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] *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.
