Advancements in the methodologies for evidence synthesis for evidence based medicine.

1. Enabling Evidence Based Medicine
Athula Herath, PhD
November 16, 2017

2. Summary
• Some thoughts on evidence synthesis
• The main theme running through the presentations is
developing a framework that captures the “base rates”
and evolve (with progress).
• The objective is to facilitate a discussion and a dialogue
2

3. http://simplystatistics.org/2016/08/24/replication-crisis/
Where are we?
3

4. 4
Treating Individuals
4

5. There are some clouds developing
in the sky ….
5

6. 6

7. Could some heuristics from Nobel
Prize Winners help ?
7

8. Overcoming the base rate fallacy
(base rate neglect)
8

9. How do we assemble the base
rate ?
9

10. The big data of health
(If only we can reach them) ?
10

11. If only we can reach them – may be
(Key RWE Big Data Outcomes Projects)
• EMIF (EU/IMI) – European Medical Information Framework
• EHR4CR (EU/IMI) –Electronic Health Records for Clinical Research
• OHDSI – Observational Health Data Sciences and Informatics,
"Odyssey" is an open-source, global, interdisciplinary collaborative
to bring out the value of health data through large-scale analytics.
• GetReal (EU/IMI) -- GetReal aims to show how robust new methods
of RWE collection and synthesis could be adopted earlier in
pharmaceutical R&D and the healthcare decision making process.
• BD4BO (EU/IMI) – Big Data for Better Outcomes
• Do IT (EU/IMI, Integration for Disease Projects, Novartis Leads)
• EHN (EU/IMI, Proposed, European Health Network)
• Disease Projects
• Harmony (Oncology -- hematologic cancers, Novartis Leads)
• Roadmap (Alzheimer's / Novartis Leads)
• Big Heart
• Prostate Cancer
Business Use Only 11

12. 12
... rely on a kind of global Internet-of-DNA which
doctors will be able to search. ... “Our bird’s eye
view is that if I were to get lung cancer in the
future, doctors are going to sequence my genome
and my tumour's genome, and then query them
against a database of 50 million other
genomes,” ... “The result will be ‘Hey, here’s the
drug that will work best for you.’
http://www.technologyreview.com/news/532266/google-wants-to-store-your-

13. Inspirations!
13

14. Inspired by ....
Treatable Traits
14
The classic Oslerian classifcation of human diseases has proven very successful in a
large proportion of patients .... however, diseases are complex and often overlap.
“Oslerian diagnostic labels” without further investigating the underlying endotype(s)
has resulted in most individuals with airway disease being excluded from the major
randomised clinical
trials which, eventually, are incorporated into documents that guide their treatment.
Moreover,
the clinically important benefcial efects of biological agents targeting eosinophilic
airway infammation were very nearly missed for similar reasons.
Treatable traits: toward precision medicine of chronic airway diseases.
Agusti A et. al, Eur Respir J. 2016 Feb;47(2):410-9. doi: 10.1183/13993003.01359-

15. Inspired by ......
IHME Global Burden of Disease (GBD)
• Systematically gathers/estimates the various health metrics
(37) and disease epidemiology of well characterized
diseases/sequelae1 (>16001) in 2182 countries in the world
and made available via a comprehensive database and data
visualization platform.
https://vizhub.healthdata.org/gbd-compare/
• Contributed by the healthcare systems in some countries (e.g.: UK)
• Conducting surveys by the Global Burden of Disease (GBD) team,
which is well networked and well established.
• Perhaps, GBD has the capacity to gather health metrics data from
countries where there is no “organized government” ;).
• 26 year historical data (approximately 5 year intervals,
recently, yearly updated)
• Well established, published, validated and globally accepted
tools to combine various forms of data.
15
1 Sequelae -- a condition which is the consequence of a previous disease or injury.
2 854 Causes, 20+ Etiologies, 31+ Impairments
3 Some countries, extensive regional and local levels, e.g: In UK , 4 nations of the UK: England, Northern Ireland, Scotland, and Wales , 9 regions of England ,
150 UTLAs of England

16. https://vizhub.healthdata.org/gbd-
compare/
16

17. What might we need ?
(my shopping list ;) )
• An up-to-date and accredited (i.e. globally accepted by health regulators,
healthcare systems and medical community) health metrics database
• A unifed approach/tools to estimate the disease burden globally, scientifcally and
according to the disease epidemiology (incidence, prevalence and other
epidemiological outcomes), clinical efcacy, safety outcomes
• A coordinate system to overlay the systematic, disparate and eclectic evidence
(including the real world evidence, even more eclectic and ad-hoc molecular data)
gathered by
• Systematically Reviewing the state of the art in literature
• Patient level Real World Data (RWD) – this include all longitudinal cohorts, observational
studies, biomarker studies, GWAS studies, whole GENOMES of people, Biobanks, disease
registries and EMR
• Clinical trials (in house pharmaceutical companies and also increasingly made publicly
available)
• Globally accepted, validated set of tools to incorporate the measures above, to
empirically estimate the un-met need globally in a validated way and also
illustrate the value proposition of the treatment regimens that we bring forward in
high precision globally.
• Last but not least, the resources (including fnancial and technical) to fll all the
gaps above.
Business Use Only 17

18. My shopping list – Integrated
Evidence Generation Frameworks
18

19. Establishing a framework -- An Example – Atopic Dermatitis
19

20. What’s available and what is being developed as possible therapies and compare them – clinical efcacy and the
efectivenesse
20

21. 21
Summary

Phenotype and derive treatable traits of patients using available
Big/Small Data of Health
 The therapeutic landscape established.

Map any available molecular expression data to the derived
landscape (e.g.: gene expression, protein expression, any
other mappable molecular expression data)
 Endotypes are derived – Molecular understanding of the mechanisms
involved in the strata of interest (poor outcomes)

Map the current therapies (the entities that are in
development by us and our competitors via the molecular
fngerprints
 The potential of the existing therapies and that are in development (the
competition and/or opportunity)

Map the published summary data/meta-analysis output of
the current therapies into the landscape
 What therapeutic efects are brought about (or potential) by these
therapies in which segments of the populations

Obtain high precision benefttrisk estimates for the
appropriate (local) population
21

22. 22
Thank YOU!
22
Did I mention the word “Bayesian”? 