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We extend ML-NMR to handle individual-level likelihoods of general form, illustrating with two examples – a real network of plaque psoriasis treatments with ordered categorical outcomes, and a simulated comparison of time-to-event outcomes.
We show how the individual-level likelihood function conditional on the covariates is integrated over the covariate distributions in each AgD study to obtain the respective marginal likelihood contributions. Quasi-Monte Carlo numerical integration is used, making application general.
Joint synthesis of ordered categorical outcomes lead to increased precision compared to separate models. ML-NMR achieved better fit than a random effects NMA, uncertainty was substantially reduced, and the model was more interpretable. For the simulated survival data, ML-NMR agreed closely with the known truth, with little loss of precision from a full IPD analysis.
ML-NMR is a flexible and general method for synthesising evidence from mixtures of individual and aggregate level data in networks of all sizes. Extension to general likelihoods, including for survival outcomes, greatly increases the applicability of the method. Decision making is aided by the production of effect estimates relevant to the decision target population.
|Publication status||Unpublished - 5 Sep 2019|
|Event||Royal Statistics Society International Conference 2019 - ICC Belfast, Belfast, United Kingdom|
Duration: 2 Sep 2019 → 5 Sep 2019
|Conference||Royal Statistics Society International Conference 2019|
|Abbreviated title||RSS 2019|
|Period||2/09/19 → 5/09/19|
1/03/17 → 29/02/20
1/01/15 → 30/09/18
Phillippo, D. M., 28 Nov 2019
Supervisor: Welton, N. (Supervisor) & Dias, S. (Supervisor)
Student thesis: Doctoral Thesis › Doctor of Philosophy (PhD)File