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ADDRESSING BETWEEN-STUDY HETEROGENEITY AND INCONSISTENCY IN MIXED TREATMENT COMPARISONS

ADDRESSING BETWEEN-STUDY HETEROGENEITY AND INCONSISTENCY IN MIXED TREATMENT COMPARISONS Application to stroke prevention treatments for Atrial Fibrillation patients. Nicola Cooper , Alex Sutton, Danielle Morris, Tony Ades, Nicky Welton. MIXED TREATMENT COMPARISON.

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ADDRESSING BETWEEN-STUDY HETEROGENEITY AND INCONSISTENCY IN MIXED TREATMENT COMPARISONS

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  1. ADDRESSING BETWEEN-STUDY HETEROGENEITY AND INCONSISTENCY IN MIXED TREATMENT COMPARISONS Application to stroke prevention treatments for Atrial Fibrillation patients. Nicola Cooper, Alex Sutton, Danielle Morris, Tony Ades, Nicky Welton

  2. MIXED TREATMENT COMPARISON • MTC - extends meta-analysis methods to enable comparisons between all relevant comparators in the clinical area of interest. Option 1: Two pairwise M-A analyses (A v C, B v C) Option 2: MTC (A v B v C) provides probability each treatment is the ‘best’ of all treatments considered for treating condition x. A B C

  3. HETEROGENIETY & INCONSISTENCY • As with M-A need to explore potential sources of variability: i) Heterogeneity - variation in treatment effects between trials within pairwise contrasts, and ii) Inconsistency - variation in treatment effects between pairwise contrasts • Random effect - allows for heterogeneity but does NOT explain it nor ensure inconsistency is addressed • Incorporation of study-level covariates can reduce both heterogeneity and inconsistency by allowing systematic variability between trials to be explained

  4. OBJECTIVE • To extend the MTC framework to allow for the incorporation of study-level covariates • 3 potential models: • Independent treatment x covariate interactions for each treatment compared to placebo • Exchangeable treatment x covariate interactions for each treatment compared to placebo • Common treatment x covariate interactions for each treatment compared to placebo

  5. EXAMPLE NETWORK 2 A B Stroke prevention treatments for Atrial Fibrillation patients (18 trials) A = Placebo B = Low dose anti-coagulant C = Standard dose anti-coagulant D = Standard dose aspirin 7 2 4 1 10 C D Covariate = publication date (proxy for factors relating to change in clinical practice over time)

  6. MTC random effects model rjk = observed number of individuals experiencing an event out of njk; pjk = probability of an event; jb= log odds of an event in trial j on ‘baseline’ treatment b; jbk= trial-specific logodds ratio of treatment k relative to treatment b;dbk= pooled log odds ratios; σ2= between study variance

  7. MODEL 1: Independent regression coefficient for each treatment NOTE: Relative treatment effects for the active treatment versus placebo are allowed to vary independently with covariate; thus,ranking of effectiveness of treatments allowed to vary for different covariate values

  8. MODEL 2: Exchangeable regression coefficient

  9. MODEL 3: Common regression (slope) coefficient Note: Relative treatment effects only vary with the covariate when comparing active treatments to placebo.

  10. FULL 17 TRT NETWORK 17 treatments 25 trials 60 data points

  11. FULL 17 TRT NETWORK: Issues • Model becomes over-specified as number of parameters to be estimated approaches or exceeds the number of data points available • For example, Model 1 (Independent ‘betas’) would require estimation of: • 25 baselines • 16 treatment means + random effects • 16 regression coefficients • 1 between-study variance

  12. FULL 17 TRT NETWORK: Options • Assume treatment x covariate interactions exchangeable or common within treatment classes • For example, • Anti-coagulants, Anti-platelets, Both

  13. Indobufen Dipyridamole Ximelagatran 1 1 Dipyridamole & Low Dose Aspirin Placebo / No treatment 1 Clopidogrel & Low Dose Aspirin 1 2 1 4 Alternate Day Low Dose Aspirin 1 1 1 Adjusted Standard Dose Warfarin 2 1 4 1 3 1 4 2 Aspirin Diff Doses 3 2 2 Adjusted Low Dose Warfarin 2 Medium Dose Aspirin 1 Fixed Low Dose Warfarin 1 1 1 High Dose Aspirin Fixed Low Dose Warfarin & Low Dose Aspirin Fixed Low Dose Warfarin & Medium Dose Aspirin white = Anti-coagulant, dark grey = Anti-platelet, black = Mixed (Anti-coagulant + Anti-platelet), light grey = Placebo/no treatment

  14. FULL 17 TRT NETWORK: Options • Assume treatment x covariate interactions exchangeable or common within treatment classes • For example, • Anti-coagulants, Anti-platelets, Both • Simplify treatment network through covariate modelling • For example, • model different doses of same drug using covariates • assume effect of combinations of drugs additive (on scale of analysis)

  15. Indobufen Dipyridamole Ximelagatran 1 1 Dipyridamole & Low Dose Aspirin Placebo / No treatment 1 Clopidogrel & Low Dose Aspirin 1 2 1 4 Alternate Day Low Dose Aspirin 1 1 1 Adjusted Standard Dose Warfarin 2 1 4 1 3 1 4 2 Aspirin Diff Doses 3 2 2 Adjusted Low Dose Warfarin 2 Medium Dose Aspirin 1 Fixed Low Dose Warfarin 1 1 1 High Dose Aspirin Fixed Low Dose Warfarin & Low Dose Aspirin Fixed Low Dose Warfarin & Medium Dose Aspirin Assume a dose-response relationship across aspirin regimens

  16. Indobufen Dipyridamole Ximelagatran 1 1 Dipyridamole & Low Dose Aspirin Placebo / No treatment 1 Clopidogrel & Low Dose Aspirin 1 2 1 4 1 1 Adjusted Standard Dose Warfarin 2 1 9 7 Aspirin (Doses) 2 2 2 Adjusted Low Dose Warfarin 2 1 1 2 Fixed Low Dose Warfarin 1 Fixed Low Dose Warfarin & Low Dose Aspirin Fixed Low Dose Warfarin & Medium Dose Aspirin Assume a dose-response relationship across aspirin regimens

  17. Indobufen Dipyridamole Ximelagatran 1 1 Dipyridamole & Low Dose Aspirin Placebo / No treatment 1 Clopidogrel & Low Dose Aspirin 1 2 1 4 1 1 Adjusted Standard Dose Warfarin 2 1 9 7 Aspirin (Doses) 2 2 2 Adjusted Low Dose Warfarin 2 1 1 2 Fixed Low Dose Warfarin 1 Fixed Low Dose Warfarin & Low Dose Aspirin Fixed Low Dose Warfarin & Medium Dose Aspirin Assume effect of aspirin is additive when given in combination

  18. Indobufen Ximelagatran 1 Dipyridamole & possible Aspirin (Doses) Placebo / No treatment 2 Clopidogrel & Low Dose Aspirin 1 2 4 1 2 Adjusted Standard Dose Warfarin 2 1 9 7 Aspirin (Doses) 2 2 Adjusted Low Dose Warfarin 2 4 2 • Reduced 16 treatments to • 9 groupings • Strong assumptions made • that need exploring • Work in progress Fixed Low Dose Warfarin & possible Aspirin (Doses) 1 Assume effect of aspirin is additive when given in combination

  19. FULL 17 TRT NETWORK: Options • Treatment x covariate interactions exchangeable or common within treatment classes • For example, • Anti-coagulants, Anti-platelets, Both • Simplify treatment network through covariate modelling • For example, • model different doses of same drug using covariates • assume effect of combinations of drugs additive (on scale of analysis) • Combination of the above. Lots of possibilities!

  20. DISCUSSION • Number of different candidate models - especially for large treatment networks often with limited data • Need to be aware of limitations posed by available data & importance of ensuring model interpretability and relevance to clinicians • Uncertainty in the regression coefficients and the treatment differences not represented on graphs (which can be considerable) • Results from MTC increasingly used to inform economic decision models. Incorporation of covariates may allow separate decisions to be made for individuals with different characteristics

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