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Slide 1: Network meta-analyses: potential and pitfalls Peter Jüni Institute of Social and Preventive Medicine (ISPM) and CTU Bern, University of Bern, Switzerland

Slide 2: Outline  Own views  Food for thought  Assumptions  Pitfalls

Slide 3: PJ’s views  Frequentist  Randomised controlled trials  Pragmatic comparisons  Simple analytical strategies

Slide 7: Early thrombolysis for AMI RCTs SK t-PA At-PA Sk+tPA r-PA TNK 8 P P 1 P P P 1 P P 1 P P 2 P P 1 P P 6 treatments: Streptokinase (SK), Tissue-plasminogen activator (t-PA), Accelerated tissue-plasminogen activator (At- PA), Tenecteplase (TNK), Reteplase (r-PA); 14 trials; 15 possible pairwise comparisons Boland et al, Health Technology Assessment, 2003

Slide 8: Meta-analysis: results from direct comparisons Treatment comparison Trials Odds Ratio CIs SK versus t-PA 8 1.00 0.94 to 1.06 At-PA vs TNK 1 0.99 0.88 to 1.13 At-PA vs r-PA 2 1.24 0.61 to 2.53 r-PA vs SK 1 0.94 0.79 to 1.12 4 comparisons actually made; 12 trials used in analysis

Slide 9: Results: Pairwise odds ratios SK t-PA Acc t-PA SK + t-PA r-PA TNK SK 1.00 0.94 t-PA At-PA 1.24 0.99 SK + t-PA r-PA TNK

Slide 10: Conclusions  Streptokinase is as effective as non-accelerated alteplase …  Tenecteplase is as effective as accelerated alteplase …  Reteplase is at least as effective as streptokinase …  … (is) streptokinase as effective as, or inferior to accelerated alteplase?  … is reteplase as effective as accelerated alteplase or not?  … two further questions … arise, whether tenecteplase is superior to streptokinase or not and whether reteplase is as effective as tenecteplase or not

Slide 11: Results: Pairwise odds ratios (upper triangle – direct comparisons, lower triangle – NWMA) SK t-PA Acc t-PA SK + t-PA r-PA TNK SK ** 1.00 0.86 0.96 0.95 t-PA 1.00 ** Acc t-PA 0.87 0.87 ** 1.12 1.02 1.01 SK + t-PA 0.96 0.97 1.11 ** r-PA 0.90 0.91 1.04 0.94 ** TNK 0.87 0.88 1.01 0.91 0.97 ** (Results from fixed effects NWMA and standard pairwise meta-analysis).

Slide 12: Probability treatment x is ‘best’: Clinical effectiveness (35 day mortality) Fixed effect 35 day Probability best Mortality % SK 6.5 0% t-PA 6.4 0% Acc t-PA 5.6 40% SK + t-PA 6.2 1% r-PA 5.8 15% TNK 5.6 43%

Slide 14: Jüni et al, Lancet 2004

Slide 15: Relative risk of myocardial infarction I2=0% Jüni et al, Lancet 2004

Slide 21: Fisher‘s exact test  P = 0.036 (1-sided)  Conventional level of significance: 0.025  Weak to moderate evidence for a harmful effect of treatment ...

Slide 22: Cytokine storm in inviduals receiving TGN1412: Fisher‘s exact test Prior Distribution (uniform) 0.0 0.2 0.4 0.6 0.8 1.0 Event Probability p

Slide 23: Cytokine storm in inviduals receiving placebo: Fisher‘s exact test Prior Distribution (uniform) 0.0 0.2 0.4 0.6 0.8 1.0 Event Probability p

Slide 24: Cytokine storm in inviduals receiving TGN1412: Bayesian approach Prior Distribution (uniform) 0.0 0.2 0.4 0.6 0.8 1.0 Event Probability p

Slide 25: Cytokine storm in inviduals receiving placebo: Bayesian approach Prior Distribution 0.0 0.2 0.4 0.6 0.8 1.0 Event Probability p

Slide 26: Bayesian approach  P < 0.0001 (2-sided)  Strong to overwhelming evidence for a harmful effect of treatment ...

Slide 29: 38 randomised controlled trials in 18,023 patients Bare Metal Stents 16 comparisons 7 comparisons 4,992 patients 4,312 patients n=18,023 Patients Sirolimus Paclitaxel Eluting Stents Eluting Stents 14 comparisons 7,893 patients

Slide 30: TLR: SES vs BMS Prior Distribution (uniform) 0.001 1 1000 Hazard ratio

Slide 31: TLR: PES vs BMS Prior Distribution (uniform) 0.001 1 1000 Hazard ratio

Slide 32: TLR: SES vs PES Prior Distribution (uniform) 0.001 1 1000 Hazard ratio

Slide 33: TLR: SES vs BMS Risk ratio Study (95% CI) % Weight Pache et al. 0.67 ( 0.45, 1.00) 13.4 DIABETES 0.27 ( 0.12, 0.62) 5.5 PRISON II 0.21 ( 0.07, 0.58) 3.9 SCANDSTENT 0.10 ( 0.04, 0.29) 4.1 HR 0.40 (95%-CI 0.32–0.51) SESAMI 0.38 ( 0.16, 0.87) 5.5 SIRIUS 0.39 ( 0.28, 0.54) 15.5 E-SIRIUS 0.32 ( 0.18, 0.58) 9.1 C-SIRIUS 0.31 ( 0.09, 1.07) 2.9 TYPHOON 0.32 ( 0.17, 0.58) 8.7 RAVEL 0.47 ( 0.21, 1.05) 5.9 SES-SMART 0.37 ( 0.18, 0.77) 6.9 DECODE 0.39 ( 0.14, 1.11) 3.9 SCORPIUS 0.29 ( 0.11, 0.74) 4.6 RRISC 0.68 ( 0.29, 1.60) 5.3 Ortolani et al 0.59 ( 0.23, 1.50) 4.7 Overall 0.38 ( 0.30, 0.48) 100.0 0.04 .038611 1 Risk ratio 25 25.8987 Favours PES Favours BMS

Slide 34: TLR: PES vs BMS Risk ratio Study (95% CI) % Weight TAXUS I 0.15 ( 0.01, 2.82) 0.5 TAXUS II 0.40 ( 0.23, 0.68) 11.6 HRIV 0.58 (95%-CI 0.46–0.72) TAXUS 0.44 ( 0.32, 0.60) 27.1 TAXUS V 0.66 ( 0.51, 0.87) 32.8 TAXUS VI 0.61 ( 0.39, 0.95) 16.3 PASSION 0.71 ( 0.38, 1.32) 9.3 HAAMU 0.36 ( 0.10, 1.27) 2.4 Overall 0.54 ( 0.44, 0.66) 100.0 0.008 .008156 1 125 122.595 Risk ratio Favours PES Favours BMS

Slide 35: TLR: SES vs BMS Posterior distribution 0.01 1 10

Slide 36: TLR: PES vs BMS Posterior distribution 0.01 1 10

Slide 37: TLR: Hazard ratio for SES vs PES (indirect) 0.40 = 0.69 0.58

Slide 38: TLR: SES vs PES (indirect) Posterior distribution 0.01 1 10

Slide 39: TLR: SES vs PES (direct) Posterior distribution 0.01 1 10

Slide 40: TLR: SES vs PES (network) Posterior distribution 0.01 1 10

Slide 41: Prerequisites/Assumptions effects MA Random- NWMA  Log RR behave additively Yes Yes  Log RR from same common Yes Yes distribution  Model fits the data Yes ?  Heterogeneity between trials Yes Yes low  Inconsistency of network low Yes ?

Slide 42: Prerequisites/Assumptions effects MA Random- NWMA  Log RR behave additively Yes Yes  Log RR from same common Yes Yes distribution  Model fits the data Yes ?  Heterogeneity between trials Yes Yes low  Inconsistency of network low

Slide 43: Prerequisites/Assumptions effects MA Random- NWMA  Log RR behave additively Yes Yes  Log RR from same common Yes Yes distribution  Model fits the data Yes ?  Heterogeneity between trials Yes Yes low  Inconsistency of network low

Slide 44: 38 randomised controlled trials in 18,023 patients Bare Metal Stents 16 comparisons 7 comparisons 4,992 patients 4,312 patients n=18,023 Patients Sirolimus Paclitaxel Eluting Stents Eluting Stents 14 comparisons 7,893 patients

Slide 45: Prerequisites/Assumptions effects MA Random- NWMA  Log RR behave additively Yes Yes  Log RR from same common Yes Yes distribution  Model fits the data Yes ?  Heterogeneity between trials Yes Yes low  Inconsistency of network low Yes ?

Slide 46: TLR: SES vs BMS Risk ratio Study (95% CI) % Weight Pache et al. 0.67 ( 0.45, 1.00) 13.4 DIABETES 0.27 ( 0.12, 0.62) 5.5 PRISON II 0.21 ( 0.07, 0.58) 3.9 SCANDSTENT 0.10 ( 0.04, 0.29) 4.1 HR 0.40 (95%-CI 0.32–0.51) SESAMI 0.38 ( 0.16, 0.87) 5.5 SIRIUS 0.39 ( 0.28, 0.54) 15.5 E-SIRIUS 0.32 ( 0.18, 0.58) 9.1 C-SIRIUS 0.31 ( 0.09, 1.07) 2.9 TYPHOON 0.32 ( 0.17, 0.58) 8.7 RAVEL 0.47 ( 0.21, 1.05) 5.9 SES-SMART 0.37 ( 0.18, 0.77) 6.9 DECODE 0.39 ( 0.14, 1.11) 3.9 SCORPIUS 0.29 ( 0.11, 0.74) 4.6 RRISC 0.68 ( 0.29, 1.60) 5.3 Ortolani et al 0.59 ( 0.23, 1.50) 4.7 Overall 0.38 ( 0.30, 0.48) 100.0 0.04 1 25 Risk ratio Favours PES Favours BMS

Slide 47: Pitfalls

Slide 48: Prerequisites/Assumptions effects MA Random- NWMA  Log RR behave additively Yes Yes  Log RR from same common Yes Yes distribution  Model fits the data Yes ?  Heterogeneity between trials Yes Yes low  Inconsistency of network low Yes ?

Slide 49: Garbage in – garbage out

Slide 50: Black box Trials NWMA Prior 1 Prior 2 Results

Slide 51: I2 = 92%

Slide 52: Heterogeneity between trials

Slide 53: Network consistency

Slide 54: 38 randomised controlled trials in 18,023 patients Bare Metal Stents 16 comparisons 7 comparisons 4,992 patients 4,312 patients n=18,023 Patients Sirolimus Paclitaxel Eluting Stents Eluting Stents 14 comparisons 7,893 patients

Slide 55: Network consistency

Slide 56: Goodness of fit (1) Residual deviance, defined as the difference between the deviance for the fitted model and the deviance for the saturated model

Slide 57: Goodness of fit (2)

Slide 58: Conclusions  Potential of clinically useful syntheses of evidence  Same pitfalls as in traditional meta- analyses  Additional work needed re assumptions

Slide 59: Acknowledgements  Simon Wandel  Sven Trelle  Marcel Zwahlen  Debbie Caldwell  Georgia Salanti  Tony Ades