Modelling the effects of drug dose in hierarchical network ...€¦ · (e.g., Low, Medium, and High...

Modelling the effects of drug

dose in hierarchical network

meta-analysis models to account

for variability in treatment

classifications Areti Angeliki Veroniki, MSc, PhD

Prepared for: 2016 CADTH Symposium

April 12, 2016

C. Del Giovane, D. Jackson, S. E. Straus, S. Thomas, E. Blondal, K. Thavorn, A. C. Tricco

Knowledge Translation Program,

Li Ka Shing Knowledge Institute,

St. Michael's Hospital,

Toronto, Canada

E-mail: [email protected]

Knowledge Translation, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada

I have no actual or potential conflict of interest in relation to this presentation


of the presentation

• To present approaches to model the effects of drug dosages in hierarchical network meta-analysis (NMA)

• How can we model dose-effects in NMA while accounting for the drug-dose relationship?

• To present hierarchical NMA models accounting for effects of: 1) treatment, 2) strength (e.g., low, medium, high) of dose, and 3) specific dose

• How do initial decisions on the network structure impact heterogeneity and inconsistency?

• To illustrate examples of the available approaches

• Does the effect size vary across different dose levels?

• Is there a pattern in dose-effects?

3


Network Meta-analysis (NMA)

NMA has become increasingly popular over the last two decades with ~500 publications

Temporal distribution of publications (n=494)

*2015 sample is not complete

0.2%

0.2%

0.4%

0.2%

0.2%

0.4%

1.0%

2.8%

6.3%

6.7%

11.7%

12.5%

18.3%

23.8%

10.5%

0 25 50 75 100 125

1997

1999

2000

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015*

2.4%

2.4%

• In many conditions there are multiple

treatments that could be considered

• When policy makers are considering

what interventions to cover through

health plans or what safety labels to

put on medications, they need

evidence from an NMA because this

method uses all available RCTs for a

specific clinical topic

• Clinicians and patients need to know

the safest and most effective

treatment dose for a particular

situation


Combining placebo-controlled trials to learn about toothpaste vs. rinse may yield erroneous results!

Salanti et al JCE 2009

Common Dilemma in NMA…

Lumping or splitting nodes?

Typical approach: Choice between ‘lumping’ and ‘splitting’ methods

Splitting:

Allows comparison between different treatment doses

⊠ Ignores the relationship between doses that belong to the same intervention ⊠ May lead to a relatively small number of studies across comparisons resulting in

considerable uncertainty around the overall treatment effects


Common Dilemma in NMA…

How do NMA authors deal with treatment doses?

219 (44%) of 494 NMAs published up to April 14, 2015 included different treatment doses in the network

Only 11 NMAs accounted

properly for the treatment-dose

relationship!

Guidelines on modeling different dose-effects in NMA are imperative

Lumping:

Increases the amount of evidence across comparisons and hence precision in treatment effects

⊠ Ignores the different doses included in each intervention ⊠ Cannot model studies that compare the same treatment at different doses

• Researchers have to select a specific dose for the underlying treatment

• If no other treatment is compared in the particular study, the study has to be excluded.

5%

0

20

4

0

60

8

0

Fre

qu

en

cy

NR Lump (L) Split (S) Recomm. Most Prevalent

Model

3%

51%

34%

0.5% 0.5%

S&L

6%

100


Variance components added to each new level

A B

RCTs comparing 2 treatment

dosages

within-study variance

Meta-analysis

𝜇𝐴𝐵

between-study variance Usually we assume common between-study variance across dose comparisons

Network Meta-analysis

Categorizing doses into groups (e.g., Low, Medium, and High doses)

between-dose-category variance within treatment*

*here the between dose variance is within dose-category (and not treatment)

between-dose variance within treatment

within dose category if doses are categorized

into groups

Categorizing doses into groups of treatments


Modelling dose-effects in NMA

Decision-makers are often interested in a treatment’s effectiveness at lower and higher doses

Hierarchical models can be used to address dose-effects in NMA and to facilitate the identification of the most effective treatment and optimal dose

B

A

E

D C

The potential dependence of treatment-effects of drug dose is an important issue for effectiveness and safety and is challenging in NMA because comparisons between interventions may vary in doses


Modelling dose-effects in NMA Independent dose-effects

Random dose-effects within treatments

Fixed dose-effects

Random dose-effects within different dose categories

Del Giovane et al Stat Med 2013

Dose-effects are related and exchangeable*

All dose-effects are unrelated

All dose-effects are assumed equal within the same treatment

Dose-effects are related and exchangeable accounting for the dose-category they belong to

a) within-study and b) between-study variance across doses

a) within-study and b) between-study variance within dose level

a) within-study, b) between-study within dose level, and c) between-dose variance within-treatment level

a) within-study, b) between-study within dose level, c) between-dose within-dose-category level, and d) between-dose-category variance within-treatment level

* Within the treatment they belong to


Illustrative Examples

1. Serotonin 5-hydroxytryptamine 3 (5HT3) receptor antagonists to treat patients undergoing surgery

2. Anti-vascular endothelial growth factor (anti-VEGF) drugs to treat patients with wet age-related macular degeneration (AMD)

* All analyses have been performed in a Bayesian framework using OpenBUGS


Illustrative Example 1

Aim

To estimate the relative safety of

5HT3 receptor antagonists at

different dosages

Serotonin 5-hydroxytryptamine 3 (5HT3) receptor antagonists to treat patients undergoing surgery

• 5-HT3 receptor antagonists are commonly used to decrease nausea and

vomiting for surgery patients

• Evidence shows that these agents may be harmful, but it is unclear whether there

are certain doses that increase the risk of arrhythmia

• Clinicians and patients are interested in the safest treatment dose

• We used data from a published systematic review and NMA

Treatment Dose (mg) Placebo Ondansetron 1mg, 2mg, 3mg, 4mg, 8mg, 16mg, 24mg Granisetron 0.1mg, 1mg, 3mg Dolasetron 12.5mg, 25mg, 50mg, 100mg, 200mg Tropisetron 0.5mg, 2mg, 5mg Ramosetron 0.3mg, 0.9mg

Tricco et al BMC Medicine 2015



• Patients of any age undergoing any type of surgery who were given a 5-HT3

receptor antagonist for nausea and/or vomiting

• Databases searched: MEDLINE, Embase, the Cochrane Central Register of

Controlled Trials; protocol registries and conference proceedings – up to January

2013

• One dichotomous outcome was considered to assess safety: arrhythmia –

We will also assess efficacy using the vomiting outcome

• 29 RCTs were eligible after the screening process

• Exclusion:

Compared a combination of treatments against a single treatment (1 study).

Did not report the treatment dosages used (1 study).

• In total, 27 studies were included in the analysis

5HT3 receptor antagonists to treat patients undergoing surgery



5-HT3 receptor antagonists for surgery


Arrhythmia : 27 studies, 8871 patients, 6 treatments, 21 doses

Circles from outside in refer to: 1st: Fixed Effects model 2nd: Random Effects (with dose consistency) 3rd: Random Effects (no dose consistency) 4th: Independent Effects

1 : Placebo 2 : Ondansetron-Fixed 3 : Ondansetron-1mg 4 : Ondansetron-2mg 5 : Ondansetron-3mg 6 : Ondansetron-4mg 7 : Ondansetron-8mg 8 : Ondansetron-16mg 9 : Ondansetron-24mg 10 : Granisetron-Fixed 11 : Granisetron-0.1mg 12 : Granisetron-1mg 13 : Granisetron-3mg

14 : Dolasetron-Fixed 15 : Dolasetron-12.5mg 16 : Dolasetron-25mg 17 : Dolasetron-50mg 18 : Dolasetron-100mg 19 : Dolasetron-200mg 20 : Tropisteron-Fixed 21 : Tropisteron-0.5mg 22 : Tropisteron-2mg 23 : Tropisteron-5mg 24 : Ramosetron-Fixed 25 : Ramosetron-0.3mg 26 : Ramosetron-0.9mg

safest

Least safe

Veroniki et al JCE 2016



1 .0225 1 44.5

OR (95% CrI) Common comparator: Placebo

Dolasetron

0.75 (0.52, 1.08)

0.76 (0.51, 1.11)

0.79 (0.47, 1.31)

0.74 (0.54, 1.03)

0.76 (0.40, 1.44)

0.76 (0.50, 1.11)

0.79 (0.46, 1.31)

0.76 (0.53, 1.09)

0.76 (0.53, 1.09)

0.75 (0.51, 1.10)

0.61 (0.32, 1.11)

0.73 (0.50, 1.05)

0.76 (0.50, 1.10)

0.53 (0.24, 1.10)

0.73 (0.48, 1.05)

0.75 (0.51, 1.11)

Fixed

12.5mg

25mg

50mg

100mg

200mg

Placebo better Active better

0.88 (0.35, 2.23)

0.84 (0.35, 2.33)

1.21 (0.36, 4.34) 0.84 (0.34, 2.28)

0.84 (0.35, 2.33) 0.62 (0.14, 2.48) 0.81 (0.33, 2.19) 0.88 (0.35, 2.23)

0.88 (0.34, 2.24) 0.76 (0.18, 2.80) 0.82 (0.33, 2.22)

Fixed

0.1mg

1mg

3mg

Granisetron

1 .0225 1 44.5

OR (95% CrI) Common comparator: Placebo

Ondansetron


0.91 (0.62, 1.30) 0.77 (0.42, 1.33) 0.89 (0.64, 1.20)

1.01 (0.46, 2.00)

0.90 (0.69, 1.18) 1.11 (0.54, 2.30) 0.92 (0.66, 1.30) 0.91 (0.62, 1.30) 1.37 (0.44, 4.51) 0.91 (0.65, 1.32) 0.91 (0.62, 1.30) 0.72 (0.19, 2.72) 0.90 (0.62, 1.27)

0.77 (0.42, 1.33)

0.91 (0.62, 1.30)

0.91 (0.65, 1.28) 0.91 (0.62, 1.30) 0.95 (0.50, 1.75) 0.90 (0.65, 1.24) 0.91 (0.62, 1.30) 0.65 (0.18, 2.29) 0.90 (0.62, 1.26) 0.91 (0.62, 1.30)

Fixed

1mg

2mg

3mg

4mg

8mg

16mg

24mg

Ramosetron

1.20 (0.70, 2.11) 1.27 (0.53, 2.95) 1.20 (0.67, 2.18) 1.24 (0.67, 2.38) 1.16 (0.48, 2.78) 1.19 (0.66, 2.16) 1.24 (0.66, 2.40)

Fixed 0.3mg

0.9mg

Tropisetron

0.85 (0.43, 1.76)

0.83 (0.33, 2.06) 0.85 (0.43, 1.63)

0.86 (0.44, 1.67) 0.75 (0.02, 7.20) 0.86 (0.42, 1.73) 0.85 (0.43, 1.76) 0.86 (0.25, 2.59) 0.86 (0.43, 1.67) 0.85 (0.43, 1.75)

Fixed

0.5mg

2mg

5mg

Fixed Effects

Random Effects (with dose consistency)

Independent Effects

Random Effects (no dose consistency)



FE model RE model with no dose consistency

RE model with dose consistency

Independent dose-effects with no dose consistency

𝜏2 (95% CrI)

0.01 (0.00, 0.10)

0.01 (0.00, 0.10)

0.01 (0.00, 0.12)

0.04 (0.00, 0.47)

𝜎2 (95% CrI)

0.01 (0.00, 0.08)

0.00 (0.00, 0.07)

DIC 109.44 110.86 111.02 133.68

D 90.29 90.42 89.91 97.77

pD 19.15 20.44 21.11 35.91

Data points

82 82 82 82

The design by treatment interaction model suggested consistency on both treatment (χ2=2.46, P-value= 0.7829, τ2=0.00) and dose (χ2=14.35, P-value=0.6423, τ2=0.00) levels

5HT3 surgery – Arrhythmia

Spiegelhalter et al J R Stat Soc Ser B Stat Methodol 2002



Aim

To estimate the relative efficacy of anti-VEGF agents at different dosages

Anti-vascular endothelial growth factor (anti-VEGF) drugs to treat patients with wet age-related macular degeneration (AMD)

• Anti-VEGF drugs are injected into the eye where they inhibit the

abnormal angiogenesis that underlies many retinal conditions

associated with vision loss

• It is unclear whether these agents have equivalent efficacy, and specifically whether

there are certain doses that patients would benefit from when comparing these

different drugs CADTH Therapeutic Review 2015

Treatment Abbreviation Dose (mg) Placebo PLAC Ranibizumab RANI 0.3mg, 0.5mg, 2mg,

Bevacizumab BEVA 1.25mg, 2.5mg Aflibercept AFLI 0.5mg, 2mg, 4mg Triamcinolone acetonide TRIAM 4mg

Photodynamic therapy with verteporfin PDT Standard fluence, reduced fluence



Anti-VEGF drugs to treat patients with wet AMD

CADTH Therapeutic Review 2015

• Adults (≥ 18 years) with wet AMD

• Databases searched: MEDLINE, Embase, Cochrane Central Register of Controlled

Trials; Grey literature (trial protocols) – up to October 2015

• Two dichotomous outcomes were considered: vision gain & vision loss

• 35 RCTs were eligible after the screening process

• Exclusion:

compared the same treatment at the same dose (8 studies).

did not report vision gain outcome (3 studies) or vision loss outcome (3

studies).

• In total, 24 studies were included in the analysis for both outcomes


Anti-vascular endothelial growth factor

Vision gain: 24 studies, 9444 patients, 5 treatments, 11 doses

Vision loss: 24 studies, 8311 patients, 6 treatments, 12 doses

RANI – 0.5 mg

RANI – 0.3 mg

RANI – 2 mg BEVA – 1.25 mg

BEVA – 2.5 mg

AFLI – 0.5 mg

AFLI – 2 mg

AFLI – 4 mg

PLAC PDT - standard

PDT - reduced

RANI

BEVA

AFLI

PLAC

PDT

RANI

BEVA AFLI

PLAC

PDT TRIAM

RANI – 0.5 mg

RANI – 0.3 mg

RANI – 2 mg BEVA – 1.25 mg

BEVA – 2.5 mg

AFLI – 0.5 mg

AFLI – 2 mg

AFLI – 4 mg

PLAC PDT- standard

PDT - reduced

TRIAM – 4 mg

Treatment Level

Dose Level


Anti-vascular endothelial growth factor Vision gain: 24 studies, 9444 patients, 5 treatments, 11 doses

Treatment Level

Fixed Effects

Independent Effects



Dose Level

RANI vs PLAC

BEVA vs PLAC

AFLI vs PLAC

PDT vs PLAC

8.46 (5.04,14.09)

8.45 (4.93,14.13)

7.93 (4.57,13.47)

7.72 (4.54,12.76)

6.80 (3.63,12.26)

6.70 (3.48,12.31)

6.66 (3.24,13.08)

6.33 (2.88,13.03)

7.74 (3.65,15.58)

7.65 (3.43,15.87)

7.87 (3.74,15.25)

5.38 (2.22,11.85)

3.09 (1.42,7.59)

3.15 (1.35,8.05)

3.13 (1.32,8.19)

3.54 (1.23,11.00)

OR (95%CrI) Treatment Comparison

2.2 1 10 20

Odds Ratio

Active better Placebo better

RANI-0.3mg vs PLAC

RANI-0.5mg vs PLAC

RANI-2mg vs PLAC

BEVA-1.25mg vs PLAC

BEVA-2.5mg vs PLAC

AFLI-0.5mg vs PLAC

AFLI-2mg vs PLAC

AFLI-4mg vs PLAC

PDT-reduced vs PLAC

PDT-standard vs PLAC

8.44 (4.08,17.76) 7.50 (4.69,12.03) 7.18 (4.41,11.64) 8.47 (4.07,17.64) 8.50 (5.20,13.52) 8.76 (5.27,14.17) 8.42 (4.05,17.55) 7.79 (4.35,13.49) 7.30 (3.14,16.09) 6.70 (2.89,14.62) 6.87 (3.71,11.84) 7.07 (3.74,12.41) 6.69 (2.83,14.75) 6.50 (3.03,13.03) 5.62 (1.80,16.49) 7.65 (3.00,18.79) 7.82 (4.01,14.30) 7.77 (3.76,14.98) 7.67 (2.97,18.82) 8.61 (4.39,15.93) 9.20 (4.50,17.78) 7.63 (2.98,18.47) 7.35 (2.82,14.94) 2.22 (0.33,11.29)

3.13 (1.19,9.26) 3.19 (1.28,8.72)

4.20 (0.74,25.26) 3.14 (1.20,9.16) 3.07 (1.39,7.12) 2.99 (1.35,7.28)


.3 1 9 27

Active better

Placebo better


Anti-vascular endothelial growth factor - SUCRA

Vision gain: 24 studies, 9444 patients, 5 treatments, 11 doses

Treatment Level

PD

T

Treatment hierarchy according to the SUrface under the Cumulative RAnking curve

Salanti JCE 2009; Veroniki et al JCE 2016

Dose Level

Circles from outside in refer to: 1st: Fixed Effects model 2nd: Random Effects (no dose consistency) 3rd: Random Effects (with dose consistency) 4th: Independent Effects


Anti-VEGF - Vision Gain




𝜏2 (95% CrI)

0.09 (0.02, 0.29)

0.04 (0.00, 0.23)

0.06 (0.00, 0.24)

0.07 (0.00, 0.29)

𝜎2 (95% CrI)

0.05 (0.00, 0.23)

0.02 (0.00, 0.33)

DIC 99.64 99.03 99.18 102.43

D 59.1 57.58 58.86 59.06

pD 40.54 41.45 40.32 43.37

Data points

55 55 55 55

The design by treatment interaction model suggested consistency on the dose level: (χ2=1.94, P-value=0.585, τ2=0.08) – Consistency cannot be assessed on the treatment level



Anti-vascular endothelial growth factor Vision loss: 24 studies, 8311 patients, 6 treatments, 12 doses

Treatment Level

Fixed Effects

Independent Effects



Dose Level

RANI vs PLAC

BEVA vs PLAC

AFLI vs PLAC

PDT vs PLAC

TRIAM vs PLAC

0.11 (0.08,0.16) 0.11 (0.07,0.17) 0.12 (0.07,0.25) 0.15 (0.09,0.25) 0.10 (0.06,0.17) 0.10 (0.06,0.18) 0.11 (0.05,0.30) 0.15 (0.06,0.33) 0.10 (0.05,0.18) 0.10 (0.05,0.20) 0.09 (0.04,0.20) 0.06 (0.02,0.18) 0.45 (0.33,0.60) 0.44 (0.30,0.63) 0.92 (0.28,2.76) 0.90 (0.40,2.01) 0.91 (0.42,2.03) 0.91 (0.38,2.21) 0.40 (0.16,0.71) 0.29 (0.16,0.49)


0 .1 1 .8 3

Odds Ratio

Placebo better

Active better

RANI-0.3mg vs PLAC

RANI-0.5mg vs PLAC

RANI-2mg vs PLAC

BEVA-1.25mg vs PLAC

BEVA-2.5mg vs PLAC

AFLI-0.5mg vs PLAC

AFLI-2mg vs PLAC

AFLI-4mg vs PLAC

PDT-reduced vs PLAC

PDT-standard vs PLAC

TRIAM-4mg vs PLAC

0.11 (0.07,0.20) 0.11 (0.07,0.16) 0.10 (0.07,0.16) 0.11 (0.07,0.20) 0.11 (0.07,0.16) 0.11 (0.07,0.17) 0.11 (0.06,0.20) 0.14 (0.08,0.40) 0.30 (0.10,0.96) 0.10 (0.05,0.20) 0.10 (0.06,0.17) 0.10 (0.06,0.18) 0.10 (0.05,0.20) 0.12 (0.06,0.38) 0.21 (0.06,0.80) 0.10 (0.04,0.22) 0.09 (0.05,0.18) 0.09 (0.05,0.19) 0.10 (0.04,0.22) 0.10 (0.05,0.19) 0.10 (0.05,0.20) 0.10 (0.04,0.22) 0.08 (0.02,0.21) 0.02 (0.00,0.33) 0.91 (0.36,2.35) 0.33 (0.12,0.59) 0.18 (0.06,0.45) 0.91 (0.36,2.37) 0.46 (0.34,0.62) 0.47 (0.35,0.64) 0.44 (0.26,0.73) 0.92 (0.41,2.00) 0.90 (0.40,2.01)


1 .3 2.5

Odds Ratio



Anti-vascular endothelial growth factor - SUCRA

Vision loss: 24 studies, 8311 patients, 6 treatments, 12 doses

Treatment Level

Treatment hierarchy according to the SUrface under the Cumulative RAnking curve

Salanti JCE 2009; Veroniki et al JCE 2016

Dose Level

Circles from outside in refer to: 1st: Fixed Effects model 2nd: Random Effects (no dose consistency) 3rd: Random Effects (with dose consistency) 4th: Independent Effects

BEVA

PDT


Anti-VEGF - Vision loss




𝜏2 (95% CrI)

0.02 (0.00, 0.16)

0.02 (0.00, 0.17)

0.02 (0.00, 0.16)

0.02 (0.00, 0.17)

𝜎2 (95% CrI)

0.01 (0.00, 0.17)

0.08 (0.00, 0.93)

DIC 97.57 98.70 96.93 98.44

D 61.32 60.13 58.04 56.85

pD 36.25 38.57 38.9 41.59

Data points

54 54 54 54

The design by treatment interaction model suggested consistency on the dose level: (χ2=1.17, P-value=0.761, τ2=0.00) – Consistency cannot be assessed on the treatment level



Summary…

Modelling dose-effects in NMA and accounting for the intervention-dose relationship:

• Adds to borrow strength in estimating dose-effects within treatment classes • Overcomes problems with sparse data in the treatment networks • Can incorporate studies that compare the same treatment at different doses • Allows the identification of not only the best treatment in a network, but

also the most effective dose • Increases power compared to carrying out several independent subgroup

analyses, lumping or extreme splitting approaches • May provide additional insight on heterogeneity, inconsistency, intervention

ranking, and hence decision-making

Why is it important to model dose-effects in NMA?

Different approaches used to classify treatments or model dose-effects in a network may result in important variations in interpretations drawn from NMA

But….


Summary…

The available approaches cannot model studies that compare combinations of treatments in a single node (e.g., A+B vs. C+D)

Dose-effect models do not always explain inconsistency. Inconsistency may still be evident due to an imbalance in the distribution of effect modifiers across comparisons NMA models require consistency in at least one (e.g., treatment) level.

Consistency should be evaluated at each level it is assumed

An IPD-NMA is the optimal approach to explore these factors. Hence, it

is imperative to advance NMA models addressing dose-effects using IPD, improving interpretability of NMA results

But….

Studies are needed to establish and advance IPD-NMA

addressing dose-effects to make results more useful for decision-making


Future Work Develop dose-response NMA models allowing to impose structural

relationships between the basic parameters Use reduced forms of standard NMA models where all doses and treatments are

conceptualized as separate treatments (accounting for between study within dose level variance)

Potentially introduce fractional polynomials in these models

Develop hierarchical models that relax the consistency assumption in dose and

treatment levels Use higher dimension design specific inconsistency parameters than the standard

NMA models, so that all studies receive appropriate inconsistency parameters related to their treatment-doses

Extend all models including the between-dose-category level (e.g., low,

recommended, high category of doses) We are currently working on the statistical code for the random dose-effects within

different dose categories model

Extend dose-response NMA models including IPD.


1. Del Giovane C, Vacchi L, Mavridis D, Filippini G, Salanti G. Network meta-analysis models to account for variability in treatment definitions: application to dose effects. Statistics in Medicine 2013; 32:25–39

2. Donegan S, Williamson P, D'Alessandro U, et al. Assessing the consistency assumption by exploring treatment by covariate interactions in mixed treatment comparison meta-analysis: individual patient-level covariates versus aggregate trial-level covariates. Statistics in medicine 2012;31(29):3840-57.

3. Nikolakopoulou A, Chaimani A, Veroniki AA, Vasiliadis HS, Schmid CH, Salanti G. PLoS One. 2014 22;9(1):e86754. doi: 10.1371/journal.pone.0086754.

4. Owen RK, Tincello DG, Keith RA. Network meta-analysis: development of a three-level hierarchical modeling approach incorporating dose-related constraints. Value Health 2015 18(1):116-26. doi: 10.1016/ j.jval.2014.10.006.

5. Salanti G, Marinho V, Higgins JP. A case study of multiple-treatments meta-analysis demonstrates that covariates should be considered. J Clin Epidemiol. 2009;62(8):857-64

6. Spiegelhalter DJ BN, Carlin BP, Van Der Linde A. Bayesian measures of model complexity and fit. J R Stat Soc Ser B Stat Methodol 2002;64(64):57

7. Tricco AC, Soobiah C, Blondal E, Veroniki AA, Khan PA, Vafaei A, et al. Comparative safety of serotonin (5-HT3) receptor antagonists in patients undergoing surgery: A systematic review and network meta-analysis, BMC Medicine. 2015; 13:136

8. Thomas S, Lillie E, Adams J, et al. Anti–vascular endothelial growth factor (VEGF) agents for the treatment of retinal conditions. CADTH Therapeutic Review, 2015

9. Veroniki AA, Mavridis D, Higgins JP, Salanti G. Statistical evaluation of inconsistency in a loop of evidence: a simulation study informed by empirical evidence. BMC Medical Research Methodology 2014; 14:106

10. Veroniki AA, Straus SE, Fyraridis A, Tricco AC. The rank-heat plot is a novel way to present the results from a network meta-analysis including multiple outcomes. J. Clin. Epidemiol. 2016; pii: S0895-4356(16)00153-0

11. Warren FC,AbramsKR,SuttonAJ.Hierarchical Bayesian networkmeta-analysis models to address sparsity of events and differing treatment classifications with regard to adverse outcomes. Stat Med 2014;33:2449–66.

12. White IR, Barret JK, Jackson D, Higgins JPT. Consistency and inconsistency in multiple treatments meta-analysis: model estimation using multivariate meta-regression. Research Synthesis Methods 2012;3(2):111-25.

References…


Special thanks to:

• My supervisor and mentors:

Dr. Sharon Straus, Dr. Andrea Tricco

• Co-authors:

Dr. Cinzia Del Giovane, Dr. Daniel Jackson, Ms. Sonia Thomas, Mr. Erik Blondal, Dr. Kednapa Thavorn

• CIHR Banting Postdoctoral Fellowship Program

E-mail: [email protected]

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