From: "TKT (Thomas Klitgaard)" tkt@novonordisk.com Subject: [NMusers] Outliers and the FDA guideline Date: Wed, August 18, 2004 3:21 am Dear all, In the FDA Guidance For industry: "Population Pharmacokinetics" (February 1999) section VII, C (p. 11 onwards) states the following about outliers: "The statistical definition of an outlier is, to some extent, arbitrary. The reasons for declaring a data point to be an outlier should be statistically convincing and, if possible, prespecified in the protocol. 1* Any physiological or study-related event that renders the data unusable should be explained in the study report. 2* A distinction should be made between outlying individuals (intersubject variability) and outlier data points (intrasubject variability). Because of the exploratory nature of population analysis, the study protocol may not specify a procedure for dealing with outliers. In such a situation, it would be possible to perform model building on the reduced data set (i.e., the data set without outliers) to 3* reanalyze the entire data set (including the outliers) using the final population model, and to discuss the difference in the results. Including extreme outliers is not a good practice when using least-squares or normal-theory type estimation methods, as such outliers 4* inevitably have a disproportionate effect on estimates. Also, it is well known that for most biological phenomena, outlying observations are far more frequent than suggested by the normal distribution (i.e., biological distributions are heavy-tailed). Some robust methods of population analysis have recently been suggested, and these may allow outliers to be retained without giving them undue weight (38-40). Outliers should be specified in a separate appendix to the report, with all data available" Our interpretation of this is the following: Either the criteria may are predefined, in a statistically reasonable way, in the protocol (*1) - or they're not, in which case (*3) model building on the reduced data set could be performed followed by a re-run of the final model on the full data set. (Section 2* does not appear to be an outlier issue, as it pertains to a non-result. ) My questions are: 1) What would be a statically convincing criterion, for the first approach in section 1*?. Note that for this approach, the criterion should be stated a priori in the protocol. 2) The procedure explained in 3*) requires that the outliers be known before model development, hence excluding the application of a model-based exclusion criterion applied on the full dataset first (e.g. "exclude if WRES>4"), followed by a re-run on the reduced data set and a discussion of the sensitivity to the applied censoring. How do you get to know you outliers beforehand? 3) How much flexibility do you allow yourselves with the criterions - would you go with common-sense based "looks better - CV% are notably smaller" instead of strict rules. Would the FDA? Thanks in advance. Thomas Klitgaard, Pharmacometrics, Novo Nordisk Denmark _______________________________________________________ From: "Robert L. James" rjames@rhoworld.com Subject: RE: [NMusers] Outliers and the FDA guideline Date: Wed, August 18, 2004 8:33 am Thomas, I always classify outliers as those that are 1) "highly improbable", and 2) those that are due to "natural extremes" in variation. "Highly improbable" outliers strongly suggest experimental protocol error (for example, the lab technician left out an important reagent when performing the assay or a laboratory appartus wasn't properly zeroed or "warmed up", incomplete mixing of a drug in blood during the first minutes following an bolus arterial injection, etc). "Highly improbable" outliers are ususally near the limit of biologic impossibility. "Natural extremes" outliers, on the other hand, are unlucky but real. Biologic systems can occassionaly vary producing very extreme values. For, "Highly improbable" outliers, I simply discard the outlier from all analyses and make a note of discarding it in my results. However, discarding "natural extreme" outliers are statistically problematic. To discard them outright will bias the results by shrinking the variance. Including them may make it very difficult to fit a good model. For "natural extreme" outliers I initially exclude them from the data during the model fitting. But then for my final model run, I'll put "natural extreme" outliers back into my model so that the variance structure reflects the natural (although extreme) variability. For this final run, I may or may not fix the theta parameters to the estimates obtained by the earlier model (without the outliers). I report model diagnostics using the final model fit which was based on the data that included the "natural extreme" outliers. Robert James _______________________________________________________ From: "Hutmacher, Matt" Matt.Hutmacher@pfizer.com Subject: RE: [NMusers] Outliers and the FDA guideline Date: Wed, August 18, 2004 11:43 am Outliers are a difficult subject. I think if you asked 10 different modelers you would get 10 different answers on how to handle them. I would suggest a systematic approach to data elimination in general. A systematic approach is the analyst's best surrogate for objectivity, since only the reviewer/audience can determine ultimately the level of objectivity. For an analysis which will be submitted to a regulatory authority, I would advocate specifying the criteria for classifying data as outliers a priori (before unblinding the data) in a population modeling analysis plan. This document should also specify how the analyst will determine if the outlier is influential and how he/she will proceed if the outlier is influential. This systematic, pre-specified approach will mitigate the subjectivity induced by eliminating data a posteriori. In general, my opinion is that it is best to include all the data whenever possible. If there are number of outliers, one might try using a mixture of epsilons (and hence variances) to down-weight these observations and reduce their influence. Sometimes, handling of outliers will depend on the goal of the analysis, and the outliers may not fulfill pre-specified criteria such as |residuals|>=3 or 4. For example, we did a population PK (PPK) analysis on some sparse data. Because the estimated CV of residual variation was >80%, no data appeared as outliers by the usual residual criteria. When you looked at the data (2 samples, 1 hour apart for each individual for each visit), some visits appeared to have concentrations, which were ascending with time (as if absorption were occurring). However, these pseudo-absorption phases were occurring much to late in the dosing interval; these were "highly improbable" observations (as below) given the drug had very predictable absorption in every other study. We figured these results were do to incorrect recollection/recording of the last administered dose. Thus, the large CV estimate was from the model predicting elimination when the data were exhibiting this pseudo-absorption. Ultimately, the purpose of the PPK analysis was to test for influential covariates. The large %CV would reduce the power to detect these covariates, so (in my opinion) it was of interest to eliminate these data points (since any attempt we made to include them failed) to better perform the exploratory covariate analysis. To mitigate the subjectivity induced by selecting the points by visual inspection (again 10 analysts might end up with 10 different data sets), we used a mixture model on Tlag. Three mixtures were discovered, the "typical", "unrealistic 1", and "unrealistic 2" absorbers. The model classified each visit for each patient into one of these three categories. We plotted the data by the three mixture classifications and it was easy to see that these data had different, unlikely characteristics. These data were deleted, and the CV was reduced to ~30%. The reviewer/audience could disagree with the procedure, but if he/she thought it was reasonable, then there would be no argument over classifying which data should be eliminated. Matt _______________________________________________________ From: Mats Karlsson [mailto:mats.karlsson@farmbio.uu.se] Subject: RE: [NMusers] Outliers and the FDA guideline Date: Monday, August 23, 2004 8:22 AM Hi Matt, Nice advice. I was just wondering about a technicality. In applying the mixture model to Tlag in a visit-specific manner, did you treat each visit as a separate subject or did you apply such a mixture while keeping the individual records intact (ie handling occasions as such not as IDs). If the latter, it would be nice to see some code. Thanks, Mats -- Mats Karlsson, PhD Professor of Pharmacometrics Div. of Pharmacokinetics and Drug Therapy Dept. of Pharmaceutical Biosciences Faculty of Pharmacy Uppsala University Box 591 SE-751 24 Uppsala Sweden phone +46 18 471 4105 fax +46 18 471 4003 mats.karlsson@farmbio.uu.se _______________________________________________________ From: "Hutmacher, Matt" Matt.Hutmacher@pfizer.com Subject: RE: [NMusers] Outliers and the FDA guideline Date: Mon, August 23, 2004 8:31 am Hello Mats, We did treat each individual as a separate subject. The variability patterns in the Tlag parameters seemed to be intrasubject (and not intersubject), and since it was a screening procedure, we did not pursue anything more complicated. Matt _______________________________________________________