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Statistical outliers in BE Studies DIA 12 april 2019

This presentation gives effective solutions to outliers issue in bioequivalence trials. It described what would be acceptable to Regulatory agencies as well as some new approaches.

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Statistical outliers in BE Studies DIA 12 april 2019

  1. 1. Prof. (Dr.) Bhaswat S. Chakraborty Emeritus Professor, Institute of Pharmacy, Nirma University Former Sr.VP &Chair, R&D, Cadila Pharmaceuticals Former Director, Biopharmaceutics, Biovail, Toronto Former Sr. Efficacy & Safety Reviewer, TPD (Canadian FDA), Ottawa Outliers and Borderline Study Failures: Creative and Systematic Solutions
  2. 2. Outliers in BE Studies TPD Guidelines © 2019 Prof Bhaswat Chakraborty Comparative bioavailability studies are small studies compared to other clinical trials One or two extreme values could have a large effect on the inference to be made from these small studies The usual parametric assumptions and estimation are not robust against extreme values Specific procedures to identify and account for outliers should be pre‐specified in the protocol No more than 5% of the subjects may be considered to be outliers, unless there are 20 or fewer subjects, in which case only 1 subject may be removed Any protocol for handling outliers should be followed before the results of the analysis are summarised into confidence intervals • i.e., regardless of whether results meet the standard, the outlier protocol should be followed) 2
  3. 3. Outliers in BE Studies TPD Guidelines.. © 2019 Prof Bhaswat Chakraborty The protocol for handling outliers should include the following: 1. The observations should be identified by an outlier test, such as such as a studentized residual being greater than 3 2. Outlirts should be outside the range of all the other observations regardless of formulation (very different from all other observation) 3. The subject in question should be identified as an outlier for all parameters, for either the test or reference product 4. Parameters of interest are usually an AUC and Cmax measure, but in some instances other parameters are required Note that TPD usually does not recommend re‐testing of outliers whether 2x2 crossover or replicate 3
  4. 4. AUC Values in a 2x2 BE Study © 2019 Prof Bhaswat Chakraborty https://doi.org/10.5455/bcp.20120928030834 4
  5. 5. Outliers in BE Studies FDA Guideleines © 2019 Prof Bhaswat Chakraborty The existence of a subject outlier with no protocol violations could indicate one of the following situations:  1. Product Failure • When a subject exhibits an unusually high or low response to one or the other of the products because of a problem with the specific dosage unit administered • This could occur, for example, with a sustained and/or delayed- release dosage form exhibiting dose dumping or a dosage unit with a coating that inhibits dissolution • The unusual response could be present for either the T or R produc; however, in the case of a subpopulation, even if the unusual response is observed on the R, lack of interchangeability of the two products is still an issue 5
  6. 6. Outliers in BE Studies FDA Guideleines.. © 2019 Prof Bhaswat Chakraborty  2. Subject-by-Formulation Interaction • When an individual in the general population in low numbers, shows markedly different value than the majority of the population, and for whom T & R are not bioequivalent For these reasons, deletion of outlier values is generally discouraged, particularly for nonreplicated designs With replicated crossover designs, the retest character of these designs should indicate whether to delete an outlier value or not Sponsors or applicants with these types of data sets may wish to review how to handle outliers with appropriate review staff 6
  7. 7. Re-Dosing in BE Studies FDA Guideleines… © 2019 Prof Bhaswat Chakraborty Re-dosing subjects (controls and outliers) must be taken from the pivotal BE study in which outlier(s) was suspected Selection of sbjects re-dosed would follow the previously defined protocol with a maximum of 20% (6 of 24) If the T/R ratios of all redosed subjects (controls and outliers) fall within the acceptable range (within + 3 Studentized Residuals), the original study might be considered as showing outliers (product or procedure failure) In such a case, results of the original pivotal study can be calculated without the outlier(s) 7
  8. 8. Before Considering Some New Methods (not from Guidances) © 2019 Prof Bhaswat Chakraborty First, it needs to be understood that parametric methods (GLM, ANOVA) using conventional statistics are oversentitiveto outliers 1 or 2 outliers in a N=24 or 36 study can reduce the power of the study so badly that the study can FAIL When can we exclude the detected outlier? • Very difficult, only if a priori defined in the protocol and justified • As mentioned in TPD guidelines, protocol for handling outliers should be followed before the results of the analysis are summarised into confidence intervals • Or FDA – vomiting, product failure, justified after re-dosing etc 8
  9. 9. Three Types of Outliers Type 1: Unexpected observations in the individual concentration level Type 2: Extremely large or small observations in the formulation level Type 3: Unusual subject who exhibits extremely high or low bioavailability with respect to the R in the subject level Relative to Types 2 &3, Type 1 outliers have much less effect on the calculation of AUC and consequently little effect on BE comparison • Exception: when the outlier or missing arises late in the blood concentration curve, the Type 1 outlier can have a potential effect on the conclusion Other very similar outlier classification is possible © 2019 Prof Bhaswat Chakraborty 9
  10. 10. Real Statistical Reasons © 2019 Prof Bhaswat Chakraborty First, this is how a BE study is conducted:  Running an AB/BA cross-over in T & R are compared for each subject with the same observation time points  Log-transforming the AUCs measured in the trial  Fitting a linear model to the log-AUCs in which subject and the period effects are eliminated to produce an estimate of the formulation effect  Estimating the standard error of the estimated formulation effect  Comparing the results to pre-established limits of equivalence, δ1 (a lower limit) and δ2 (an upper limit); BE limits log(0.8) and log(1.25) on the log-AUC scale such that δ1 = − δ2 where 2 = δ = 0 223 Liao J. Journal of Biopharmaceutical Statistics, 17: 393–405, 2007 10
  11. 11. Real Statistical Reasons .. © 2019 Prof Bhaswat Chakraborty Second, the problems with this design in consideration of the outliers  All these approaches are ANOVA-type analysis for log-AUC under assumptions for log-AUC  However, the concentration, not the AUC, is the raw observation. The concentration profile is the concentrations taken from blood samples at various time points after drug administration  They are correlated repeated measurements  Current approaches do not take this into consideration, which may underestimate the variance of the PK parameters  Plus, these approaches do not connect any information from the literature of a PK study, where normal and log-normal are two most commonly assumed distributions for the concentration Liao J. Journal of Biopharmaceutical Statistics, 17: 393–405, 2007 11
  12. 12. Outliers in BE Studies Some Methods (not from Guidances) © 2019 Prof Bhaswat Chakraborty First, it needs to be understood that parametric methods (GLM, ANOVA) using conventional statistics are oversentitive to outliers 1 or 2 outliers in a N=24 or 36 study can reduce the power of the study so badly that the study can FAIL What about Lund’s Test that FDA recommended earlier? • Lund’s test is not appropriate for crossover designs in which the pharmacokinetic responses from the same subject are correlated • It does not take into account the features of the study design 12
  13. 13. Solutions (not from Guidances) Likelihood Distance Test • The likelihood distance (LD) statistic for the ith subject (a potential outlier) is twice of the difference between the log likelihood evaluated by using the estimates from all of the subjects and from the estimates obtained after deleting the ith subject Estimates Distance Test • This method for examining the effect of ith subject in the study is based on the difference in the parameter estimates arising from the deletion of the ith subject • Estimates distance test (ED) is similar to the LD because of accounting the distances of parameter estimates in the case of the presence or absence of the ith subject © 2019 Prof Bhaswat Chakraborty Liao J. Journal of Biopharmaceutical Statistics, 17: 393–405, 2007 13
  14. 14. Solutions (not from Guidances).. Hotelling T2 Test • This is a procedure based on the order statistics of the two sample Hotelling T2 (HT) statistics to identify possible outlying observations • The T2 value is then compared with the critical value to decide whether or not the ith subject is an outlier Mean Shift Test • This procedure is based on the mean-shift model for the ith subject’s response to the jth formulation • Rge test statistic can be used to test whether or not the tth subject is an outlier, and that it is distributed in a particular way © 2019 Prof Bhaswat Chakraborty Liao J. Journal of Biopharmaceutical Statistics, 17: 393–405, 2007 14
  15. 15. Studentized Residuals Test (Replicate) © 2019 Prof Bhaswat Chakraborty Nothing Unusual Schall R et al.Journal of Biopharmaceutical Statistics, 20: 835–849, 2010 15
  16. 16. Studentized Residuals Reference (Replicate) © 2019 Prof Bhaswat Chakraborty Subject 7 Outlier Schall R et al.Journal of Biopharmaceutical Statistics, 20: 835–849, 2010 16
  17. 17. Subject by Formulation Residuals (Replicate) © 2019 Prof Bhaswat Chakraborty Again nothing Unusual Schall R et al.Journal of Biopharmaceutical Statistics, 20: 835–849, 2010 17
  18. 18. Subject by Formulation Residuals (Replicate) © 2019 Prof Bhaswat Chakraborty Subject 31 Outlier Schall R et al.Journal of Biopharmaceutical Statistics, 20: 835–849, 2010
  19. 19. Solutions: Final Thoughts Outliers may only be removed only if they are caused by process or product failures If caused by subject x formulation interaction, do not remove In 2 × 2 crossover trials it is not possible to distinguish outliers caused by process or product failures from outliers caused by subject-by-formulation, solely through statistical criteria The 2-treatment, 2-sequence, 4-period replicate crossover design can be used to identify and classify outliers © 2019 Prof Bhaswat Chakraborty 19
  20. 20. Solutions: Final Thoughts.. One can remove an outlier as a single-data-point outlier, rather than a subject-by-formulation outlier Also, the outlier would have to be present in both AUC and Cmax data • Eg. If outier is caused by vomiting shortly after drug administration, clearly both AUC and Cmax would have to be extremely low (indeed, the complete concentration–time profile would have to be low). Finally, removal of data points from primary analysis should always be supported by a sensitivity analysis • thus, analysis results would usually have to be presented both including and excluding the suspect data points © 2019 Prof Bhaswat Chakraborty 20
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