CTU Bern, Bern University Hospital & Institute of Social and Preventive Medicine (ISPM)

Transcription

1 10 TH ANNIVERSARY OF THE CHMG Network Meta-Analyses Sven Trelle CTU Bern, Bern University Hospital & Institute of Social and Preventive Medicine (ISPM)

2 Outline > Standard meta-analyses > Principles of network meta-analyses > Presentation -2-

3 Standard meta-analyses What we do everyday > First-line treatment of CLL F C H O P -3-

4 Standard meta-analyses What we do everyday Study Risk ratio (95%-CI) Trial ( 0.22 to 1.26) Trial ( 0.61 to 1.05) Trial ( 0.59 to 0.98) Trial ( 0.28 to 0.80) Trial ( 0.62 to 3.49) Overall 0.73 ( 0.57 to 0.93) Risk ratio -4-

5 Standard meta-analyses Assumptions > Included trials are comparable > Model fits the data > Relative treatment effects behave additively > Relative treatment effects from common distribution Standard Multicenter trials -5-

6 Standard meta-analyses Investigate similarity > Clinically Participant characteristics, interventions, > Methodologically Quality of allocation, analysis, > Other Outcome reporting bias, Publication bias > Statistically Quantify heterogeneity (random-effects model) e.g. I 2 = 36%, tau 2 (D-L) =

7 First-line treatment of CLL Ch Ch P C O P F C H O P Ch F C H P F C F C R Ch R -7-

8 First-line treatment of CLL > Solution?! Network meta-analysis Integration of direct and indirect evidence in one analysis while fully preserving randomization -8-

9 Three-arm trial: amethocaine > Amethocaine vs. placebo vs. no anesthetic > Injection pain (VAS 0-100) [mean difference] > A versus P (A-P) -12 A+ > A versus N (A-N) 2 N+ > N versus P (N-P) -14 N+ P A N -9-

10 Three-arm trial: amethocaine > Amethocaine vs. placebo vs. no anesthetic > Injection pain (VAS 0-100) [mean difference] > A versus P (A-P) -12 A+ > A versus N (A-N) 2 N+ > N-P indirect =(A-P)-(A-N)=-P+N -12-2=-14 N+ > Three-arm: -14 (N+) P P A A N N -10-

11 Direct and indirect comparisons > Direct evidence P is equal to N A is better than P > Informal indirect evidence A is better than N > Formal indirect evidence AN = PN PA P A N -11-

12 Direct and indirect comparisons > Direct evidence P is equal to N A is better than P > Informal indirect evidence A is better than N > Formal indirect evidence AN = PN PA Not feasible in networks with > 3 interventions Integration of direct and indirect evidence not feasible P A P A B N N -12-

13 Mixed treatment comparisons > Integration of direct and indirect evidence in one analysis while fully preserving randomization > Based on relative treatment effects (logrr, loghr, logor, mean difference) derived from direct comparisons > Can deal with > 3 interventions Trials with > 2 treatment groups > Usually implemented in a Bayesian framework -13-

14 Example of a network Golfinopoulos et al

15 Integration of direct and indirect evidence Golfinopoulos et al

16 Integration of direct and indirect evidence Golfinopoulos et al

17 Integration of direct and indirect evidence Golfinopoulos et al

18 Integration of direct and indirect evidence Golfinopoulos et al

19 Network meta-analysis Assumptions > Relative treatment effects behave additively > Model fits the data > Included trials are comparable > Included trials are exchangable > Relative treatment effects from common distribution Network Standard Multicenter trials -19-

20 Confounding in indirect comparisons I 100% B Mortality 50% C A B A = B B > C A > C 0% Adapted from Baker & Kramer

21 Confounding in indirect comparisons II 100% 100% B Mortality 50% C A B 50% C A B 0% 30% 80% Received effective supportive care 0% 30% 80% Received effective supportive care Adapted from Baker & Kramer

22 Selective reporting Golfinopoulos et al. 2007

23 Direct versus indirect evidence Direct evidence Indirect evidence C C A B B -23-

24 Inconsistency Direct evidence C Discrepency Direct vs. Indirect ICF [eg loghr] C Indirect evidence A B B -24-

25 Criticism > Their statistical models are so complex that many are mystified by whether the conclusions make sense. > As if by magic, the network meta-analysis pushes the SES vs PES comparison for myocardial infarction to p= > This might be squeezing the data too hard. > I am concerned about the increasing reliance on random-effects methods. > Another concern is that such meta-analysis combines evidence from trials that are substantially different. Pocock 2007

26 Why bother? > Because we get confused when more than two treatment options are available > Because network meta-analysis may result in increased statistical precision > Because network meta-analysis may increase our understanding of inconsistencies > Because we perform informal indirect comparisons anyway -26-

27 Informal indirect comparisons > German CLL Study Group CLL4: F vs. FC FC>F CLL8: FC vs. FCR FCR>FC & implicit FCR>F CLL10: FCR vs. BR? -27-

28 Network meta-analysis Conclusions > Potential of clinically useful syntheses of evidence > Methods more complex, less transparent > Shares many limitations with traditional meta-analyses, but these may be less evident > Important to show direct meta-analyses in detail, including trial quality, comparators, study populations > Additional methodological work needed

29 Presentation Chemotherapy for advanced CRC I Golfinopoulos et al

30 Presentation Chemotherapy for advanced CRC II Golfinopoulos et al

31 Presentation Chemotherapy for advanced CRC III > True probability statements possible > Probability to be best first-line regimen 68%: 5-FU-based + irinotecan + bevacizumab > Ranking Rank 1 (95%-CI: 1 to 4): 5-FU-based + irinotecan + bevacizumab Golfinopoulos et al

32 Presentation Multiple outcomes -32-

33 Presentation Drug-eluting stents Stettler et al

34 Presentation Posterior probabilities A: Myocardial infarction Probability to be below threshold 0% 10% 33% 50% 66% 90% 100% Naproxen Ibuprofen Diclofenac Celecoxib Etoricoxib Rofecoxib Lumiracoxib Rate ratio