Bill I suspect that Bob will be able to answer this better. But, it is probably just create something “parallel” to 101.
Dennis Dennis Fisher MD P < (The "P Less Than" Company) Phone: 1-866-PLessThan (1-866-753-7784) Fax: 1-866-PLessThan (1-866-753-7784) www.PLessThan.com <http://www.plessthan.com/> > On Nov 20, 2015, at 1:51 PM, Denney, William S. <william.s.den...@pfizer.com> > wrote: > > Hi Bob and Dennis, > > I was unaware of these values, thanks for the pointer. What is a use case > for MDV=100? The only case I can think of is if you have a measurement that > you don't believe to be accurate, but then it should be removed and/or set to > actually missing before NONMEM. > > Thanks, > > Bill > > On Nov 20, 2015, at 16:42, "Fisher Dennis" <fis...@plessthan.com > <mailto:fis...@plessthan.com>> wrote: > > Even better, take advantage of this (from NMHELP): > > Values of MDV are: > > 0 The DV data item is an observed value, i.e., DV is not miss- > ing. > > 1 The DV data item is not regarded an observed value, i.e., DV > is missing. The DV data item is ignored. | > > 100 Same as MDV=0, but this record is ignored during Estimation | > and Covariance Steps. During other steps, MDV will changed | > to 0. | > > 101 Same as MDV=1, but this record is ignored during Estimation | > and Covariance Steps. During other steps, MDV will changed | > to 1. | > > Reserved variables MDVI1, MDVI2, MDVI3 can be used to over- | > ride values of MDV>100. These variables are defined in | > include file nonmem_reserved_general. > > Dennis > > Dennis Fisher MD > P < (The "P Less Than" Company) > Phone: 1-866-PLessThan (1-866-753-7784) > Fax: 1-866-PLessThan (1-866-753-7784) > www.PLessThan.com > <https://urldefense.proofpoint.com/v2/url?u=http-3A__www.PLessThan.com&d=CwMFAg&c=UE1eNsedaKncO0Yl_u8bfw&r=4WqjVFXRfAkMXd6y3wiAtxtNlICJwFMiogoD6jkpUkg&m=WYu-CQioIB0i7YgHqkz6PNMt7uCea2R_jfzrL98PYfw&s=zc442lHm9Sn0NQGqUpk8TZgvysYTMDYaSmndj8HXFhY&e=> > > > >> On Nov 20, 2015, at 12:38 PM, Nick Holford <n.holf...@auckland.ac.nz >> <mailto:n.holf...@auckland.ac.nz>> wrote: >> >> Pavel, >> Did you test the run time with double the records? >> I would expect that the MDV=1 records would be largely ignored in the >> estimation step and not contribute much to run time. >> Nick >> >> On 21-Nov-15 08:59, Pavel Belo wrote: >>> Thank you Bill, >>> In my case it exactly doubles the number of records... The records are >>> daily measures and the code is running slow enough. I'll split the code >>> into estimation part and one that that is redundant, but uses a larger file >>> and creates an output. It will be something like >>> $EST MAXEVALS=9999 SIG=3 NOABORT PRINT=1 SORT CONSTRAIN=5 >>> METHOD=SAEM NBURN=0 NITER=0 POSTHOC INTERACTION >>> LAPLACIAN GRD=TG(1-7):TS(8-9) CTYPE=3 CINTERVAL=10 >>> I guess the best future way is modify something in NONMEM so there is an >>> option to provide only PRED in the PRED column (version 7.4?). >>> Thanks! >>> Pavel >>> On Fri, Nov 20, 2015 at 01:06 PM, Denney, William S. wrote: >>> >>> Hi Pavel, >>> >>> The easiest way that I know is to generate your data file with one >>> set of rows for estimation with M3 and another row just above or >>> below with MDV=1. NONMEM will then provide PRED and IPRED in the >>> rows with MDV=1. >>> >>> Thanks, >>> >>> Bill >>> >>> *From:*owner-nmus...@globomaxnm.com <mailto:owner-nmus...@globomaxnm.com> >>> [mailto:owner-nmus...@globomaxnm.com >>> <mailto:owner-nmus...@globomaxnm.com>] *On Behalf Of *Pavel Belo >>> *Sent:* Friday, November 20, 2015 11:47 AM >>> *To:* nmusers@globomaxnm.com <mailto:nmusers@globomaxnm.com> >>> *Subject:* [NMusers] PRED for BLQ-like observations >>> >>> Hello The NONMEM Users, >>> >>> When we use M3-like approach, the outputs has PRED for non-missing >>> observations and something else for BLQ (is that PRED=CUMD?). As >>> in the diagnostic figures PRED for BLQs looks like noise, I remove >>> them. It is not always perfect, but OK in for most frequent cases. >>> >>> When we use count data such as a scale with few possible values >>> (for example, 0, 1, 2, 3, 4, 5), it makes more sense to use PHI >>> function (home-made likelihood) for all observations rather than >>> to treat the count as a continuous variable an apply M3-like >>> approach to 1 and 5 while only (as we know, they are like LLOQ and >>> ULOQ). In this case, all PRED values look like noise. A hard way >>> to replace the noise with PRED value is to simulate PRED for each >>> point and merge them with the DV and IPRED data. Is there an easy >>> way? >>> >>> (The model runs well and better than when the count is treated as >>> a continuous variable.) >>> >>> Thanks! >>> >>> Pavel >>> >> >> -- >> Nick Holford, Professor Clinical Pharmacology >> Dept Pharmacology & Clinical Pharmacology, Bldg 503 Room 302A >> University of Auckland,85 Park Rd,Private Bag 92019,Auckland,New Zealand >> office:+64(9)923-6730 mobile:NZ+64(21)46 23 53 >> email: n.holf...@auckland.ac.nz <mailto:n.holf...@auckland.ac.nz> >> http://holford.fmhs.auckland.ac.nz/ >> <https://urldefense.proofpoint.com/v2/url?u=http-3A__holford.fmhs.auckland.ac.nz_&d=CwMFAg&c=UE1eNsedaKncO0Yl_u8bfw&r=4WqjVFXRfAkMXd6y3wiAtxtNlICJwFMiogoD6jkpUkg&m=WYu-CQioIB0i7YgHqkz6PNMt7uCea2R_jfzrL98PYfw&s=mXOHPHTRH3KFb_dSGnMz_dQtDkhhBtasaU3R5_x-Ip4&e=> >> >> Holford SD, Allegaert K, Anderson BJ, Kukanich B, Sousa AB, Steinman A, >> Pypendop, B., Mehvar, R., Giorgi, M., Holford,N.H.G. Parent-metabolite >> pharmacokinetic models - tests of assumptions and predictions. Journal of >> Pharmacology & Clinical Toxicology. 2014;2(2):1023-34. >> Holford N. Clinical pharmacology = disease progression + drug action. Br J >> Clin Pharmacol. 2015;79(1):18-27. >> >
