title: Evaluating novel agent effects in multiple-treatments meta-regression.
abstract: Multiple-treatments meta-analyses 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 pair-wise meta-analysis, 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 multiple-treatments meta-regression model fitted within a Bayesian framework. When there are several multiple-treatments meta-analyses for diverse conditions within the same field/specialty with similar agents involved, one may consider either different novel agent effects in each meta-analysis 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 non-hormonal 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 multiple-treatments meta-regression 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).
authors.affiliation: Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece. gsalanti@cc.uoi.gr
authors.forename: Georgia
authors.initials: G
authors.lastname: Salanti
authors.forename: Sofia
authors.initials: S
authors.lastname: Dias
authors.forename: Nicky J
authors.initials: NJ
authors.lastname: Welton
authors.forename: A E
authors.initials: AE
authors.lastname: Ades
authors.forename: Vassilis
authors.initials: V
authors.lastname: Golfinopoulos
authors.forename: Maria
authors.initials: M
authors.lastname: Kyrgiou
authors.forename: Davide
authors.initials: D
authors.lastname: Mauri
authors.forename: John P A
authors.initials: JP
authors.lastname: Ioannidis
mesh_terms: D000970:Antineoplastic Agents / Q000627:therapeutic use*; D000971:Antineoplastic Combined Chemotherapy Protocols / Q000627:therapeutic use*; D001499:Bayes Theorem; D015982:Bias; D001943:Breast Neoplasms / Q000188:drug therapy*; D015179:Colorectal Neoplasms / Q000188:drug therapy*; D005260:Female; D006801:Humans; D015201:Meta-Analysis as Topic; D015999:Multivariate Analysis; D010051:Ovarian Neoplasms / Q000188:drug therapy*; D016032:Randomized Controlled Trials as Topic; D016896:Treatment Outcome
chemical_list: D000970:Antineoplastic Agents