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Abstract
Multipletreatments metaanalyses are increasingly used to evaluate the relative effectiveness of several competing regimens. In some fields which evolve with the continuous introduction of new agents over time, it is possible that in trials comparing older with newer regimens the effectiveness of the latter is exaggerated. Optimism bias, conflicts of interest and other forces may be responsible for this exaggeration, but its magnitude and impact, if any, needs to be formally assessed in each case. Whereas such novelty bias is not identifiable in a pairwise metaanalysis, it is possible to explore it in a network of trials involving several treatments. To evaluate the hypothesis of novel agent effects and adjust for them, we developed a multipletreatments metaregression model fitted within a Bayesian framework. When there are several multipletreatments metaanalyses for diverse conditions within the same field/specialty with similar agents involved, one may consider either different novel agent effects in each metaanalysis or may consider the effects to be exchangeable across the different conditions and outcomes. As an application, we evaluate the impact of modelling and adjusting for novel agent effects for chemotherapy and other nonhormonal systemic treatments for three malignancies. We present the results and the impact of different model assumptions to the relative ranking of the various regimens in each network. We established that multipletreatments metaregression is a good method for examining whether novel agent effects are present and estimation of their magnitude in the three worked examples suggests an exaggeration of the hazard ratio by 6 per cent (2–11 per cent).
Translated title of the contribution  Evaluating novel agent effects in multiple treatments metaregression 

Original language  English 
Pages (fromto)  2369  2383 
Number of pages  15 
Journal  Statistics in Medicine 
Volume  29 
DOIs  
Publication status  Published  Oct 2010 
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Dive into the research topics of 'Evaluating novel agent effects in multiple treatments metaregression'. Together they form a unique fingerprint.Projects
 1 Finished

COLLABORATION AND INNOVATION IN DIFFICULT OR RANDOMISED CONTROLLED TRIALS
1/04/09 → 1/04/14
Project: Research