Calibration of multiple treatment comparisons using individual patient data

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There are well established methods for comparing multiple treatments based on a synthesis of all the trials that compare any two or more of them. These "network meta-analysis" methods are routinely used in NICE appraisals, NICE Clinical Guidelines, health technology assessment in general, and with growing frequency in Cochrane Collaboration reviews. These methods do, however, assume that the distribution of effect modifiers is the same in the included trials. Such assumptions are difficult to verify, but if they are not met results are likely to be biased. As a result, there has been recent interest in methods that appear to address these concerns by using IPD from one trial to adjust the between-treatment comparisons. These methods are being seen with increasing frequency in the applied health technology assessment literature, and also in submissions to NICE. They are particularly attractive to manufacturers, who have access to IPD from their own trials but not to trials on competitor products. The methods that have been published have limited scope, and their reliability has not been assessed. Of still greater concern is that they are also being applied to non-randomised (one-arm) studies: this is a radical departure from standard practice because it makes assumptions that have always been regarded as implausible.Using simulation studies, and real examples from industry partners, we propose to develop and evaluate new methods that can use IPD from one or more trials to adjust treatment comparisons across an entire network of comparisons, and to provide improved comparative effectiveness estimates that are relevant to any specified target population. We will assess and compare the reliability of or the methods we plan to develop with standard methods, and with the methods now appearing in the literature, and we will check the validity of the various assumptions made by the different methods, using real data.