Download w 1 - Statisticians in the Pharmaceutical Industry

Improving Adverse
Drug Reaction
Information in Product
Labels
PSI Conference
May 2017
Sally Lettis
GlaxoSmithKline
Disclaimer

The views and opinions expressed in the following slides are those of
the individual presenter and should not be attributed to any
organization with which the presenter is employed or affiliated.
Outline
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Current Labelling Practice
Current Labelling Limitations
The problem to solve
The proposed solution
Presentation title
4
Current Labelling Practice
– Product labels are intended to provide health care professionals with
clear and concise prescribing information that will enhance the safe and
effective use of drug products.
Presentation title
5
Current Labelling Practice: US Product Label
– Quantitative approach used in the Adverse Reactions section
– Incidence of ‘‘common’’ ADRs presented
– Common defined as an ADR that occurs at or above a specified
incidence e.g. >=3%
– A comparator must be provided, except in exceptional circumstances.
– Typically, a single ADR table is included; however, more than 1 can be
included when the ADR profile differs substantially from one setting or
population to another
Presentation title
6
Current Labelling Practice: EU SPc
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ADRs (from RCTs) placed into frequency categories
Convention for classification: very common (1/10), common (1/100 to
<1/10), uncommon (1/1,000 to <1/100), rare (1/10,000 to <1/1,000),
and very rare (<1/10,000); (CIOMS III and V)
Comparator typically not included
Category based on crude incidence on drug. Studies included don’t
need common comparator
For drugs for long-term use, there is no representative duration; in
practice, studies of differing durations are combined together
Only in exceptional cases is more than 1 table included for different
populations
Presentation title
7
Current Labelling Limitations: The issue with no
comparator data in label
Disease
Severity
Duration
Incidence
on drug
Category
Incidence
on
Comparator
X
Moderate
3 months
0.6%
Uncommon
-
X
Moderate
6 months
1.5%
Common
1.7%
X
Severe
12 months
6.3%
Common
3.3%
X
Pooled
3.0%
Common
2.6%
Y
Pooled
0.5%
Uncommon
-
X+Y
Pooled
1.0%
Common
-
– In label it is an ADR and is common
– Heterogeneity? Appropriate to combine? Helpful for patient?
– If two similar drugs tested in different populations very different
incidences with no comparator data to contextualise
Presentation title
8
Current Labelling Limitations:
The issue with crude pooling
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Data presented is based on crude pooling of data across multiple
studies: pooling data as if from a single study
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Can lead to “Simpson’s Paradox”: When studies combined, trend seen
in the individual studies either disappears or is reversed
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Why? Can result in an overall baseline risk that is different among
treatment groups due to differing randomization allocations within a study
and different study populations across studies
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Differences that could affect the incidence include age, sex, race,
medical practice, differing time on study
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Common for reporting proportions with an ADR in labels
Not a new issue (Chuang-Stein and Beltangady (2010))
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E.g. Cochran-Mantel-Haenszel to produce a common odds ratio across strata
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If used, applied in Integrated Summaries of Safety
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Too complicated for Product Labels which revert to crude incidences
Current Labelling Limitations:
Example showing misleading incidence proportion from
crude pooling
New treatment,
n/N (%)
Placebo,
n/N (%)
Total
patients
in study
Phase 2 study
30/300 (10%)
10/100 (10%)
400
Phase 3 study
133/700 (19%)
67/350 (19%)
1050
Phase 3 study in
refractory patients
200/500 (40%)
200/500 (40%)
1000
363/1500 (24%)
277/950 (29%)
2450
Incidence
proportion:
crude pooling
N = total number of patients in the group; n = the number in the group that experienced the event
The last row gives the impression the new drug has a beneficial effect,
though the AE incidences are equal in each study.
If you were to do a statistical test -> p=0.007
Current Labelling Limitations:
Why did crude pooling go wrong?
New treatment,
n/N (%)
Placebo,
n/N (%)
Total
Allocation
patients
Ratio
in study
Phase 2 study
30/300 (10%)
10/100 (10%)
400
3:1
Phase 3 study
133/700 (19%)
67/350 (19%)
1050
2:1
Phase 3 study
in refractory
patients
200/500 (40%)
200/500 (40%)
1000
1:1
Incidence
proportion:
crude pooling
363/1500 (24%)
277/950 (29%)
2450
N = total number of patients in the group; n = the number in the group that experienced the event
Studies with uniformly lower ADR proportions (e.g. Phase 2) have more
subjects on new treatment than placebo
The problem to solve: So what do we do?
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We can see what the issue is
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How should we present adjusted proportions?
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Conclusions from crude pooling misleading
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Meta-analytic techniques:
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e.g. Cochran-Mantel-Haenszel weights
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Not easy for non-statistician to understand
16Aug2016
CBI’s Pharmacovigilance Final Rule Summit on IND 2016
12
The Proposed Solution: A better way to go
Study-size Adjusted Percentages
New treatment,
n/N (%)
Placebo,
n/N (%)
Total
Proportion of
patients in total patients in
study
study
Phase 2 study
30/300 (10%)
10/100 (10%)
400
w1 = 400/2450
(16%)
Phase 3 study
133/700 (19%)
67/350 (19%)
1050
w2 = 1050/2450
(43%)
Phase 3 study
in refractory
patients
200/500 (40%)
200/500 (40%)
1000
w3 = 1000/2450
(41%)
Incidence
proportion from
crude pooling
363/1500 (24%)
277/950 (29%)
2450
The Proposed Solution: A better way to go
Study-size Adjusted Percentages
New treatment,
n/N (%)
Placebo,
n/N (%)
Total
Proportion of
patients in total patients in
study
study
Phase 2 study
30/300 (10%)
10/100 (10%)
400
w1 = 400/2450
(16%)
Phase 3 study
133/700 (19%)
67/350 (19%)
1050
w2 = 1050/2450
(43%)
Phase 3 study
in refractory
patients
200/500 (40%)
200/500 (40%)
1000
w3 = 1000/2450
(41%)
Incidence
proportion from
crude pooling
363/1500 (24%)
277/950 (29%)
2450
w1 x (30/300) +
w2 x (133/700) +
w3 x (200/500) =
26%
w1 x (10/100) +
w2 x (67/350) +
w3 x (200/500) =
26%
Study-sizeadjusted
incidence
proportions
Comparison of Weights for New Treatment
New
treatment
n/N (%)
Study
Size
Weights in
Crude Pooling =
Proportion of
total patients on
new drug
Weights in
Study sized
pooling =
Proportion of
patients in
study
Weights using
CMH
aj = (n1jn2j)/
(n1j+n2j)
Study 1
30/300
(10%)
400
w1 = 300/1500
20%
w1 = 400/2450
16%
w1 = a1/aj
13%
Study 2
133/700
(19%)
1050
w2 = 700/1500
47%
w2 = 1050/2450
43%
w2 = a2/aj
42%
Study 3
200/500
(40%)
1000
w3 = 500/1500
33%
w3 = 1000/2450
41%
w3 = a3/aj
45%
N
1500
2450
w1 x (30/300) +
w2 x (133/700) +
w3 x (200/500) =
24%
w1 x (30/300) +
w2 x (133/700) +
w3 x (200/500) =
26%
w1 x (30/300) +
w2 x (133/700) +
w3 x (200/500) =
27%
15
Study Adjusted Size and CMH weights same for each treatment; Crude pooling weights are not
Comparison of Weights for Placebo
Placebo
n/N (%)
Study
Size
Weights in
Crude Pooling
= Proportion of
total patients
on placebo
Weights in Study
sized pooling =
Proportion of
patients in study
Weights using
CMH
aj = (n1jn2j)/
(n1j+n2j)
Study 1
10/100
(10%)
400
w1 = 100/950
11%
w1 = 400/2450
16%
w1 = a1/aj
13%
Study 2
67/350
(19%)
1050
w2 = 350/950
37%
w2 = 1050/2450
43%
w2 = a2/aj
42%
Study 3
200/500
(40%)
1000
w3 = 500/950
53%
w3 = 1000/2450
41%
w3 = a3/aj
45%
N
950
2450
w1 x (10/300) +
w2 x (67/350) +
w3 x (200/500) =
29%
w1 x (10/100) +
w2 x (67/350) +
w3 x (200/500) =
26%
w1 x (10/100) +
w2 x (67/350) +
w3 x (200/500) =
27%
16
Study Adjusted Size and CMH weights same for each treatment; Crude pooling weights are not
Current labelling limitations:
The Issue with “Rule of 3”

If ADR not observed in clinical trials but determined to be causally
related post authorization based on spontaneous reports, ‘‘Rule of 3’’
used to estimate category using sample sizes from clinical trials
 If X is number of patients exposed to drug in all relevant clinical
trials, then frequency category would be 3/X, the upper limit of a
95% CI for the true incidence proportion of the event in question
 This method estimates the incidence proportion by the upper end of a
range of plausible values for the incidence proportion.
 By doing so, ADRs that were never reported in clinical trials can be
assigned to a frequency category that is higher than the category for
ADRs that were reported in clinical trials.
 Anomalies arise when AE was observed in clinical trials at same or
lower incidence than placebo and so not considered ADR. Subsequently
included as ADR based on spontaneous reports. Frequency?
Presentation title
17
Recommendations

That product labels that include comparator data be changed to
include adjusted incidence proportions (or rates) when needed for
adverse drug reactions (ADR) that are somewhat common.
 Product labels better reflect the risk of a drug relative to a
comparator
 Not needed if:
 The ratio of patients receiving the new drug to that receiving the
comparator is approximately the same across all the studies
included or
 Incidences of AEs in the comparator group are nearly the same
across the studies
 If crude pooling be sure to look at the individual study results to
check that pooled results are consistent with the individual studies
 Including comparator incidence in product labels gives health care
providers and patients appropriate information to put the absolute
risks in perspective
References
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
Crowe, B., Chuang-Stein, C., Lettis, S., & Brueckner, A. (2016).
Reporting Adverse Drug Reactions in Product Labels. Therapeutic
Innovation & Regulatory Science, 50(4), 455-463.
Chuang-Stein, C., and Beltangady, M. (2010). Reporting Cumulative
Proportion of Subjects With an Adverse Event Based on Data From
Multiple Studies. Pharmaceutical Statistics, 10, 3–7.
16Aug2016
CBI’s Pharmacovigilance Final Rule Summit on IND 2016
19