1. Improving Adverse Drug Reaction Information in Product Labels
PSI Conference
May 2017
Sally Lettis GlaxoSmithKline

2. 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.

4. Outline Current Labelling Practice Current Labelling Limitations
The problem to solve
The proposed solution
Presentation title

5. 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

6. 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

7. Current Labelling Practice: EU SPc
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

8. 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 -
6 months
1.5%
Common
1.7%
Severe
12 months
6.3%
3.3%
Pooled
3.0%
2.6% Y
0.5%
X+Y
1.0%
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

9. Current Labelling Limitations: The issue with crude pooling
Data presented is based on crude pooling of data across multiple studies: pooling data as if from a single study
Can lead to “Simpson’s Paradox”: When studies combined, trend seen in the individual studies either disappears or is reversed
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
Differences that could affect the incidence include age, sex, race, medical practice, differing time on study
Common for reporting proportions with an ADR in labels
Not a new issue (Chuang-Stein and Beltangady (2010))
E.g. Cochran-Mantel-Haenszel to produce a common odds ratio across strata
If used, applied in Integrated Summaries of Safety
Too complicated for Product Labels which revert to crude incidences

10. Total patients in study
Current Labelling Limitations: Example showing misleading incidence proportion from crude pooling
New treatment,
n/N (%)
Placebo,
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%)
1000
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
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

11. Current Labelling Limitations: Why did crude pooling go wrong?
New treatment,
n/N (%)
Placebo,
Total patients in study
Allocation
Ratio
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%)
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

12. The problem to solve: So what do we do?
We can see what the issue is
How should we present adjusted proportions?
Conclusions from crude pooling misleading
Meta-analytic techniques:
e.g. Cochran-Mantel-Haenszel weights
Not easy for non-statistician to understand
16Aug2016
CBI’s Pharmacovigilance Final Rule Summit on IND 2016

13. Total patients in study Proportion of total patients in study
The Proposed Solution: A better way to go Study-size Adjusted Percentages
New treatment,
n/N (%)
Placebo,
Total patients in study
Proportion of total patients in 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%)
1000
w3 = 1000/2450
(41%)
Incidence proportion from crude pooling
363/1500 (24%)
277/950 (29%)
2450

14. Total patients in study Proportion of total patients in study
The Proposed Solution: A better way to go Study-size Adjusted Percentages
New treatment,
n/N (%)
Placebo,
Total patients in study
Proportion of total patients in 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%)
1000
w3 = 1000/2450
(41%)
Incidence proportion from crude pooling
363/1500 (24%)
277/950 (29%)
2450
Study-size-adjusted incidence proportions
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%

15. Comparison of Weights for New Treatment
6/18/2018
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/ %
w1 = 400/ %
w1 = a1/aj
13%
Study 2
133/700 (19%)
1050
w2 = 700/ %
w2 = 1050/ %
w2 = a2/aj
42%
Study 3
200/500 (40%)
1000
w3 = 500/ %
w3 = 1000/ %
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%
The population total in crude pooling is based on the total number of patients on drug. The weights to calculate the incidence for crude pooling are based on the number of patients on drug a study contributes out of the total number of patients on drug.
N_study/N_total_treatment
Study Adjusted Size and CMH weights same for each treatment; Crude pooling weights are not

16. Comparison of Weights for Placebo
6/18/2018
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/ %
w1 = a1/aj
13%
Study 2
67/350 (19%)
1050
w2 = 350/950 37%
w2 = 1050/ %
w2 = a2/aj
42%
Study 3
200/500 (40%)
1000
w3 = 500/950 53%
w3 = 1000/ %
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%
The population total in crude pooling is based on the total number of patients on drug. The weights to calculate the incidence for crude pooling are based on the number of patients on drug a study contributes out of the total number of patients on drug.
N_study/N_total_treatment
Study Adjusted Size and CMH weights same for each treatment; Crude pooling weights are not

17. 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

18. 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
There are a couple of occasions when continuing with the
current practice in data pooling will not distort the comparison
of ADR incidences between a new drug and a comparator.
They occur when the ratio of patients receiving the new drug
to that receiving the comparator is approximately the same
across all the studies included in the integrated summary, or
when the incidences of AEs in the comparator group are nearly
the same across the same set of studies. On such occasions,
naıve pooling will produce similar results to the proposed
adjustment method.

19. CBI’s Pharmacovigilance Final Rule Summit on IND 2016
References
Crowe, B., Chuang-Stein, C., Lettis, S., & Brueckner, A. (2016). Reporting Adverse Drug Reactions in Product Labels. Therapeutic Innovation & Regulatory Science, 50(4),
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