New methods for reviewing mechanistic evidence
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New methods for reviewing mechanistic evidence Systematic review guidelines for integrating evidence from human, animal and other mechanistic studies which link diet, nutrition and physical activity to cancer Richard Martin School of Social and Community Medicine, University of Bristol
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Transcript of New methods for reviewing mechanistic evidence
New methods for reviewing mechanistic evidence Systematic review guidelines for integrating evidence from human, animal and other mechanistic studies which link diet, nutrition and physical activity to cancer
Richard Martin
School of Social and Community Medicine, University of Bristol
Why systematically synthesize mechanistic data?
Having confidence in the evidence
Extensive mechanistic data link diet with cancer
– Biological plausibility; informs interventions
Of 52 observational claims tested in trials published in JAMA,
JNCI & NEJM none were confirmed & 10% contradictory
< 10% highly promising basic science discoveries enter
clinical use
Evidence distorted (falsely inflated) by
– Irrelevance (population, exposure, outcomes)
– Weak internal validity (randomisation, allocation concealment,
blinding, drop outs, co-morbidities)
– Publication bias (98% of animal studies ‘significant’)
Selective reporting bias in cancer prognostic studies
Meta-analysis of association of TP53 status with risk of death at 2 years
Kyzas P A et al. JNCI J Natl Cancer Inst 2005;97:1043-1055
Our aimsTo develop a detailed protocol for comprehensive systematic reviews of mechanistic studies
Systematic reviews
– Allow objective appraisal of evidence
– Reduce false-positive & false-negative results
– Identify sources of bias, improving study quality
This project should increase the value of mechanistic data:
– Enable more rigorous systematic reviews
– Increased precision of estimated effects
– Identify gaps in the research evidence
– Reduce selective citation of mechanistic evidence
– Inform relevance to humans (cross species/model heterogeneity)
Tool for translating basic science into policy & practice
Important for the Continuous Update Project
Overall approachGuideline development
Four workshops with experts within our group
– mixture of presentations with discussion, small group exercises,
round table discussions
Regular meetings in between workshops
– Refine the protocol
– Carry-out searches
– Determine inclusion/exclusion criteria
– Investigate QC criteria
– Consider relevance, publication bias, reporting/display of results
Expertise in:
- Systematic reviews of epidemiological studies
- Experimental studies of cancer
- Bioinformatics
- Information technology
The TeamUniversity of Bristol:
PI - Dr Sarah Lewis - Genetic epidemiology/systematic reviews of genetic studies
Co-PI - Prof Richard Martin - Cancer epidemiology/systematic reviews
Dr Mona Jeffreys - Cancer epidemiology/systematic reviews
Dr Mike Gardner - Animal biology/systematic reviews
Prof Jeff Holly - Molecular biology – IGF and cancer
Dr Claire Perks - Molecular biology
Dr Tom Gaunt - Genetic epidemiology/bioinformatics
Prof Jonathan Sterne - Meta-analysis and systematic review methodology
Professor Julian Higgins - Meta-analysis and systematic review methodology
Prof Steve Thomas - Head and neck surgeon
Dr Pauline Emmett - Nutritional epidemiology
Dr Kate Northstone - Nutritional Epidemiology
Cath Borwick – Information specialist
University of Cambridge: World Cancer Research Fund International
Dr Suzanne Turner - Animal models Prof Martin Wiseman
Dr Pierre Hainut (advisor to WCRF Int)
Dr Panagiota Mitrou, Dr Rachel Thompson
IARC:
Dr Sabina Rinaldi - Hormones and cancer
Summary of the process
Research
question
(PEMO))
• Identify
• Appraise individual
studies
• Integrate body of
evidence
• Risk of bias
• Relevance
• Mechanism discovery
(unbiased)
• Specific mechanisms (targeted)
• Within an evidence stream (human, animal, in vitro)
• Across evidence streams
• Confidence
conclusion
High
Moderate
Low
Eligibility
criteria
Identifying the evidence
Two step process for searching for mechanisms
Broad automated search to encompass all mechanisms
– Mechanism discovery
– Quantitative assessment of mechanism evidence
– Assists prioritization of mechanisms for review
– “Hypothesis-free” (to some extent)
– Identifies efficient starting points for review
– Identifies potential mechanisms unknown to the reviewer
Targeted search – focus on a particular pathway
– Apply pre-specified inclusion/exclusion
– Has the cancer arisen in the animal model rather than being
transplanted into the animal?
– Have cell lines been authenticated and results replicated?
Primary search
Mechanism discovery
Specific searches
EXPOSURES INTERMEDIATE
MECHANISMSOUTCOMES
Automated mechanism quantification and display
Evidence synthesis
Three step process
Appraisal of the individual studies
– Risk of bias
– Present summary data, stratified for presence of
heterogeneity
Integrate within evidence streams (human / animal)
– Risk of bias Magnitude of effect
– Inconsistency Confounding controlled for
– Imprecision Dose response
– Publication bias
– Irrelevance
Integrate across evidence streams
–
Causal conclusions
Integrating evidence
Leve
l of
evid
ence
in
hu
man
stu
die
s
HighStrong
ModerateWeak Modest
LowInconclusive Weak Modest
Low Moderate High
Level of evidence in animal studies
Supportive evidence from in vitro and xenograft models
underpinning biological plausibility
Milk and prostate cancer exemplar
Milk implicated in prostate cancer, but
– is measured semi-quantitatively
– is susceptible to confounding
– large differences between individuals in the same group
lead to attenuation by measurement errors
Systematic reviews of human observational studies are
inconclusive
– Limited-suggestive evidence (World Cancer Research
Fund International, 2014)
Experimental studies not systematically reviewed
Results of automated mechanism quantification (36,000 hits)
Targeted search results
4946 targeted studies identified
725 studies retrieved for detailed
evaluation
4221 studies excluded after
review of title and abstract
(duplicates or clearly
ineligible)
Databases searched:
• Medline
• Embase
• BIOSIS
• CINAHL
268 studies potentially eligible
325 studies excluded after
review of full text
132 studies awaiting ILL
23 milk-IGF studies 245 IGF-prostate cancer
22 human studies extracted (5
RCT, 3 cohort, 14 cross-
sectional)
50 human studies
extracted (total: 99)7 animal studies
extracted (total: 8)
138 Cell line studies
Evidence synthesis
NOTE: Weights are from random effects analysis
Overall (I-squared = 69.0%, p = 0.012)
Hoppe 2004
Zhu 2005
Study
Cadogan 1997
Rich-Edwards 2007
Ben-Shlomo 2005
.02
2
Follow-up
(yrs)
1.5
.02
25
100
0
Male
Control
0
0
%
52.8
20.87 (-8.75, 50.49)
40.20 (-7.43, 87.83)
28.60 (-17.66, 74.86)
ES (95% CI)
69.00 (5.89, 132.11)
13.30 (-28.64, 55.24)
-9.50 (-16.75, -2.25)
100.00
17.59
18.06
%
Weight
13.09
19.59
31.66
20.87 (-8.75, 50.49)
40.20 (-7.43, 87.83)
28.60 (-17.66, 74.86)
ES (95% CI)
69.00 (5.89, 132.11)
13.30 (-28.64, 55.24)
-9.50 (-16.75, -2.25)
100.00
17.59
18.06
%
Weight
13.09
19.59
31.66
Favours control Favours intervention
0-132 0 132
Difference in IGF-I (ng/ml)
Milk and IGF in human RCTs by increasing length of follow-up
IGF and hallmarks of cancer
(authenticated cell lines)
Future work
Automated mechanisms discovery
– Validation (not missing important studies)
– Inter-relationships between mechanisms
– Develop stand-alone automation software and beta
testing
Validation of relevance questions
Acceptability
Reliability
For further information
@wcrfint
facebook.com/wcrfint
www.wcrf.org
Sarah Lewis: [email protected]
Richard Martin: [email protected]
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