Statistical outliers in BE Studies DIA 12 april 2019

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

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

Outliers in BE Studies TPD Guidelines..
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

AUC Values in a 2x2 BE Study
https://doi.org/10.5455/bcp.20120928030834
4

Outliers in BE Studies FDA Guideleines
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

Outliers in BE Studies FDA Guideleines..
 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

Re-Dosing in BE Studies FDA
Guideleines…
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

Before Considering Some New Methods
(not from Guidances)
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

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
9

Real Statistical Reasons
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

Real Statistical Reasons ..
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
11

Outliers in BE Studies Some Methods
(not from Guidances)
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

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
13

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
14

Studentized Residuals Test (Replicate)
Nothing Unusual
Schall R et al.Journal of Biopharmaceutical Statistics, 20: 835–849, 2010
15

Studentized Residuals Reference
(Replicate)
Subject 7 Outlier
16

Subject by Formulation Residuals
(Replicate)
Again nothing Unusual
17

Subject by Formulation Residuals
(Replicate)
Subject 31 Outlier

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
19

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
20

Statistical outliers in BE Studies DIA 12 april 2019

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Statistical outliers in BE Studies DIA 12 april 2019