The Simulacrum, a Synthetic Cancer Dataset

Supporting precise data
analysis without releasing
patient records: the Simulacrum
in action
Cong Chen, Paul Clarke, Lora Frayling, Sally Vernon, Brian Shand, Pesh
Doubleday, Jem Rashbass

Overview
• Context and goals of this talk
• Background: our motivating problem
• What is synthetic data and how does it help?
• What is the goal of the data exercise?
• Building a synthetic data model in the Simulacrum
• Results and applications
• Conclusion
Presentation title - edit in Header and Footer

TalkAims
• Introduce and motivate
concepts
• Synthetic data
• The information governance
environment
• Externally guided analysis
• Describe and explain
• The Simulacrum as synthetic data –
what is it and how was it created?
• Synthetic data-guided queries
• How this has led to faster, more
private answers

Problems with sharing cancer data
• Lots of data is available
• This would enable researchers and industry to provide
valuable insight into disease epidemiology, survival,
clinical practice, resource utilisation, outcomes
• Highly sensitive
• Sharing data is an exercise in risk-reward balancing
• Complex and intricate
• Data dictionaries do not provide a perfect view of what
to expect, analysis can be slow to converge

Synthetic data
• Data items which are not created by
observations
• This includes simulations (e.g.
Synthea), partially synthetic data
(generalised perturbation) and fully
synthetic data
• Does not represent individuals
• Removes re-identification risk, but
attribution risks remain

Simulacrum project aims
Users should have direct access to a public resource
• Showing data as it looks to internal analysts
• Be able to identify their cohort and the cohort size, data completeness and
quality, and the codes/ranges used
• Be able to prepare and code algorithms against the synthetic data
With a prepared analytical plan
• Engage PHE with the proposed study
• Share code which runs on the real data
• Be able to complete analysis without releasing row-level or other sensitive
data
Take a data-driven approach where possible
• Use parameters
• To adjust for differently sized or shaped datasets
• To adjust to different privacy constraints/requirements

Linked datasets
• Data represents the course of patient treatment – we are
interested in a coherent story and sensible timeline.
• Patients can have multiple tumours, with very many
treatment events – we need to capture this.

How did we do it?
• Key idea: sample from empirical conditional distributions.
• Question: how do we keep from running out of data?
• Use low-dimensional distributions.
• Question: which variables do we condition on?
• Use independence tests to find strongly associated
variables.

More details
• Question: what do we do for linked tables?
• Use all previous data (but in read-only mode).
• Question: what about sequences of events?
• Use information from the previous event (if it exists)
and data in upstream tables – so a Markov model.
• Question: what about sampling from small conditional
distributions, which risk reflecting real individuals?
• Cluster these distributions to meet accepted healthcare
data standards.

What models look like (without the data)

The Simulacrum as a dataset
• Version 1 – released 2018. 1.5 million tumours
(corresponding to English incidences 2013-2015) with
tumour/demographic/mortality data and chemotherapy
treatment.
• Representative at low dimensions (of variable
combinations), not as good for complex detail.
• Non-disclosive for public release.
• Ongoing development.

How does it look?
Cumulative age distribution (breast)
Blue: Synthetic, Red: Real
0
0.25
0.5
0.75
1
0 50 100
Cumulative age distribution
(prostate)
0
0.25
0.5
0.75
1
0 50 100

Applications
• Synthetic data used to back up a statistical query
gateway (currently manual).
• We’ve shared our synthetic data with partners to write
queries against – those have turned out to be robust and
aware of data formats, categories in our data and run
against our data.
• Publications accepted for conferences and journal
articles.
• We then try to release non-disclosive aggregates, model
parameters/diagnostics without the personal data used to
build those models.

Current work
• Better documentation of research and access process for
less technical researchers
• Model improvement, application in context of other
datasets
• More test-driven quality measures, automatic simulation
with specific goals
• Use other synthetic methodology within the data
architecture
• Fidelity isn’t objective – need to think about suitability for
specific purpose

Conclusions
• Synthetic data is a game changer for supporting
research and reducing risks
• This opens understanding of the data and analysis to a
wider audience while reducing workload and
misunderstandings
• Realistic understanding of aims and expectations helps
a synthetic data project improve mutual understanding

Acknowledgements
• Analyses were based on anonymous aggregate patient
data from the National Cancer Registration and Analysis
Service.
• Thank you to NCRAS and HDI, as well as everyone
working on or who has worked on the Simulacrum.
• Pick up the data at https://simulacrum.healthdatainsight.org.uk
• https://github.com/UCL-simulacrum/EDA is an amazing piece of work carried
out by UCL students over 3 months with no reference to the real data.
• cong.chen@phe.gov.uk
• ncrasenquiries@phe.gov.uk

The Simulacrum, a Synthetic Cancer Dataset