Treatment effectiveness in multimorbidity: Combining efficacy estimates from clinical trials with the natural history obtained from large routine healthcare databases to determine net overall treatment benefits. (360G-Wellcome-201492_Z_16_Z)

This research will develop and validate a novel method for estimating net overall treatment benefits (effectiveness) for patients with multimorbidity, using Bayesian Evidence Synthesis (BES). Clinical guideline development routinely uses evidence synthesis, which combines estimates of relative treatment efficacy (RTE) from clinical trials with standard treatment comparator (natural history) event rates from observational data to estimate effectiveness in target populations. Bayesian approaches to evidence synthesis can accommodate more complex modelling, but methods to address the increased complexities of multimorbidity have not been developed. This research will address the clinical and methodological barriers to this use of BES. Key Research Goals: 1\. Summarise parametrically the variation in RTE according the presence and severity of comorbid diseases for a range of drug-groupings, outcome-types and comorbidities 2\. For an exemplar indication, combine these summaries (1) with published trial results of specific treatment comparisons to produce comorbidity-specific estimates of RTE, and validate this approach empirically 3\. For the same exemplar, show how these comorbidity-specific efficacy estimates (2) can be combined with administrative-data derived natural history rates, via BES, in order to produce comorbidity-specific estimates of effectiveness 4\. To encourage the adoption of this novel method via software tools and documentation