Download Clinical Validation of Prognostic Biomarkers of Risk and

Clinical Validation of Prognostic
Biomarkers of Risk and
Predictive Biomarkers of Drug
Efficacy or Safety
Gene Pennello, Ph.D.
Team Leader, Diagnostics Devices Branch
Division of Biostatistics
Office of Surveillance and Biometrics
Center for Devices and Radiological Health, FDA
SAMSI Risk Perception Policy Practice Workshop
October 3, 2007
1
Outline
•
•
•
•
FDA and Device Regulation
Types of Biomarkers
Validation of Diagnostics
Predictive and Prognostic Biomarkers
– Definitions, Endpoints
– Study Designs for Predictive Biomarkers
• Prospective Designs – efficiency comparison
• Prospective-Retrospective Designs
• Summary
2
FDA
CDER
Drugs
CDRH,
Devices
CVM,
Veterinary
CBER,
Biologics
CFSAN,
Food
NCTR
3
What are Medical Devices?
An item for treating or diagnosing a health condition
whose intended use is not achieved primarily by
chemical or biological action within the body (Section
201(h) of the Federal Food Drug & Cosmetic (FD&C)
Act).
Definition by exclusion: Simply put, a medical device
is any medical item for use in humans that is not a
drug nor a biological product.
4
Example of Medical Devices
Relatively Simple Devices
tongue depressors
thermometers
latex gloves
simple surgical instruments
Ophthalmic devices
intraocular lenses
PRK lasers,
Radiological devices
MRI machines
CT scanners
digital mammography
computer aided detection
Cardiovascular Devices
pacemakers
defibrillators
heart valves
coronary stents
artificial hearts
Monitoring Devices
glucometers
bone densitometers
Diagnostic Devices
diagnostic test kits for HIV
prostate-specific antigen (PSA) test
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human papillomavirus (HPV) test
Example of Medical Devices
Dental, Ear, Nose, and
Throat Devices
hearing aids
bronchoscopy system
General, Surgical, and
Restorative Devices
breast implants
artificial hips
spinal fixation devices
artificial skin
Emerging technologies
multiplex genetic tests (e.g., for
multiple mutations or microbes)
Genomic and proteomic Dx tests
Nanotechnological devices
Microspheres for molecular
treatment of cancer
Robotics
Theranostics (predictive
biomarkers of response or
adverse reaction to therapy).
Artificial pancreas
6
Example of Medical Devices
Due to the wide variety in technology,
complexity, and intended use,
medical devices can present novel
statistical design and analysis challenges.
7
Device Regulation
Decision to approve a PMA application must “rely
upon valid scientific evidence to determine whether
there is reasonable assurance that the device is
safe and effective”.
“Valid scientific evidence is evidence from well
controlled studies, partially controlled studies and
objective trials without matched controls, well
documented case histories conducted by qualified
experts that there is a reasonable assurance of
safety and effectiveness . . .”
U.S. Code of Federal Regulations, Title 21 (Food and Drugs), U.S. Government Printing Office,
Washington DC, 2001, Part 860.7 Web address
http://www.access.gpo.gov/nara/cfr/waisidx_01/21cfr860_01.html (Accessed February, 2002)
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Device Regulation
Least Burdensome Provisions of FDA
Modernization Act (1997)
“Secretary shall only request information that
is necessary to making substantial
equivalence determinations.”
“Secretary shall consider, …, the least
burdensome appropriate means of
evaluating device effectiveness that would
have a reasonable likelihood of resulting in
approval.”
U.S. Code of Federal Regulations, Title 21 (Food and Drugs), U.S. Government Printing
Office, Washington DC, 2001, Part 513(i)(1)(D) and 513(a)(3)(D)(ii). Web address
9
http://www.access.gpo.gov/nara/cfr/waisidx_01/21cfr860_01.html
FDA Least Burdensome Guidance
FDA Guidance: The Least Burdensome
Provisions of the FDA Modernization Act
of 1997: Concept and Principles (2002)
“Modern statistical methods may also play an
important role in achieving a least
burdensome path to market. For example,
through the use of Baysian [sic] analyses,
studies can be combined in order to help
reduce the sample size needed for the
experimental and/or control device.”
10
Examples of Less Burdensome
Non-U.S. data
Surrogate endpoints (e.g., acute follow-up)
Interim analysis, Adaptive design
Bayesian methods (e.g., to reduce sample size)†
Propensity Scores for historical controls
Sensitivity analysis for missing data.
Note, could trade clinical for statistical burden
†FDA
Draft Guidance for the Use of Bayesian Statistics in Medical Device (released May
11
23, 2006) www.fda.gov/cdrh/osb/guidance/1601.html
Least Burdensome Provision
• Least burdensome provision in FDAMA of
1997 is directed to both medical devices
and diagnostics (including biomarkers).
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Device Risk Classification
Class I: Devices for which “general controls”
provide reasonable assurance of the safety and
effectiveness.
Class II: “General controls” insufficient, Can
establish “special controls” (performance standards
[CLIA, ISO], FDA guidance. May require clinical
data on a 510(k).
Class III: General and special controls insufficient.
Life-sustaining/supporting, substantial importance
in preventing impairment of human health, potential
unreasonable risk of illness or injury. Needs premarket approval (PMA).
13
Post-Market Transformation
• “Make postmarket data more widely available to
Center staff and supplement search and
reporting tools”
– "Investigate the use of data and text mining
techniques to identify the "needles in the haystack" by
identifying patterns in the incoming data that equate
to public health signals.”
– Example is WebVDME Bayesian data-mining
• Design a pilot project to test the usefulness of
quantitative decision-making methods for
medical device regulation across the total
product life cycle
http://www.fda.gov/cdrh/postmarket/mdpi-report-1106.html
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Types of Biomarkers
•
•
•
•
•
•
Diagnostic
Early detection (screening), enabling intervention at
an earlier and potentially more curable stage than
under usual clinical diagnostic conditions
Monitoring of disease response during therapy, with
potential for adjusting level of intervention (e.g. dose)
on a dynamic and personal basis
Risk assessment leading to preventive interventions
for those at sufficient risk
Prognosis, allowing for more aggressive therapy for
patients with poorer prognosis
Prediction of safety or efficacy (response) of a
therapy, thereby providing guidance in choice of
therapy
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Types of Biomarkers
•
•
•
•
•
•
Diagnostic
Early Detection (screening)
Monitoring
Risk Assessment
Prognostic
Predictive of Safety or Efficacy
The first three are considered together,
where the focus is on identifying the
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disease or condition.
Types of Biomarkers
•
•
•
•
•
•
Diagnostic
Early Detection (screening)
Monitoring
Risk Assessment
Prognostic
Predictive of Safety or Efficacy
The last three are attempting to predict
the future.
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Analytical Validation
How well are you measuring the measurand?
– Precision / Reproducibility
– Method Comparison
– LoB, LoD, LoQ
– Linearity
– Stability
Clinical Laboratory Standards Institute (CLSI)
(http://www.nccls.org/)
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Clinical Validation (“Qualification”)
– Does the test have clinical utility?
– Does it have added value over standard tests
(e.g, clinical covariates like age, tumor size,
stage)?
– May or may not require a clinical study
•
EX. Roche Amplichip
CDRH guidance document: “Statistical Guidance on Reporting
Results from Studies Evaluating Diagnostic Tests” issued in final
form in March, 2007, concerns reporting agreement when there is no
perfect standard and also discrepancy resolution.
19
http://www.fda.gov/cdrh/osb/guidance/1620.html
Roche AmpliChip CYP450 Test
(CDRH de novo 510(k) K042259)
Genotypes two cytochrome P450 genes (29 polymorphisms in
CYP2D6 gene, 2 in CYP2C19) to provide the predictive phenotype
of the metabolic rate for a class of therapeutics metabolized primarily
by CYP2D6 or CYP2C19 gene products. The phenotypes are
(1) Poor metabolizers:
(3) Extensive metabolizers:
(2) Intermediate metabolizers: (4) Ultrarapid metabolizers:
Cytochrome P450s are a large multi-gene family of enzymes found
in the liver, and are linked to the metabolism of approximately 7080% of all drugs. Among them, the polymorphic CYP2D6 and
CYP2C19 genes are responsible for approximately 25% of all
CYP450-mediated drug metabolism. A polymorphism in these
enzymes can lead to an excessive or prolonged therapeutic effect or
drug-related toxicity after a typical dose by failing to clear a drug
from the blood or by changing the pattern of metabolism to produce
toxic metabolites.
20
http://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfPMN/pmn.cfm
Adding Value to Standard Clinical
Predictors
1) Head to Head: Marker superior to
clinical predictors at predicting outcome.
2) Incremental Improvement:
Combination superior to clinical
predictors alone.
3) Marker Predictive within Clinical
Strata: e.g., HR(+, –) significant within
age, tumor grade, tumor size groups.
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Multivariate Index Assays
• An IVDMIA is a device that:
– Combines the values of multiple variables using an
interpretation function to yield a single, patientspecific result (e.g., a “classification,” “score,” “index,”
etc.), that is intended for use in the diagnosis of
disease or other conditions, or in the cure, mitigation,
treatment or prevention of disease, and
– Provides a result whose derivation is non-transparent
and cannot be independently derived or verified by
the end user. MIA result could be a binary
(dichotomous) (such as yes or no), categorical (such
as disease type), ordinal (such as low, medium, high)
or a continuous scale.
– Source: FDA MIA Draft Guidance
http://www.fda.gov/cdrh/oivd/guidance/1610.html22
Typical Endpoints for Prognostic or
Predictive Biomarkers
1. Time to Event
Treatment
Median Survival Time
A
B
Hazard Ratio
6 months
12 months
0.5
2. Event by Time t
Treatment
R
Not R
Response Rate
A
B
30
10
30
50
0.50 (30/60)
0.13 (10/60)
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Relative Risk vs. Diagnostic Accuracy†
Marker
Event by Time t
3.0 =
(30/60)/(10/60)
+
30
30
60
–
10
50
60
Relative
Risk
Se
Sp
40
80
120
PPV
0.50 (30/60)
NPV
0.83 (50/60)
E
No E
0.75 (30/40)
0.63 (50/80)
Relative Risk looks good, but Dx accuracy
not great → limited clinical utility?
†Example taken from Emir, Wieand, Su, Cha, Analysis of repeated
markers used to predict progression of cancer Statist. Med., 17, 256378, 1998.
24
Hazard Ratio vs. Diagnostic Accuracy†
• NCCTG Mayo Clinic Study. CA15-3 ratio
as diagnostic for progression of breast
cancer (as determined by physical exam).
Hazard Ratio
Se
Sp
PPV
2.3 (p = 0.0002)
0.30 (0.17,0.43)
0.82 (0.74,0.89)
0.27 (0.21,0.33)
†Example taken from Emir, Wieand, Su, Cha, Analysis of repeated
markers used to predict progression of cancer Statist. Med., 17, 256378, 1998.
25
Diagnostic Performance
Sensitivity
(TP rate):
fraction of
responders
who test +
Specificity
(TN rate):
fraction of
non-responders
who test –
FP rate:
fraction of
non-responders
who test +
Test is useful if TP rate > FP rate, i.e.,
sensitivity + specificity > 1.
EX. Useless test: sensitivity 0.80,
specificity 0.20
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Diagnostic Performance
Positive
predictive
value (PPV):
fraction of
test +’s who
respond
Negative
predictive
value (NPV):
fraction of
test –’s who
don’t respond
1 – NPV:
fraction of
test –’s who
respond
Test is useful if PPV + NPV > 1
EX. Useless test: PPV 0.60,
NPV 0.40
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d
A ROC curve is a plot of sensitivity (true positive rate) vs. 1-specificity (false
positive rate) over all possible cutoff points for the test. The test is
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informative if the area under the curve is greater than 0.5.
Prognostic Biomarker (Strong Def’n)
Prognostic factor. Informs about an outcome
independent of specific treatment (ability of
tumor to proliferate, invade, and/or spread).
Prognostic biomarker is associated with
likelihood of an outcome (e.g., survival,
response, recurrence) such that magnitude
of association is independent of treatment.
On some scale, treatment and biomarker
effects are additive, that is, do not interact.
29
HR(A,B)=0.67
HR(A,B)=0.67
30
31
Prognostic Biomarker (Weak Def’n)
Prognostic factor. Informs about an outcome
independent of specific treatment (ability of
tumor to proliferate, invade, and/or spread).
Prognostic biomarker is associated with
likelihood of an outcome (e.g., survival,
response, recurrence) in a population that is
untreated or on a “standard” (non-targeted)
treatment.
If population is clearly defined, than can use
to choose more or less aggressive therapy,
but not specific therapies, per se.
32
HR(A,B)=0.67
HR(A,B)=0.67
33
Prognostic Biomarker
• Her2-neu for node-negative women with breast
cancer – prognostic for recurrence
• Breast cancer prognostic test based on
microarray gene expression of RNAs extracted
from breast tumor tissue to assess a patient’s
risk for distant metastasis for women less than
61 with Stage I or II disease with tumor size less
than or equal 5.0 cm and who are lymph node
negative.
(Ref.: Buyse et al. JNCI 98, 1183-1192)
34
Agendia Mammaprint Gene Signature
for Time to Distant Metastasis (N=302)
0.6
0.4
10-year:
Low risk group: 0.90 (0.85-0.96)
High risk group:0.71 (0.65-0.78)
0.2
Patients Events Risk group
111
191
18
58
Gene signature low risk
Gene signature high risk
0.0
Probability
0.8
1.0
5-year:
Low risk group: 0.95 (0.91-0.99)
High risk group: 0.78 (0.72-0.84)
0
2
4
6
8
10
12
14
80
103
64
84
43
49
Year
111
191
108
169
102
151
95
92
136
117
Number at risk
Buyse et al JNCI (2006), 98,
1183-1192
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Proportion alive at 10 years
Clinical
Gene
Signature
N
Proportion*
Low Risk
Low Risk
High Risk
High Risk
Low Risk
High Risk
Low Risk
High Risk
52
28
59
163
0.88
0.69
0.89
0.69
*Buyse
(0.74 to 0.95) Sp
(0.45 to 0.84) 1–Se
(0.77 to 0.95) Sp
(0.61 to 0.76) 1–Se
et al JNCI 2006
36
Predictive Biomarker
Predictive factor. Implies relative sensitivity
or resistance to specific treatments or
agents.
Predictive biomarker predicts differential
effect of treatment on outcome.
Treatment and biomarker interact.
Predictive biomarker can be useful for
selecting specific therapy.
37
HR(A,B)=0.5
HR(A,B)=1.0
38
Predictive Biomarker of Efficacy
Marker: HER2/neu
Treatment: Trastuzumab (Herceptin)
Objective response rate:
Herceptin+Chemo Chemo
FISH+ 95/176 (54%)
51/168 (30%)
FISH- 19/50 (38%)
22/57 (39%)
Arch. Pathol. Lab Med Jan 2007 (ASCO/CAP Guidelines)
39
Predictive Biomarkers for Safety
• Predict risk of an adverse event
dependent on the biomarker
• Example
– UGT1A1, cleared by FDA, to predict the risk
of neutropenia in patients taking irinotecan for
colorectal cancer
40
Prospective Study Designs for
Predictive Markers
• Untargeted Design (Reference)
Validate Treatment, Marker Simultaneously
• Marker by Treatment Design
• Targeted Design (Marker + Subset Only)
• Marker Strategy Design
• Historical Control
41
Untargeted Design (Reference)
• Test if drug works in entire population.
• Mixture of marker + and – drug effects.
• Can store samples if test is not ready.
42
Marker by Treatment (Interaction)
Design
• A Randomized Block Design
• Can test for biomarker by treatment interaction
(predictive biomarker)
• Test needs to be available before trial ensues.
43
Marker by Treatment Design
Questions
• Test Drug Overall and within Marker + Subset
– 0.04, 0.01 tests suggested to control Type I error rate
at 0.05 (Simon), but subset could drive overall result.
– Frequentist multiplicity penalty may preclude subset
testing as good business strategy.
– Statement about drug, not biomarker
• Test Marker Overall and within Drug Subset
– Statement about marker, not drug.
• Test for Treatment by Marker Interaction
– Simultaneously validates drug and marker.
44
Targeted Design
Test if drug works in subset.
Cannot test if marker discriminates. Only
PPV available.
45
Efficiency of Designs
Marker
Relative
Prevalence Efficacy*
Relative Efficiency
Targeted Interaction
Design† Design ††
25%
0%
16x
8x
50%
0%
4x
2x
75%
0%
1.8x
0.9x
Efficiency gain depends
on marker prevalence,
relative efficacy, and
difference tested.
* Marker – to Marker + Patients
†Simon
& Maitournam, CCR 2004
†† Marker
by Treatment Design: Test for
Interaction approx. efficiency
enriching with half +’s, half –’s.
46
Efficiency of Designs
Marker
Relative
Prevalence Efficacy*
Relative Efficiency
Targeted Interaction
Design† Design ††
25%
25%
5.2x
1.5x
50%
25%
2.6x
0.7x
75%
25%
1.5x
0.4x
Efficiency gain depends
on marker prevalence,
relative efficacy, and
difference tested.
* Marker – to Marker + Patients
†Simon
& Maitournam, CCR 2004
†† Marker
by Treatment Design: Test for
Interaction approx. efficiency
enriching with half +’s, half –’s.
47
Efficiency of Designs
Marker
Relative
Prevalence Efficacy*
Relative Efficiency
Targeted Interaction
Design† Design ††
25%
50%
2.5x
0.3x
50%
50%
1.8x
0.2x
75%
50%
1.3x
0.1x
Efficiency gain depends
on marker prevalence,
relative efficacy, and
difference tested.
* Marker – to Marker + Patients
†Simon
& Maitournam, CCR 2004
†† Marker
by Treatment Design: Test for
Interaction approx. efficiency when
enriching with half +’s, half –’s.
48
Improving Efficiency of Interaction
Design
• Enrich with Test Positives if Pr(+) is low
• Find scale such that marker and treatment
effects are additive
• Adaptive Randomization
• Bayesian subset analysis
• If reader variability (e.g., IHC), then use
multiple readers.
• Prior Information
49
Possibilities for Increasing
Efficiency of Interaction Design
• Enrich with Test Positives if Pr(+) is low
– Estimates of Sensitivity and Specificity are
biased because they depend on Pr(+).
– Use inverse probability weighting (Horvitz,
Thompson, 1952) or Bayes Theorem (Begg,
Greenes, 1983) to obtain unbiased estimates.
50
A Marker-Based Strategy
Pro:
Con:
More ethical, perhaps.
More patients given experimental drug.
Test utility based on PPVE, NPVE.
Cannot assess test-treatment interaction. 51
Marker-Based Strategy
Test +
Response
R
Not R
E
a
b
P
0
0
E Naïve
E Unb’d
Se
a / (a+c)
a / (a+2c)
Sp
d / (d+b)
2d / (2d+b)
Test –
PPV a / (a+b) same
R
Not R
E
c
d
P
e
f
NPV d / (c+d)
same
52
A Marker-Based Strategy
Test +
Response
E Naïve E Unb’d
R
Not R
E
20
20
40
P
0
0
0
20
20
40
Se
20/43
(0.47)
Sp
157/177 314/334
(0.89)
(0.94)
Test –
PPV 20/40
R
Not R
E
23
157
180
P
24
156
180
46
314
360
20/66
(0.30)
Same
(0.50)
NPV 157/180 Same
(0.88)
53
Possibilities for Increasing
Efficiency of Interaction Design
• Transformation
– Find a transformation (Box-Cox?) of outcome
that makes treatment and effects additive.
– Can then pool marker effect estimates within
treatments A and B.
– Can also pool drug effect estimates within
marker + and marker – ‘s.
54
Possibilities for Increasing
Efficiency of Interaction Design
• Adaptive Randomization
– Adapt randomization ratio to treatment A and
B within biomarker subsets to maximize
(a) power, or
(b) fraction of patients on better treatment
– If response rate < 0.5 for both treatments,
then (a) and (b) are compatible, otherwise in
tension.
– Pr(+) disturbed, so need to adjust Se, Sp
55
Possibilities for Increasing
Efficiency of Interaction Design
• Bayesian subset analysis (cf. Dixon, Simon)
– Subsets modeled as exchangeable via
random effects.
– Subset estimate borrows strength from
complement subset, increasing precision of
estimate.
– However, interaction estimate more
conservative relative to usual non-Bayesian
analysis.
56
Bayesian Subset Analysis
• Power is enhanced to show drug works in
marker + subset (blue).
• Power is enhanced to show marker works
(discriminates) in patients taking drug (red)
57
Possibilities for Increasing
Efficiency of Interaction Design
• Use Multiple Readers
– EGFR IHC test (Dako) and Cetuximab and
Panitumumab (Amgen) for Colorectal Cancer. % of
cells stained and maximum staining intensity subject
to reader variability
– Use multiple readers, account for random reader
effects.
• Multiple Reader, Multiple Case Designs (MRMC)
are used for digital mammography systems and
computed aided detection (CAD) systems
• Analysis can be difficult.
58
Possibilities for Increasing
Efficiency of Interaction Design
• Prior Information (Bayesian analysis)
– Borrow strength from previous study regarded
as exchangeable with current study.
59
Marker Based Strategy Design
Marker
Based
Strategy
Register
Marker
Level (-)
Treatment A
Marker
Level (+)
Treatment B
Randomize
Test
Marker
Non Marker
Based
Strategy
Treatment A
Sargent et al., JCO 2005
60
Marker Based Strategy Design
Marker
Based
Strategy
Register
Marker
Level (-)
Treatment A
Marker
Level (+)
Treatment B
Randomize
Test
Marker
Treatment A
Non Marker
Based
Strategy
Randomize
Treatment B
Sargent et al., JCO 2005
61
Marker Based Strategy Design
• Lacks power: Differential effect
comparison diluted because some patients
in non-marker-based strategy arm get
marker-based treatment (could eliminate
these to increase power).
• Might be best suited if have > 2 treatments
or > 2 markers
– EX. Irinotecan regiment (dose, timing,
frequency) determined by UGT1A1 genotype
(6/6, 6/7, or 7/7) in colorectal cancer patients.
62
Marker Based Strategy
• If no gold standard, then can be only way
to assess effectiveness of a test.
• EX. Detection tumor of origin in cancers of
unknown primary.
– No gold standard: IHC, imaging, may fail to
identify TOO.
– Randomize patients to be managed with
• new test + standard, or
• with standard alone
– Compare arms on survival
63
Targeted Design w. Historical
Control
• Drug already on market, but has poor
response rate.
• If response rate in marker + study is
significantly greater than historical rate,
then marker discriminates.
• Limitations
– Lacks power because effect diluted.
– Need to calibrate historical rate to marker +
study (adjust for covariates).
64
Prospective-Retrospective Designs
• Prospectively apply marker to stored samples (in
retrospect).
• Can test overall, w. subset, or for interaction.
• Missing samples could introduce bias.
• RCT samples. Randomization ensures case and
control samples have similar characteristics.
• Case-control samples. Avoid selection bias by
matching on sample processing date, processing
sites, etc., and not excluding censored times.
• Reserve samples only for analytically validated
markers that are biologically plausible.
65
The Challenge of Multiplicity
• Multiplicity of classifiers
• Microarrays and proteomics
• Many predictive models could be built with
so many inputs.
• The challenge is to confirm any such
model with an independent data set.
• A caveat: the independent test data set
cannot be continually reused. Great
discipline is required in this regard.
66
Cross-Validation Pitfall
Simon, Radmacher, Dobbin, McShane (2003), Pitfalls in the Use of DNA
Microarray Data for Diagnostic and Prognostic Classification, JNCI, 95 (1)
67
Summary Remarks
• How to assess a test or biomarker is wellknown, but not as well-known in therapeutic
circles.
• Need to assess whether the biomarker adds
anything to what we already know.
• The number of possibly good biomarker
candidates is enormous but great care is
needed in restricting the search.
68
Summary Remarks
• Need to encourage least burdensome
approaches to validating biomarkers without
compromising level of evidence
• Essential to confirm marker in independent
dataset
• Studies to demonstrate informativeness of a
biomarker can be quite difficult to design,
conduct and analyze.
69
Acknowledgements
• CDRH Division of Biostatistics (DBS)
– Greg Campbell, Division Director
– Diagnostic Devices Branch (DDB)
• Lakshmi Vishnuvajjala, Branch Chief
• Estelle Russek-Cohen, Team Leader
• Gene Pennello, Team Leader
Bipasa Biswas
Harry Bushar
Arkendra De
Shanti Gomatam
Thomas Gwise
Kyungsook Kim,
Samir Lababidi
Kristen Meier
Kyunghee Song
Rong Tang
70
More References
• Sargent et al (2005). Clinical trial designs for predictive
marker validation in cancer treatment trials. J Clin Oncol
23:2020-2027.
• Pennello & Vishnuvajjala (2005). Statistical design and
analysis issues with pharmacogenomic drug-diagnostic
co-development, In American Stat. Assoc. 2005 Proc. of
the Biopharm. Section, Joint Statistical Meetings,
Minneapolis, MN, August, 2005; American Stat. Assoc.:
Alexandria, VA.
• FDA Drug-Diagnostic Co-Development Concept Paper.
April 2005.
http://www.fda.gov/cder/genomics/pharmacoconceptfn.pdf
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