CADTH 2015 Symposium

1. Incorporating data from single-arm
studies into network meta-analyses
Steve Kanters PhD(c), Kristian Thorlund PhD, Edward
Mills PhD, Jeroen Jansen PhD, Nick Bansback PhD
April 14th, 2015
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2. Network meta-analyses (NMA)
• An expansion of traditional pairwise meta-analyses that
consider multiple treatments at a time
• NMA combine direct and indirect comparisons to make the
most of the available evidence
• The utility of NMA is in providing comparative efficacy for
all therapeutics of a given medical condition
• Presently, NMAs are generally restricted to RCT evidence
• Alternative sources of evidence include comparative
observational studies and single-arm studies
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3. Potential limitations to RCT
evidence
• A large phase 3 RCT is at the top of the hierarchy of evidence
• In some situations it may be viewed as being lower on a ‘hierarchy of relevance’ than other designs
• Timeliness: A large phase 3 RCT can take years to complete
• the relevance of its findings may be reduced by the time of reporting
• E.g. in oncology, if findings of several uncontrolled trials and observational
studies may have already shown promising results
• Ethics: RCTs will often be needed to confirm treatment effects, but not always
ethical.
• Underpowered: For safety endpoints, observational studies can be much more
relevant because RCTs are likely to be too short for safety outcomes
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4. When are other sources of
evidence needed?
• For some interventions only single arm trials or observational evidence is
available.
• i.e., to connect the network.
• e.g., rare diseases.
• RCTs tend to be powered for efficacy and in turn are often underpowered
for safety.
• Observational studies can often be larger and longer and hence better inform
safety.
• Observational studies may shed light on efficacy and safety within sub-
populations.
• RCTs dominated by Caucasian participants may not speak to Asian or Black
populations.
• In time-to analyses, single arm phase IV trials may help supplement time-
to information for both efficacy and safety.
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5. Purpose
• Methods have already been suggested for
combining comparative observational studies to
RCTs
• However uptake has been slow
• The purpose of today’s talk is to discuss how to
integrate single-arm evidence into NMA
• We provide motivational examples , but perhaps the
most convincing is the integration of non-comparative
phase IV trials to safety analyses
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6. Standard NMA models
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1
1
AB
22
2
AB
jj
j
AB
1
qjk =
mj
mj +dj
ì
í
ï
îï
k = A
k = B
dj ~ Normal dAB,sd
2
( )

7. Standard model definitions
• θj are the likelihood parameters transformed by
the appropriate link function
• E.g. logit(pj), yj, log(rj)
• μj are the study effects: the part of the observed
outcome attributable to prognostic factors
• δj is the comparative treatment effect that we
seek to solve for
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8. Adjusted indirect comparison
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9. ADDING STUDIES OF OTHER
EPIDEMIOLOGICAL DESIGN TO AN RCT NMA
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10. Single-arm studies (& comparative
observational studies)
• Uncontrolled studies: Impossible to disentangle study effects from
treatment effects. Only observed outcomes.
• However, it can be useful to add these kinds of studies to synthesis
of RCT evidence….
…as long as we acknowledge their limitations with the
analyses methods!
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11. Combining RCT and to other
designs
How can we incorporate single-arm evidence to
NMA?
1. Use the single-arm evidence to create
informative priors
1. Create a virtual comparison based on patient
characteristics
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12. Informative priors
• > 65% of NMAs conducted today are conducted using Bayesian
hierarchical models
• The majority of the remaining 35% are restricted to adjusted indirect
comparisons using the Bucher method
• These tend to start with non-informative priors for the model parameters.
Specifically:
• dAB ~ N(0, 0.0001)
• μj ~ N(0, 0.0001)
• If single-arm evidence exists for both treatments A and B, we can use this
evidence to create informative priors on dAB
• For example, if dealing with a dichotomous outcome with linear model for
mean difference dAB ~ N(yB,endo – yA,endo, precendoω), where ω is a correction
weight
• Note that it does not make sense to construct informative priors on μ as this is
study specific rather than treatment specific 12

13. Indirect comparison (NMA)
incorporating single arm trial
A
B
C D
A
B
C C D
A
B
C D
1 2
3
Prediction of comparator arm given
patient characteristics in single arm
trial for D. Creation of ‘virtual’ CD
trial.
Interested in relative treatment
effect of D versus A, B and C. Only
single arm trial for D
Incorporation of ‘virtual’ CD
trial in network 13

14. Relative advantages of each
method
• Both approaches allow for the integration of
single-arm evidence to NMA
• Informative priors offer a more convenient way to
weight the evidence
• Direct inclusion into the NMA requires a more
contrived reduction of the effective sample size
• Direct inclusion lends itself better to all additional
manipulations of the NMA, such as meta-
regression adjustments
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15. APPLICATIONS
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16. Single-arm evidence as prior
Example 1 - Meningitis
• Cryptococcal meningitis is a leading cause of HIV-associated
death and is the most common cause of meningitis in sub-
Saharan Africa
• Multiple guidelines recommend use of Amphotericin B (AmB)
in combination either 5-flucytosine (5FC), where available, or
fluconazole (Azole)
• Despite high level of recommendations:
• No RCTs have shown mortality benefit for addition of Azoles to AmB
• Single, recent RCT has shown mortality benefit for addition of 5FC to
AmB
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17. Randomized Controlled Trials
( ) 17
Observational studies
Single-arm Studies
Campbell JI, Kanters S, Bennett JE, Thorlund K, et al. Comparative effectiveness of induction
therapy for human immunodeficiency virus-associated cryptococcal meningitis: A network
meta-analysis. Open Forum Infect Dis. 2015;2(1)

18. Example 1 – Meningitis
Methods applied
• Pooled single arm results for each intervention
• Used single-arm based comparative effects as
‘informative priors’ in the Bayesian NMA model
• Estimated expected comparative pairwise efficacy by
taking the difference between single arm results.
• Penalized precision of single arm comparative
estimates by 4
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19. Amphotericin
+ Azole
Conceptual
control
Rationale for Penalization
Amphotericin
+ 5FC
Conceptual
Indirect
Comparison
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20. Example 1 – Meningitis
Results
• Heterogeneity in the model was reduced
• By 26%
• Model fit was improved
• DIC 144 vs. 234
• Effect estimates were more precise
• Two comparisons became “statistically significant”
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21. Example 2 – Hepatitis C
• Sofosbuvir is a recently licensed direct acting antiviral (DAA) for
hepatitis C
• Single arm trials makes up much of the evidence for the two
Sofosbuvir regimens
• Non-RCT evidence is required to connect the network, particularly
when restricted to non-cirrhotic patients
• We analyzed the network by
• Directly including single-arm evidence by using virtual comparisons
• Integrating the single arm data through informative priors
• With informative priors with decreased precision (factor of 4)
• Excluding the single arm-evidence
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22. Application to Hepatitis C
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Randomized Controlled Trials
Single-arm Studies( ) 22
Full network
Non-cirrhotic patients only

23. Odds ratios for sustained
virological response
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Comparison
Versus P2aR
Direct inclusion
of single arm
evidence
Fully informative
priors
Penalized prior
precision (x4)
Conventional
non-informed
P2bR 0.80 (071, 0.91) 0.68 (0.56, 0.80) 0.67 (0.50, 0.87) 0.63 (0.37, 0.95)
BOC+P2bR (SDT) 2.87 (1.86, 4.49) 2.53 (1.51, 4.29) 2.53 (1.43, 4.36) 2.42 (1.02, 5.23)
BOC+P2bR (RGT) 2.36 (1.40, 3.99) 2.08 (1.11, 3.84) 2.09 (1.06, 3.91) 1.99 (0.71, 4.89)
TEL + P2aR (SDT) 2.56 (1.22, 5.41) 1.99 (0.82, 4.74) 1.98 (0.80, 4.79) 1.87 (0.55, 5.63)
TEL + P2aR (RGT) 3.68 (2.12, 6.37) 3.80 (2.02, 7.22) 3.80 (2.02, 7.16) 3.80 (1.53, 9.26)
SIM + P2aR (RGT) 4.95 (3.39, 7.33) 3.77 (2.35, 6.12) 3.75 (2.31, 6.03) 3.73 (2.04, 6.79)
SIM + P2bR (RGT) 3.42 (2.01, 5.90) 2.31 (1.06, 5.16) 2.28 (1.06, 5.08) 2.25 (0.84, 5.82)
SOF + P2aR 9.82 (6.16, 15.9) 9.35 (6.89, 12.7) 8.97 (5.01, 15.8) 6.59 (1.51, 31.1)
SOF + LDV 34.5 (19.0, 67.6) 34.1 (16.7, 69.5) 34.0 (8.1, 138.7) --
P2bR = pegylated interferon alpha-2b; P2aR = pegylated interferon alpha-2a
BOC = boceprevir; TEL = telaprevir; SIM = simeprevir;
SOF = sofosbuvir; LDV = ledipasvir;
(SDT) = standard duration therapy;
(RGT) = response guided therapy

24. TAKE HOME MESSAGES
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25. Take home messages
• Typically evidence synthesis of only RCT evidence has good
internal validity.
• In some cases adding single-arm evidence can be very
informative, especially when there are a limited number of
RCTs.
• We have to be aware of limitations of observational single-
arm evidence
• Analyses should be done using multiple methods, including those
restricted to RCTs (if possible)
• It comes back to validity vs. precision
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26. THANK YOU
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