Re: PRED for BLQ-like observations

On Sat, Nov 21, 2015 at 10:23 AM, Leonid Gibiansky wrote: > As we always do post-processing any way, one option is to use PRED provided by Nonmem to compute inverse cumulative distribution function (qnorm in R, for example) and then restore PRED value > IPRED = ... > W = ... > LLOQ = ... > IF(BQL.EQ.1) Y=PHI((LLOQ-IPRED)/W) > > If you have PRED LLOQ and W in the Nonmem output file (that you read to the R data frame "data"), you can re-define > > if(BQL == 1) data$PRED = data$LLOQ - qnorm(data$PRED)*data$W # R code > > (I have not tested it; use on your own risk :) ) > > Leonid > > -------------------------------------- > Leonid Gibiansky, Ph.D. > President, QuantPharm LLC > web: www.quantpharm.com > e-mail: LGibiansky at quantpharm.com > tel: (301) 767 5566 > > On 11/20/2015 5:15 PM, Pavel Belo wrote: > > > That is perfect! > > P. > > On Fri, Nov 20, 2015 at 04:04 PM, Bauer, Robert wrote: > > > > Unfortunately adding records to estimation slows down estimation > > even with MDV=1 records. Please do a search on MDV=101 option in > > > > nm730.pdf 1 (section Ignoring Non-Impact Records During Estimation > > > > (NM73)). These records will be used only on the $TABLE step. > > > > Robert J. Bauer, Ph.D. > > > > Vice President, Pharmacometrics R&D > > > > ICON Early Phase > > > > Office: (215) 616-6428 > > > > Mobile: (925) 286-0769 > > > > [email protected] > > www.iconplc.com > > *From:*[email protected] > > [mailto:[email protected]] *On Behalf Of *Nick Holford > > *Sent:* Friday, November 20, 2015 12:39 PM > > *To:* nmusers > > *Subject:* Re: [NMusers] PRED for BLQ-like observations > > > > 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:*[email protected] > > > [mailto:[email protected]] *On Behalf Of *Pavel Belo > > > *Sent:* Friday, November 20, 2015 11:47 AM > > > > > > *To:* [email protected] *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: [email protected] http://holford.fmhs.auckland.ac.nz/ > > > > 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. > > > >