2015 PhUSE SDE, Copenhagen10. June 2015

Jean-Marc FerranConsultant & Owner

• Thanks to – Sarah Nolan (University of Liverpool)– Khaled El Emam (Privacy Analytics) for their input

DeID Standards Risk Data

UtilityRWD

Studies

MD5

PharmaEmployees CROs Researchers

(Portal)Researchers (Data is sent)

Public(Web)

Legal Framework

Technical Framework& Controls

Data De-

Identification

Data De-Identification

Processes

Quasi/Direct Identifiers

AssessmentRules

Residual Risk

PatientID

DoB Age Gender Race Country PartnerAge

1 12APR1963 51 Male White Canada 48

2 28MAY1974 40 Male Asian France 41

3 06MAY1961 53 Male White United States 36

4 28MAY1954 60 Female Black Spain 65

5 14JUL1969 45 Male Black Brazil 41

6 13AUG1964 50 Female White Argentina 45

7 18MAR1961 53 Male White United States 48

8 22JAN1961 53 Male White United States 37

9 27SEP1924 90 Male White Canada 73

10 07FEB1956 58 Male White Canada 62

?

PatientID

Age Category

Age Gender Race Country PartnerAge

1 <89 51 Male White Canada

2 <89 40 Male Asian France

3 <89 53 Male White United States

4 <89 60 Female Black Spain

5 <89 45 Male Black Brazil

6 <89 50 Female White Argentina

7 <89 53 Male White United States

8 <89 53 Male White United States

9 ≥89 . Male White Canada

10 <89 58 Male White Canada

?

??

PatientID

Age Category 2

Age Gender Race Continent PartnerAge

1 50-59 Male White North America

2 40-49 Male Asian Europe

3 50-59 Male White North America

4 60-69 Female Black Europe

5 40-49 Male Black South America

6 50-59 Female White South America

7 50-59 Male White North America

8 50-59 Male White North America

9 ≥89 Male White North America

10 50-59 Male White North America

?

??

?

?

PatientID

DoB Age Gender Race Country PartnerAge

1

2

3

4

5

6

7

8

9

10

?

?

?

??

??

?

??

PatientID

DoB Age Gender Race Country PartnerAge

1 12APR1963 51 Male White Canada 48

2 28MAY1974 40 Male Asian France 41

3 06MAY1961 53 Male White United States 36

4 28MAY1954 60 Female Black Spain 65

5 14JUL1969 45 Male Black Brazil 41

6 13AUG1964 50 Female White Argentina 45

7 18MAR1961 53 Male White United States 48

8 22JAN1961 53 Male White United States 37

9 27SEP1924 90 Male White Canada 73

10 07FEB1956 58 Male White Canada 62

?

Size 1: 100.0%

Patients having same characteristics for important quasi identifiers

PatientID

Age Category

Age Gender Race Country PartnerAge

1 <89 51 Male White Canada

2 <89 40 Male Asian France

3 <89 53 Male White United States

4 <89 60 Female Black Spain

5 <89 45 Male Black Brazil

6 <89 50 Female White Argentina

7 <89 53 Male White United States

8 <89 53 Male White United States

9 ≥89 . Male White Canada

10 <89 58 Male White Canada

?

??

Size 3: 33.3%

Patients having same characteristics for important quasi identifiers

PatientID

Age Category 2

Age Gender Race Continent PartnerAge

1 50-59 Male White North America

2 40-49 Male Asian Europe

3 50-59 Male White North America

4 60-69 Female Black Europe

5 40-49 Male Black South America

6 50-59 Female White South America

7 50-59 Male White North America

8 50-59 Male White North America

9 ≥89 Male White North America

10 50-59 Male White North America

?

??

?

?

Size 5: 20.0%

Patients having same characteristics for important quasi identifiers

AveragePatients

1Size(EquivalenceClass[Patient])!

"#

$

%&

Maxi

1Size(EquivalenceClass[i])!

"#

$

%&

Hrynaszkiewicz et al., BMJ 2010: Less than 3 quasi identifiers

Disease Population in Geographical LocationProb=1/XXXXX

All Similar Clinical TrialsProb=1/XXX

All Similar Sponsor Clinical TrialsProb=1/XX

Clinical TrialProb=1/X

ProactiveOutside a Request

Use Company/Industry Guidelines

Compare to SAP

Good common sense…

ReactiveBased on a Request

Use Company/Industry Guidelines

Focus on what is needed

Negotiate with researcher

Minimum Data Utility

Quasi/Direct Identifiers

Data Rules

Risk

5th Clinical Trial Data Transparency Forum Heidelberg, Germany, 23rd April

Clinical Study Data Request.com and the SAS Data Access System:

An Academic Researcher’s Experience

Sarah J. Nolan (sjn16@liv.ac.uk)Department of Biostatistics

University of Liverpool

Academic Context• October 2011: 3 year research project for

Cochrane Epilepsy Group funded by National Institute of Health Research (NIHR):

“Clinical and cost effectiveness of interventions for epilepsy in the NHS”

o Cochrane Individual Participant Data (IPD) Network Meta Analysis (10 drugs)o Carbamazepine (CBZ), Phenytoin (PHT), Valproate

(VPA), Phenobarbitone (PB), Oxcarbazepine (OXC), Lamotrigine (LTG), Gabapentin (GBP), Topiramate(TPM), Levetiracatam (LEV), Zonisamide (ZNS)

Network Meta Analysis: Data Requestingo 29 existing studies (n=5881)–12 Academic studies (n=1383)–13 Pharmaceutical studies (n=3320)–4 Government studies (n=1178)

oData provided from 18 studies (n=4697, 80%)–2 Academic studies (n=286, 21%)–13 Pharmaceutical studies (n=3320, 100%)–3 Government studies (n=1091, 93%)

Network Meta-Analysis

SAS Multi Sponsor Data Access Environment

Standard operating procedure for the project

1) Perform a detailed check of the data for content• Go back to data providers if anything is missing

2) Consistency check against the publication• Check inconsistencies with data providers

3) Prepare analysis variables for the network meta-analysis outcomes

Completed in approximately: 5 working days

• Real World Data is any data from external “Real World” sources – Insurance claims databases – Electronic medical/health records – Social media feeds – Web trawling of online

documentation – Biosensor device data – Mobile App data – Genomic/Proteomic/Xxxxxx-omic

data – Publicly available environmental

data – Marketing survey data

Source: PhUSE German SDE 2014 –Rob Walls, Roche

• Many De-Identification Standards Available

• Increasing awareness around Risk Assessment

• Data Utility is key and must be assessed properly including reproduction of results

• Increase of Pubic Data and Real World Data Studies will require data de-identification to evolve

Jean-Marc FerranConsultant & Owner, Qualiance ApS

dk.linkedin.com/in/jeanmarcferran/

@QualianceTwitta

• [1] Clinical Trial Transparency Regulatory Landscape - Ben Rotz – Eli Lilly and Company – Clinical Trial Data Transparency Forum – 11 February 2014

• [2] EMA Guidelines 0070 (Draft) – June 2013– http://www.ema.europa.eu/docs/en_GB/document_library/Other/2013/06/WC500144730.p

df• [3] Hrynaszkiewicz I, Norton M L, et al. Preparing raw clinical data for publication:

guidance for journal editors, authors, and peer reviewers. British Medical Journal 2010; 340:304–307– http://www.bmj.com/content/340/bmj.c181

• [4] The Twelve Characteristics of an Anonymization Methodology, Khaled El Emam– http://www.privacyanalytics.ca/wp-content/uploads/2013/07/TwelveCharacteristics.pdf

• [5] A De-identification Strategy Used for Sharing One Data Provider’s Oncology Trials Data through the Project Data Sphere Repository, Malin, 2013 – https://www.google.com/url?q=https://www.projectdatasphere.org/projectdatasphere/html/

resources/PDF/DEIDENTIFICATION&sa=U&ei=D69iU47DBYaN4ASf4YGgBw&ved=0CBsQFjAA&usg=AFQjCNGaWTa9-cXwUpP9q6UfE9FBjPV4vw

• [6] Preparing individual patient data from clinical trials for sharing: the GlaxoSmithKline approach – Pharmaceutical Statistics 2014