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Estimation and adjustment of bias in randomized evidence by using mixed treatment comparison meta-analysis

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  • S. Dias
  • N. J. Welton
  • V. C. C. Marinho
  • G. Salanti
  • J. P. T. Higgins
  • A. E. Ades
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    Abstract

    There is good empirical evidence that specific flaws in the conduct of randomized controlled trials are associated with exaggeration of treatment effect estimates. Mixed treatment comparison meta-analysis, which combines data from trials on several treatments that form a network of comparisons, has the potential both to estimate bias parameters within the synthesis and to produce bias-adjusted estimates of treatment effects. We present a hierarchical model for bias with common mean across treatment comparisons of active treatment "versus" control. It is often unclear, from the information that is reported, whether a study is at risk of bias or not. We extend our model to estimate the probability that a particular study is biased, where the probabilities for the 'unclear' studies are drawn from a common beta distribution. We illustrate these methods with a synthesis of 130 trials on four fluoride treatments and two control interventions for the prevention of dental caries in children. Whether there is adequate allocation concealment and/or blinding are considered as indicators of whether a study is at risk of bias. Bias adjustment reduces the estimated relative efficacy of the treatments and the extent of between-trial heterogeneity. Copyright (c) 2010 Royal Statistical Society.

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    Bibliographic Info

    Article provided by Royal Statistical Society in its journal Journal of the Royal Statistical Society: Series A (Statistics in Society).

    Volume (Year): 173 (2010)
    Issue (Month): 3 ()
    Pages: 613-629

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    Handle: RePEc:bla:jorssa:v:173:y:2010:i:3:p:613-629

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