BACKGROUND: Individual patient data (IPD) meta-analysis is the gold standard. Aggregate data (AD) and IPD can be combined using conventional pairwise meta-analysis when IPD cannot be obtained for all relevant studies. We extend the methodology to combine IPD and AD in a mixed treatment comparison (MTC) meta-analysis. METHODS: The proposed random-effects MTC models combine IPD and AD for a dichotomous outcome. We study the benefits of acquiring IPD for a subset of trials when assessing the underlying consistency assumption by including treatment-by-covariate interactions in the model. We describe three different model specifications that make increasingly stronger assumptions regarding the interactions. We illustrate the methodology through application to real data sets to compare drugs for treating malaria by using the outcome unadjusted treatment success at day 28. We compare results from AD alone, IPD alone and all data. RESULTS: When IPD contributed (i.e. either using IPD alone or combining IPD and AD), the chains converged, and we identified statistically significant regression coefficients for the interactions. Using IPD alone, we were able to compare only three of the six treatments of interest. When models were fitted to AD, the treatment effects and regression coefficients for the interactions were far more imprecise, and the chains did not converge. CONCLUSIONS: The models combining IPD and AD encapsulated all available evidence. When exploring interactions, it can be beneficial to obtain IPD for a subset of trials and to combine IPD with additional AD.
BACKGROUND: Individual patient data (IPD) meta-analysis is the gold standard. Aggregate data (AD) and IPD can be combined using conventional pairwise meta-analysis when IPD cannot be obtained for all relevant studies. We extend the methodology to combine IPD and AD in a mixed treatment comparison (MTC) meta-analysis. METHODS: The proposed random-effects MTC models combine IPD and AD for a dichotomous outcome. We study the benefits of acquiring IPD for a subset of trials when assessing the underlying consistency assumption by including treatment-by-covariate interactions in the model. We describe three different model specifications that make increasingly stronger assumptions regarding the interactions. We illustrate the methodology through application to real data sets to compare drugs for treating malaria by using the outcome unadjusted treatment success at day 28. We compare results from AD alone, IPD alone and all data. RESULTS: When IPD contributed (i.e. either using IPD alone or combining IPD and AD), the chains converged, and we identified statistically significant regression coefficients for the interactions. Using IPD alone, we were able to compare only three of the six treatments of interest. When models were fitted to AD, the treatment effects and regression coefficients for the interactions were far more imprecise, and the chains did not converge. CONCLUSIONS: The models combining IPD and AD encapsulated all available evidence. When exploring interactions, it can be beneficial to obtain IPD for a subset of trials and to combine IPD with additional AD.
Authors: Vakaramoko Diaby; Askal A Ali; Georges Adunlin; Christine G Kohn; Alberto J Montero Journal: Curr Med Res Opin Date: 2016-03-02 Impact factor: 2.580
Authors: Howard H Z Thom; Gorana Capkun; Annamaria Cerulli; Richard M Nixon; Luke S Howard Journal: BMC Med Res Methodol Date: 2015-04-12 Impact factor: 4.615
Authors: Catrin Tudur Smith; Maura Marcucci; Sarah J Nolan; Alfonso Iorio; Maria Sudell; Richard Riley; Maroeska M Rovers; Paula R Williamson Journal: Cochrane Database Syst Rev Date: 2016-09-06
Authors: Catrin Tudur Smith; Kerry Dwan; Douglas G Altman; Mike Clarke; Richard Riley; Paula R Williamson Journal: PLoS One Date: 2014-05-29 Impact factor: 3.240