ioannidis conferencia inaugural
TRANSCRIPT
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CONFERENCIA INAUGURAL
“Credibilidad y geometría de la evidencia:
¿se basan las recomendaciones y decisiones
en estudios clínicos apropiados?”
John Ioannidis
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Credibility and geometry of the evidence: Are
recommendations and decisions based on
appropriate clinical studies?
Madrid 10/2012
John P.A. Ioannidis, MD, DSc
C.F. Rehnborg Chair in Disease Prevention
Professor of Medicine and Professor of Health Research and Policy
Director, Stanford Prevention Research Center
Stanford University School of Medicine
Professor of Statistics (by courtesy)
Stanford University School of Humanities and Sciences
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Recommendations and decisions
may depend on:
Experts (less reliable) Evidence (more reliable)
• However, experts may still
design, collect, analyze,
interpret, synthesize, apply,
or even enforce evidence
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Uneven research in the health
sciences
• Spongiform encephalopathies: 2050
MEDLINE publications per 1000 patients
• Myasthenia gravis: 156 MEDLINE
publications per 1000 patients
• Cerebrovascular disease: 7.7 MEDLINE
publications per 1000 patients
• Severe varicose veins: 0.5 MEDLINE
publications per 1000 patients
Frankel and West 1993
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Clinical
evidence
and burden
of disease:
do we
perform
research on
important
problems?
Swingler et al. BMJ 2003
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Clinical research in/for Africa
Isaakidis et al. BMJ 2002
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The new basic science for medicine:
evidence-based medicine
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The advent of meta-analysis and
RCTs
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The crisis of false positive research
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Diet causes cancer
• Open a popular cookbook
• Randomly check 50 ingredients
• How many of those are associated with
significantly increased or significantly
decreased cancer risk in the scientific
literature?
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Associated with cancer risk
• veal, salt, pepper spice, flour, egg, bread,
pork, butter, tomato, lemon, duck, onion,
celery, carrot, parsley, mace, sherry, olive,
mushroom, tripe, milk, cheese, coffee,
bacon, sugar, lobster, potato, beef, lamb,
mustard, nuts, wine, peas, corn, cinnamon,
cayenne, orange, tea, rum, raisin
Schoenfeld and Ioannidis, Am J Clin Nutrition, in press
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Why research findings may not be
credible?
• There is bias
• There is random error (see multiple
comparisons)
• Usually there is plenty of both
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Bias
• Any deviation from the truth beyond chance error
• Conscious, subconscious, or unconscious
• One may create theory (or theories) about bias or may study its consequences
• The former seem more robust, but it is the latter that we measure, witness, and eventually suffer
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Chavalarias and Ioannidis, JCE 2010
Mapping 235 biases in 17 million Pub Med papers
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A time array for biases
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Discrepancies over time occur even in
randomized trials Myocardial infarction interventions
Cumulative sample size
40000
30000
20000
10000
5000
4000
3000
2000
1000
500
400
300
200
100
Rela
tive c
hange in
tre
atm
ent eff
ect
3
2
1
.9
.8
.7
.6
Ioannidis and Lau, PNAS 2001
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Inflation in statistically significant
treatment effects of meta-analyses of
randomized trials?
Ioannidis, Epidemiology 1998
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Pereira, Horwitz, Ioannidis, JAMA 2012
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Post-study odds of a true finding are small
• When effect sizes are small
• When studies are small
• When fields are “hot” (many furtively
competitively teams work on them)
• When there is strong interest in the results
• When databases are large
• When analyses are more flexible
Ioannidis JP. PLoS Medicine 2005
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The slow and uncertain pace of clinical translation
Contopoulos-Ioannidis et al. Science 2008
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Disclosures • In my dreams I am the CEO of MMM (Make
More Money, Inc.)
• My company has successfully developed a new drug that is probably a big loser, but I want to make big money
• At best, the new drug may be modestly effective for one or two diseases/indications for one among many outcomes (most of them irrelevant to patients)
• If I test my drug in a study, even for this one or two indications, it may seem not to be worth it
• But still, I want to make big money
• What should I do?
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The answer • Run many studies with many outcomes on each of many different
indications
• Ideally run trials against placebo (this is the gold standard for regulatory agencies) or straw man comparators, but registry studies or even electronic records would do, if need be
• Test 10 indications and 10 outcomes for each, just by chance you get 5 indications with statistically significant beneficial results
• A bit of selective outcome and analysis will help present “positive” results for 7-8, maybe even for all 10 indications
• There are systematic reviewers out there who will perform a systematic review based on the published data SEPARATELY for each indication proving the drug works for all 10 indications
• With $ 1 billion market share per approved indication, we can make $ 10 billion a year out of an (almost) totally useless drug
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We probably all agree
• It is stupid to depend on the evidence of a
single study
• when there are many studies and a meta-
analysis thereof on the same treatment
comparison and same indication
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Similarly
• It is stupid to depend on a single meta-analysis
• when there are many outcomes
• when there are many indications the same
treatment comparison has been applied to
• when there are many other treatments and
comparisons that have been considered for each of
these indications
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Network definition
• Diverse pieces of data that pertain to research
questions that belong to a wider agenda
• Information on one research question may
indirectly affect also evidence on and inferences
from other research questions
• In the typical application, data come from trials on
different comparisons of different interventions,
where many interventions are available to
compare
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Size of each node proportional to the
amount of information (sample size)
A c LD
M c SD
M s SD
N c
N s
N+bmab
N+lpnb
NT
O c
O s
T c
A c SD T s
T+tzmb
Ts+lpnb
A s LD
A s SD
A+tzmb SD
AN SD
ANT SD
AT SD
M c LD
Figure 2a
A network offers a wider picture than a single
traditional meta-analysis: e.g. making sense of 700
trials of advanced breast cancer treatment
Mauri et al, JNCI 2008
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Size of each node reflecting the year of
first publication
A c LD
M c SD
M s SD
N c
N s
N+bmab
N+lpnb
NT
O c
O s
T c
A c SD
T s
T+tzmb
Ts+lpnb
A s LD
A s SD
A+tzmb SD
AN SD
ANT SD
AT SD
M c LD
Figure 2b
Focusing on what is most recent in the market
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Main types of network geometry
Salanti, Higgins, Ades, Ioannidis, Stat Methods Med Res 2008
Polygons
Stars
Lines
Complex figures
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Homophily
• OΜOΦΙΛΙΑ = Greek for “love of the same” =
birds of a feather flock together
• Testing for homophily examines whether
agents in the same class are disproportionately
more likely to be compared against each other
than with agents of other classes.
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For example: Antifungal agents
agenda
• Old classes: polyenes, old azoles
• New classes: echinocandins, newer azoles
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Rizos et al, J Clin Epidemiol, 2010
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2
18 11
1
1 3
1
2
1
1
3
4
2
17
amphotericin B
ketoconazole
lipid amphotericin B
posaconazole
voriconazole
fluconazole
itraconazole
Figure 2
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3
2
1
8
micafungin
other
anidulafungin
caspofungin
Figure 3
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Figure 4
10
12
1
other
voriconazole or posaconazole
echinocandins
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Auto-looping Design of clinical research: an open world or isolated city-states (company-states)?
Lathyris et al., Eur J Clin Invest, 2010
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Don’t blame the Big Pharma
necessarily
• Treatments for basal cell cancer: surgical,
destructive, topical
• Specialties do not seem to communicate.
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CA
PDT
Cryo
SG
IMI
L
5-FU IFN
MMS
SE
Rad
C&D
7 2
1
1
1
5 1
8
2
3
Placebo
3
1
4 6
1
1
1
1
LDE
1
1
API31510
1
Vismodegib
PEP005
1
1
Diclofen, Calcitriol, Both
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Published + ClinicalTrials.Gov
Destructive Procedures
Surgical Procedures
Topical Creams or Injectables
4
25
15
1
9 1
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Synthesis of the network evidence
(multiple-treatment meta-analysis)
• Incoherence
• Summary effects
• Ranking
• Bias modeling
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Credible intervals and predictive
intervals in network meta-analysis
Salanti, Ades, Ioannidis, JCE, 2011
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Cumulative ranking probability
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Probability of not being worse than
threshold t from the best treatment
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Modeling bias
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Reversing the paradigm
Design networks prospectively
– Data are incorporated prospectively
– Geometry of the research agenda is pre-
designed
– Next study is designed based on enhancing,
improving geometry of the network, and
maximizing the informativity given the network
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This may be happening
already?
Agenda-wide meta-analyses
BMJ 2010
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Anti-TNF agents: $ 10 billion and 43 meta-analyses,
all showing significant efficacy for single indications
Indications
RA
Psoriasis Psoriatic
arthritis
Crohn’s
disease
Juvenile
idiopathic
arthritis
Ulcerative
colitis
Ankylosing
spondylitis
5 FDA-approved anti-TNF agents
Infliximab
Etanercept
Adalimumab
Golimumab
Certolizumab pegol
1998
1998
2003
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1200 (and counting) clinical trials of
bevacizumab
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Fifty years of research with 2,000 trials:
9 of the 14 largest RCTs on systemic steroids
claim statistically significant mortality benefits
Contopoulos-Ioannidis and Ioannidis EJCI 2011
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Trial networks for neglected
tropical diseases (burden: 1 billion people)
ALB+IVM+PZQ
PZQ
Artesunate/ACT
Mefloquine
Artemether-lumefantrine
Mirazid
Oxamniquine
Micronutrients
Placebo/NT
Micronutrients+PZQ
PZQ+ALB
ALB
PZQ+calcitriol
Calcitriol
Oltipraz
Metrifonate
Niridazole
Hycanthone
Potassium antimony nitrate
Lucanthone
Oxamniquine+PZQ
Tartar emetic
Lucanthone+tartar emetic
Metrifonate+PZQ
Metrifonate+niridazole
PZQ+LEV
LEV
PIP
PyrPam
Placebo/NT
Bephenium
LEV
Phenylene-diisothiocyanate
MEB
ThiabendazoleOxantel pyrantel pamoate
Metronidazole
ALB
IVM
PZQ
MEB+education
Education
ALB+PZQ
Paico
Nitazoxanide
ALB+IVM
ALB+DEC
Micronutrients
Carica papaya
Tribendimidine
Thienpydin
Thienpydin+MEB
PyrPam+MEB
Fenbendazole
Bitoscanate
PIP+metronidazole
MEB+pyrantel oxantel pamoate
DEC
MEB+LEV
ALB+MEB
PIP+bephenium
ALB+education
Bephenium
TCEPyrantel emboate
Tetramisole
LEV
Phenylene di-isothiocyanate
PyrPam
MEB
Thiabendazole
Placebo/NT
FLUB
Oxantel pyrantel pamoate
ALB
Metrifonate
PZQ
IVM
IVM+ALB
PZQ+ALB
DEC
LEV+MEB
ALB+DEC
Carica papaya
Tribendimidine
PIP+bephenium
Bitoscanate
Fenbendazole
MEB+ALB
Neobedermin
Phenylene di-isothiocyanate+TCE
PIP
PyrPam
Placebo/NT
Bephenium
LEV
Phenylene-diisothiocyanate
MEB
ThiabendazoleOxantel pyrantel pamoate
Metronidazole
ALB
IVM
PZQ
MEB+education
Education
ALB+PZQ
Paico
Nitazoxanide
ALB+IVM
ALB+DEC
Micronutrients
Carica papaya
Tribendimidine
Thienpydin
Thienpydin+MEB
PyrPam+MEB
Fenbendazole
Bitoscanate
PIP+metronidazole
MEB+pyrantel oxantel pamoate
DEC
MEB+LEV
ALB+MEB
PIP+bephenium
ALB+education
Sitamniquine
Ampho B
Liposomal amphotericin+miltefosine
Liposomal amphotericin+paromomycin
Miltefosine+paromomycin
Paromomycin
PAs
PAs+paromomycin
Liposomal amphotericin
Miltefosine
ABLC
Pentamidine
Aminosidine
PAs+interferon gamma
Ketoconazole
PAs+aminosidine
PAs+pentamidine
PAs+allopurinol
PAs+ketoconazole
PAs+LEV
Pentamidine+allopurinol
Kappagoda and Ioannidis, BMJ, 2012
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What the next study should do?
• Maximize diversity of treatment options
• Address comparisons that have not been addressed
• Break (unwarranted) homophily
• Be powered to find an effect or narrow the credible or predictive interval for comparisons of interest
• Maximize informativity across the network of information
• Some/all of the above
• Answer questions that are important to patients, not sponsors or academics necessarily
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Meta-analysis=primary type of
prospective research
We need to think about how to design
prospectively large agendas of randomized
trials and their respective networks for
questions that are important to patients and
can make a difference in their lives
This in information should be considered a
public commodity, available transparently
to all in full details (raw data, protocols,
analysis codes)
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• Tony Ades, University of Bristol
• Despina Contopoulos-Ioannidis, Stanford
University
• Shanthi Kappagoda, Stanford University
• Fotini Karassa, University of Ioannina
• Fainia Kavvoura, Oxford University
• David Kim, Stanford University
• Dimitris Lathyris, University of Ioannina
• Davide Mauri, University of Ioannina
• Georgia Salanti, University of Ioannina
• Jean Tang, Stanford University
• Shanthi Kappagoda, Stanford University
• Vish Nair, Stanford University
• Nazmus Saquib, Stanford University
• Juliann Saquib, Stanford University
• Despina Contopoulos-Ioannidis, Stanford
University
• Jonathan Schoenfeld, Harvard University
• Thomas Pfeiffer, Harvard University
Special thanks
• Lars Bertram, Harvard University and
Max Planck Institute
• David Chavalarias, Ecole Polytechnique,
Paris
• Fainia Kavvoura, Oxford University
• Kostas Siontis, University of Ioannina
• George Siontis, University of Ioannina
• Vangelis Evangelou, University of
Ioannina
• Muin Khoury, CDC and NCI
• Panagiotis Kyzas, University of Ioannina
• Orestis Panagiotou, University of Ioannina
• Jonathan Sterne, University of Bristol
• Alex Sutton, University of Leicester
• Daniele Fanelli, University of Edinburgh
• Julian Higgins, MRC Biostatistics Unit,
Cambridge University
• Joseph Lau, ICRHPS, Tufts University
• Tiago Pereira, U Sao Paolo