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Analysis of clinical reports published in the context of Policy 0070

Technical Anonymisation Group (TAG) meeting, London, 29-30 November 2017 Agenda point 06

Presented by Ada Adriano Documents Access and Publication Service

Acknowledgments

Kanako Sasaki (Visiting Expert from Japanese MHLW) and EMA Clinical Data Publication team

Analysis of clinical reports published in the context of Policy 0070 1

Analysis of published anonym isation reports

• 54 anonymisation reports published (cut-off date: 06 October 2017)

Analysis of clinical reports published in the context of Policy 0070 2

20% 80%

Product category

without patient identifiers with patient identifiers (11) (43) 64% 18% 18%

Product category

(without patient identifiers)

generics hybrids non- generics (2) (2) (7) *(literature data) * 12% 2% 65% 21%

Product category

(with patient identifiers)

generics biosimilars full applications

• rphans

(1)

(28)

(9) (5) Non-orphans/ non-generics

Orphans

• Mainly small/ very small study size (e.g. n= 2, n= 3 subjects);
• Size of study population mostly accounted for in the anonymisation

process (8/ 9);

• Attacks envisaged linked to the type of product (e.g. gene therapy).

Analysis of clinical reports published in the context of Policy 0070 3

Orphans

Methodology applied

Analysis of clinical reports published in the context of Policy 0070 4

67% 22% 11%

Anonymisation assessment

risk assessment (qualitative) risk assessment (quantitative) fulfillment 3 criteria (6) (2) (1)

N(orphans)= 9

89% 11%

Anonymisation technique

redaction transformation (8) (1)

Orphans

Analysis of clinical reports published in the context of Policy 0070 5

Anonym isation applied

• Redaction of medical history and demographic characteristics throughout CSRs (8/ 9);

Anonym isation of:

• Dem ographic characteristics;
• Medical history;
• Verbatim text;

34% 33% 11% 22%

Redaction of adverse events

(3) (3) (1) (2) 78% 22%

Full redaction of case narratives

yes no, selected identifiers only (7) (2)

Orphans

Alprolix:

• Redaction of quasi-identifiers to remove unique combinations of quasi-

identifiers;

• Full redaction of narratives performed;
• For subgroups ≤11*, median, minimum and maximum values redacted.

Darzalex:

• Same approach used for non-orphan/ non-generic product (i.e. Afinitor);
• Case narratives NOT fully redacted!

Analysis of clinical reports published in the context of Policy 0070 6

* N patients= 1/ 0.09

Exam ples of quantitative approaches

Non-orphans/ non-generics

• Usually large studies (i.e. > 100 subjects);
• Few studies with < 100 subjects (e.g. Phase I studies);

Analysis of clinical reports published in the context of Policy 0070 7

90% 7% 3%

Anonymisation assessment

risk assessment (qualitative) risk assessment (quantitative) fulfillment 3 criteria (26) (2) (1) 97% 3%

Anonymisation technique

redaction redaction and transformation (28) (1)

N(non-orphans/ non-generics)= 29

Qualitative approach (non-orphans/ non-generics)

• Qualitative risk threshold to be set (e.g. low, very low);
• No calculation of re-identification risk;
• Risk assessment based on subjective evaluation;
• Analytical approach?
• Redaction as preferred technique;
• Study categorisation driven by sample size (12/ 26): what is small/ big?
• Heterogeneity in the anonymisation performed.

Analysis of clinical reports published in the context of Policy 0070 8

Qualitative approach (non-orphans/ non-generics)

Anonym isation applied

Analysis of clinical reports published in the context of Policy 0070 9

Redaction

Dem ographic characteristics ( 2 6 / 2 6 ) Medical history ( 2 0 / 2 6 ) I n-text narratives ( 1 4 / 2 6 )

50% 4% 15% 31%

Full Redaction of case narratives

(13) (1) (4) (8) 31% 11% 12% 19% 15% 12%

Redaction of adverse events

(8) (3) (3) (5) (4) (3)

Qualitative approach (non-orphans/ non-generics)

Analysis of clinical reports published in the context of Policy 0070 10

Num bers of quasi-identifiers per trial participant Size of study population Uniqueness

• f variable

values

Uniqueness of variable values ( 1 1 / 2 6 ) :

• Criterion for identifiers

selection;

• Redaction of specific

variable values;

• Non-uniqueness

considered. Size of study population ( 1 8 / 2 6 ) :

• Study categorisation based on

study characteristics;

• Lack of harmonisation in the

identifiers/ sections redacted. Num bers of quasi identifiers per trial participant ( 1 8 / 2 6 ) :

• Combination of variables

considered.

Quantitative approach (non-orphans/ non-generics)

• Quantitative risk threshold to be set (0.09);
• Calculation of re-identification risk;
• Transformation as additional technique (e.g. pseudo-anonymisation,
• ffset dates, randomisation, generalisation of medical history to

MedDRA HLT, HLGT and SOC);

• Less conservative assumptions (data set considered, attacker

knowledge);

• Different methodologies applied.

Analysis of clinical reports published in the context of Policy 0070 11

Quantitative approach (non-orphans/ non-generics)

Analysis of clinical reports published in the context of Policy 0070 12

Zinbryta:

• Full com bined population of all studies

used in the analysis;

• Subjects grouped into equivalence classes

(minimum equivalent class size= 12);

• Verbatim terms and sensitive data not

included in the risk assessment;

• Redaction as anonymisation technique.
• No full redaction of case narratives (subject

ID, dates, age);

• Adverse events redacted when in com bination

and/ or unique;

• Redaction selected frequencies in table

summarizing adverse events by body weight.

Afinitor:

• Population in sim ilar trials used in the analysis;
• Quasi-identifiers that are caught and those missed

accounted for in the risk calculation;

• Local recoding: different transformation based on

the level of risk;

• Transform ation as anonymisation technique

(dates, age, medical history).

• Suppression applied to some identifiers (e.g.

race);

• Subject IDs pseudo-anonymised;
• Full redaction of case narratives prior to risk

assessment;

• Serious adverse events redacted in narratives.

Data utility

• Not integrated in the risk assessment;
• Linked to aggregated data only;
• Expectations of end users not clearly addressed;
• Impact of full redaction of narratives not always addressed.

Analysis of clinical reports published in the context of Policy 0070 13

Conclusions

• Disease and/ or study population driving the anonymisation

process;

• Limited experience (public release, potential adversaries,

unstructured text);

• Limited confidence with the assumptions (threshold, data set,

type of attacks).

Analysis of clinical reports published in the context of Policy 0070 14

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