Reporting and handling values below the limit of quantification

Dear all, I know I am opening a bit of a can of worms here, and one that has been opened before, but please bear with me.. We are trying to make our case with our analytical laboratory to convince them to release to us (pharmacometrics) the values below the limit of quantification (BLQ), which they normally define as the level below which they can't guarantee 20% CV on the measurement. So far, they have been quite reluctant, because they say that this would go against their SOPs, quality assurance policies, some FDA and EMA guidelines, and what not. However, after months of insisting, it seems like they may finally be open for discussion and asked us to present as much supporting evidence and experience from other labs as possible. Our main argument is that censoring BLQ values may be a reasonable policy when the data needs to be used for other purposes or by clinicians, but for us modelers it is a terrible waste of information, because we have tools to properly deal with the additional level of uncertainty, My first question to the group is then the following - Nick, I explicitly count on you for this one... :) 1. Can you suggest any literature/guidelines/references in support of our cause? a. Any literature clearly advocating for/supporting the release of the BLQ values for pharmacometric modelling. b. Any official guidelines providing/justifying an exception to the standard practice of censoring when the data is handled with modelling c. Any personal experience with your lab or the regulatory authority about this topic So far, I've found some previous threads here on NMUsers and the conclusion section in this paper: Byon W, Fletcher C V, Brundage RC. Impact of censoring data below an arbitrary quantification limit on structural model misspecification. J. Pharmacokinet. Pharmacodyn. 35: 101–16, 2008. The second question is about how to handle these values if we manage to get them (fingers crossed). The released data will have some actual values below the assay validation limit (that we can call "low precision"), and some that will be NA, because sometimes the mass-spec will not be able to identify a peak in the elution profile. 2. What error structure would you recommend to handle a dataset including uncensored BLQ values? a. Should one fix the additive component of the error to a fraction of the LLOQ (say 50%)? And if so, for all samples, even the ones above LLOQ, or only the BLQ ones? b. How would you handle the NAs? Would you impute 0? Impute the lowest value reported? Half of it? c. If you have a series of NAs to impute, would you retain only the first one and exclude the following, or would include them all? Would you have the proportional component of the error apply also to the imputed NAs or not? Any input and help is greatly appreciated! Greetings from Cape Town, Paolo -- ------------------------------------------------ Paolo Denti, PhD Pharmacometrics Group Division of Clinical Pharmacology Department of Medicine University of Cape Town K45 Old Main Building Groote Schuur Hospital Observatory, Cape Town 7925 South Africa phone: +27 21 404 7719 fax: +27 21 448 1989 email: [email protected] ------------------------------------------------ ________________________________ UNIVERSITY OF CAPE TOWN This e-mail is subject to the UCT ICT policies and e-mail disclaimer published on our website at http://www.uct.ac.za/about/policies/emaildisclaimer/ or obtainable from +27 21 650 9111. This e-mail is intended only for the person(s) to whom it is addressed. If the e-mail has reached you in error, please notify the author. If you are not the intended recipient of the e-mail you may not use, disclose, copy, redirect or print the content. If this e-mail is not related to the business of UCT it is sent by the sender in the sender's individual capacity.