ADDRESSING BETWEENSTUDY HETEROGENEITY AND INCONSISTENCY IN MIXED TREATMENT COMPARISONS

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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
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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
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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 ensure inconsistency is addressed
• Incorporation of study-level covariates can
  reduce both heterogeneity and inconsistency by
  allowing systematic variability between trials to
  be explained

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OBJECTIVE

• To extend the MTC framework to allow for the
  incorporation of study-level covariates
• 3 models
• Different regression coefficient for each
  treatment
• Exchangeable regression coefficient
• Common regression (slope) coefficient

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EXAMPLE NETWORK
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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
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2
1
4
10
C
D
Covariate publication date (proxy for factors
relating to change in clinical practice over
time)
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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
log odds ratio of treatment k relative to
treatment b dbk pooled log odds ratios s2
between study variance
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MODEL 1 Different 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
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MODEL 2 Exchangeable regression coefficient
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MODEL 3 Common regression (slope) coefficient
Note Relative treatment effects only vary with
the covariate when comparing active treatments to
placebo.
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FULL 17 TRT NETWORK
17 treatments 25 trials 60 data points
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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
• e.g. Model 1 - requires estimation of 25
  baselines, 16 treatment means, 16 regression
  coefficients, between-study variance ( random
  effects).
• May be sensible to consider treatments within
  classes
• e.g. Anti-coagulant, Anti-platelet, Both
• Best fitting model exchangeable treatment x
  covariate effects by class
• Reference Cooper NJ, Sutton AJ, Morris D, Ades
  AE, Welton NJ. Addressing between-study
  heterogeneity and inconsistency in mixed
  treatment comparisons Application to stroke
  prevention treatments in individuals with
  non-rheumatic Atrial Fibrillation. Submitted to
  Statistics in Medicine

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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