMEM. > > > > > > > *"Leonid Gibiansky" <[EMAIL PROTECTED]>* > Sent by: [EMAIL PROTECTED] > > 17-Oct-2008 09:30 > > > To > "Nick Holford" <[EMAIL PROTECTED]> > cc > "nmusers" <[email protected]> > Subject > Re: [NMusers] More Levels of Random Effects > > > > > > > > > Nick, > > This is exactly what I meant. If you have a model for English, Irish and > Welsh, you may at least extrapolate it to Australians and New Zealanders > (of British descent :) ). With occasion treated as non-ordered > categorical covariate, you cannot extrapolate the model at all because > time cannot be repeated, so your covariate (occasion) will have > different value (level) at any future trial. > > Leonid > > -------------------------------------- > Leonid Gibiansky, Ph.D. > President, QuantPharm LLC > web: www.quantpharm.com > e-mail: LGibiansky at quantpharm.com > tel: (301) 767 5566 > > > > > Nick Holford wrote: > > Leonid, > > > > I dont understand what you mean by "we lose predictive power of the > > model: we do not know what will be > > the variability on the next occasion.". > > > > Or are you concerned about the situation where you have say 3 occasions > > and the IOV seems to be different on each occasion but you now want to > > predict the IOV for a future study on the 4th occasion? > > > > I agree it is hard to extrapolate to future occasions but this seems to > > be just like any other non-ordered categorical covariate - e.g. if we > > see differences between English, Irish and Welsh what difference would > > you expect for Russians? :-) > > > > Nick > > > > > > Leonid Gibiansky wrote: > >> Hi Xia, Nick > >> Technically, one can use different variances on different occasions but > >> then we loose predictive power of the model: we do not know what will be > >> the variability on the next occasion. One can use occasion-dependent IOV > >> variance to check for trends (for example, to investigate the time > >> dependence of the IOV variability, or to check whether the first > >> occasion (e.g., after the first dose of a long-term study) is for some > >> reasons different from the others) but the final model should have some > >> condition that specifies the relations of IOV variances at different > >> occasion (SAME being the simplest, most reasonable and the most-often > >> used option). > >> > >> Thanks > >> Leonid > >> > >> -------------------------------------- > >> Leonid Gibiansky, Ph.D. > >> President, QuantPharm LLC > >> web: www.quantpharm.com > >> e-mail: LGibiansky at quantpharm.com > >> tel: (301) 767 5566 > >> > >> > >> > >> > >> Nick Holford wrote: > >>> Xia, > >>> > >>> There is no requirement to use the SAME option. However, it is a > >>> reasonable model for IOV that it has the same variability on each > >>> occasion. > >>> > >>> If you dont use the SAME option then you just need to estimate an > >>> extra OMEGA parameter for each occasion you dont use SAME. You can > >>> test if the SAME assumption is supported by your data or not by > >>> comparing models with and without SAME. > >>> > >>> Nick > >>> > >>> PS Your computer clock seems to be more than 2 years out of date. > >>> Your email claimed it was sent in 17 Jan 2006. > >>> > >>> Xia Li wrote: > >>>> Dear All, > >>>> Do we have to assume the variability between all occasions are the > >>>> same when > >>>> we estimate IOV? What will happen if I don't use the 'same' > >>>> constrain in the > >>>> $OMEGA BLOCK statement? Any input will be appreciated. > >>>> > >>>> Best, > >>>> > >>>> Xia Li > >>>> > >>>> -----Original Message----- > >>>> From: [EMAIL PROTECTED] > >>>> [mailto:[EMAIL PROTECTED] On > >>>> Behalf Of Johan Wallin > >>>> Sent: Wednesday, October 15, 2008 9:17 AM > >>>> To: [email protected] > >>>> Subject: RE: [NMusers] More Levels of Random Effects > >>>> > >>>> Bill, > >>>> Is it really an eta you want, or is this rather solved by different > >>>> error > >>>> models for the different machines? > >>>> > >>>> If still want etas, one way would be to model in the same way as > >>>> IOV. In the > >>>> case of intermachine-variability you would have to assume the > >>>> variability > >>>> between all machines are the same... Or would you rather assume > >>>> interindividual variability is different with > >>>> different machine, and you then would want one eta for TH(X) for every > >>>> machine...? It depends on what you mean by different slope every day, > >>>> regarding on what your experiments like, but calibration differences > >>>> should > >>>> perhaps be taken care of by looking into your error model, eta on > >>>> epsilon > >>>> for starters... > >>>> > >>>> Without knowing your structure of data, a short example of IOV-like > >>>> variability would be: > >>>> > >>>> MA1=0 > >>>> MA2=0 > >>>> IF(MACH=1)MA1=1 > >>>> IF(MACH=2)MA2=1 > >>>> ;Intermachine variability: > >>>> ETAM = MA1*ETA(Y)+MA2*ETA(Z) > >>>> > >>>> PAR= TH(X) *EXP(ETA(X)+ETAM) > >>>> > >>>> $OMEGA value1 > >>>> $OMEGA BLOCK(1) value2 > >>>> $OMEGA BLOCK(1) same > >>>> > >>>> /Johan > >>>> > >>>> > >>>> _________________________________________ > >>>> Johan Wallin, M.Sci./Ph.D.-student > >>>> Pharmacometrics Group > >>>> Div. of Pharmacokinetics and Drug therapy > >>>> Uppsala University > >>>> _________________________________________ > >>>> > >>>> > >>>> -----Original Message----- > >>>> From: [EMAIL PROTECTED] > >>>> [mailto:[EMAIL PROTECTED] On > >>>> Behalf Of Denney, William S. > >>>> Sent: den 15 oktober 2008 14:39 > >>>> To: [email protected] > >>>> Subject: [NMusers] More Levels of Random Effects > >>>> > >>>> Hello, > >>>> > >>>> I'm trying to build a model where I need to have ETAs generated on > >>>> separately for the ID and another variable (MACH). What I have is > a PD > >>>> experiment that was run on several different machines (MACH). Each > >>>> machine appears to have a different slope per day and a different > >>>> calibration. I still need to keep some ETAs on the ID column, so I > >>>> can't just assign MACH=ID. > >>>> > >>>> I've heard that there are ways to do similar to this, but I have been > >>>> unable to find examples of how to set etas to key off of different > >>>> columns. > >>>> > >>>> Thanks, > >>>> > >>>> Bill Notice: This e-mail message, together with any attachments, contains information of Merck & Co., Inc. (One Merck Drive, Whitehouse Station, New Jersey, USA 08889), and/or its affiliates (which may be known outside the United States as Merck Frosst, Merck Sharp & Dohme or MSD and in Japan, as Banyu - direct contact information for affiliates is available at http://www.merck.com/contact/contacts.html) that may be confidential, proprietary copyrighted and/or legally privileged. It is intended solely for the use of the individual or entity named on this message. If you are not the intended recipient, and have received this message in error, please notify us immediately by reply e-mail and then delete it from your system.

> -----Original Message----- > From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] > On Behalf Of Leonid Gibiansky > Sent: Friday, October 17, 2008 10:35 AM > To: [EMAIL PROTECTED] > Cc: nmusers; Nick Holford > Subject: Re: [NMusers] More Levels of Random Effects > > The next level of random effects can be introduced in NONMEM using PRIOR > > subroutine. It existed as undocumented feature even in Nonmem V. Now (in > > Nonmem VI) it is official (see help). > Leonid > > -------------------------------------- > Leonid Gibiansky, Ph.D. > President, QuantPharm LLC > web: www.quantpharm.com > e-mail: LGibiansky at quantpharm.com > tel: (301) 767 5566 > > [EMAIL PROTECTED] wrote: > > > I suppose it really comes down to what you are going to do with the model. Many times I have checked the SAME assumption when modeling inter-occasional variability, and found that sometimes, removing it > > does > > > indeed improve the fit significantly. In almost every case I've retained it (despite the better fit) for the exact reasons Leonid > > cites: > > > it makes your model completely data-dependent. I suppose if the model was meant as a description or summary of the data, then it would not matter, but I make all of my models work for a living... > > > > There is a related topic which I'd be interested in hearing from the group about. Many times, we take several Phase 1 studies and put them together in order to develop a population model early in development. I've learned through experience to be careful when doing this, because > > > often, one or more studies will appear to have a different mean > > response > > > for some parameter, e.g., CL or V2. Of course, you can introduce study > > > as a covariate, but this intrduces the same problem as above; in a simulation context, which CL value is correct? There is a work-around for this (use both values) but this doubles the number of simulations you have to do, and from a scientific stand-point it is not very satisfying. What we need is another level of random effects at the > > STUDY > > > level, similar to what is routinely done when performing hierarchical modeling in something like WinBUGS. I'd love to see this feature in a future version of NONMEM. > > > > *"Leonid Gibiansky" <[EMAIL PROTECTED]>* > > Sent by: [EMAIL PROTECTED] > > > > 17-Oct-2008 09:30 > > > > To > > > > "Nick Holford" <[EMAIL PROTECTED]> > > cc > > "nmusers" <[email protected]> > > Subject > > Re: [NMusers] More Levels of Random Effects > > > > Nick, > > > > This is exactly what I meant. If you have a model for English, Irish > > and > > > Welsh, you may at least extrapolate it to Australians and New > > Zealanders > > > (of British descent :) ). With occasion treated as non-ordered > > categorical covariate, you cannot extrapolate the model at all because > > time cannot be repeated, so your covariate (occasion) will have > > different value (level) at any future trial. > > > > Leonid > > > > -------------------------------------- > > Leonid Gibiansky, Ph.D. > > President, QuantPharm LLC > > web: www.quantpharm.com > > e-mail: LGibiansky at quantpharm.com > > tel: (301) 767 5566 > > > > Nick Holford wrote: > > > Leonid, > > > > > > I dont understand what you mean by "we lose predictive power of the > > > model: we do not know what will be > > > the variability on the next occasion.". > > > > > > Or are you concerned about the situation where you have say 3 > > occasions > > > > and the IOV seems to be different on each occasion but you now want > > to > > > > predict the IOV for a future study on the 4th occasion? > > > > > > I agree it is hard to extrapolate to future occasions but this > > seems to > > > > be just like any other non-ordered categorical covariate - e.g. if > > we > > > > see differences between English, Irish and Welsh what difference > > would > > > > you expect for Russians? :-) > > > > > > Nick > > > > > > > > > Leonid Gibiansky wrote: > > >> Hi Xia, Nick > > >> Technically, one can use different variances on different > > occasions but > > > >> then we loose predictive power of the model: we do not know what > > will be > > > >> the variability on the next occasion. One can use > > occasion-dependent IOV > > > >> variance to check for trends (for example, to investigate the time > > >> dependence of the IOV variability, or to check whether the first > > >> occasion (e.g., after the first dose of a long-term study) is for > > some > > > >> reasons different from the others) but the final model should have > > some > > > >> condition that specifies the relations of IOV variances at > > different > > > >> occasion (SAME being the simplest, most reasonable and the > > most-often > > > >> used option). > > >> > > >> Thanks > > >> Leonid > > >> > > >> -------------------------------------- > > >> Leonid Gibiansky, Ph.D. > > >> President, QuantPharm LLC > > >> web: www.quantpharm.com > > >> e-mail: LGibiansky at quantpharm.com > > >> tel: (301) 767 5566 > > >> > > >> > > >> > > >> > > >> Nick Holford wrote: > > >>> Xia, > > >>> > > >>> There is no requirement to use the SAME option. However, it is a > > >>> reasonable model for IOV that it has the same variability on each > > >>> occasion. > > >>> > > >>> If you dont use the SAME option then you just need to estimate an > > >>> extra OMEGA parameter for each occasion you dont use SAME. You > > can > > > >>> test if the SAME assumption is supported by your data or not by > > >>> comparing models with and without SAME. > > >>> > > >>> Nick > > >>> > > >>> PS Your computer clock seems to be more than 2 years out of date. > > >>> Your email claimed it was sent in 17 Jan 2006. > > >>> > > >>> Xia Li wrote: > > >>>> Dear All, > > >>>> Do we have to assume the variability between all occasions are > > the > > > >>>> same when > > >>>> we estimate IOV? What will happen if I don't use the 'same' > > >>>> constrain in the > > >>>> $OMEGA BLOCK statement? Any input will be appreciated. > > >>>> > > >>>> Best, > > >>>> > > >>>> Xia Li > > >>>> > > >>>> -----Original Message----- > > >>>> From: [EMAIL PROTECTED] > > >>>> [mailto:[EMAIL PROTECTED] On > > >>>> Behalf Of Johan Wallin > > >>>> Sent: Wednesday, October 15, 2008 9:17 AM > > >>>> To: [email protected] > > >>>> Subject: RE: [NMusers] More Levels of Random Effects > > >>>> > > >>>> Bill, > > >>>> Is it really an eta you want, or is this rather solved by > > different > > > >>>> error > > >>>> models for the different machines? > > >>>> > > >>>> If still want etas, one way would be to model in the same way as > > >>>> IOV. In the > > >>>> case of intermachine-variability you would have to assume the > > >>>> variability > > >>>> between all machines are the same... Or would you rather assume > > >>>> interindividual variability is different with > > >>>> different machine, and you then would want one eta for TH(X) for > > every > > > >>>> machine...? It depends on what you mean by different slope every > > day, > > > >>>> regarding on what your experiments like, but calibration > > differences > > > >>>> should > > >>>> perhaps be taken care of by looking into your error model, eta > > on > > > >>>> epsilon > > >>>> for starters... > > >>>> > > >>>> Without knowing your structure of data, a short example of > > IOV-like > > > >>>> variability would be: > > >>>> > > >>>> MA1=0 > > >>>> MA2=0 > > >>>> IF(MACH=1)MA1=1 > > >>>> IF(MACH=2)MA2=1 > > >>>> ;Intermachine variability: > > >>>> ETAM = MA1*ETA(Y)+MA2*ETA(Z) > > >>>> > > >>>> PAR= TH(X) *EXP(ETA(X)+ETAM) > > >>>> > > >>>> $OMEGA value1 > > >>>> $OMEGA BLOCK(1) value2 > > >>>> $OMEGA BLOCK(1) same > > >>>> > > >>>> /Johan > > >>>> > > >>>> > > >>>> _________________________________________ > > >>>> Johan Wallin, M.Sci./Ph.D.-student > > >>>> Pharmacometrics Group > > >>>> Div. of Pharmacokinetics and Drug therapy > > >>>> Uppsala University > > >>>> _________________________________________ > > >>>> > > >>>> > > >>>> -----Original Message----- > > >>>> From: [EMAIL PROTECTED] > > >>>> [mailto:[EMAIL PROTECTED] On > > >>>> Behalf Of Denney, William S. > > >>>> Sent: den 15 oktober 2008 14:39 > > >>>> To: [email protected] > > >>>> Subject: [NMusers] More Levels of Random Effects > > >>>> > > >>>> Hello, > > >>>> > > >>>> I'm trying to build a model where I need to have ETAs generated > > on > > > >>>> separately for the ID and another variable (MACH). What I have > > is > > > a PD > > >>>> experiment that was run on several different machines (MACH). > > Each > > > >>>> machine appears to have a different slope per day and a > > different > > > >>>> calibration. I still need to keep some ETAs on the ID column, > > so I > > > >>>> can't just assign MACH=ID. > > >>>> > > >>>> I've heard that there are ways to do similar to this, but I have > > been > > > >>>> unable to find examples of how to set etas to key off of > > different > > > >>>> columns. > > >>>> > > >>>> Thanks, > > >>>> > > >>>> Bill > > Notice: This e-mail message, together with any attachments, contains > information of Merck & Co., Inc. (One Merck Drive, Whitehouse Station, > New Jersey, USA 08889), and/or its affiliates (which may be known > outside the United States as Merck Frosst, Merck Sharp & Dohme or > MSD and in Japan, as Banyu - direct contact information for affiliates is > available at http://www.merck.com/contact/contacts.html) that may be > confidential, proprietary copyrighted and/or legally privileged. It is > intended solely for the use of the individual or entity named on this > message. If you are not the intended recipient, and have received this > message in error, please notify us immediately by reply e-mail and > then delete it from your system.