Projects per year
Objective(s): We extend the ML-NMR framework to handle individual-level likelihoods of any 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.
Method(s): We show how the individual-level likelihood function conditional on the covariates is integrated over the covariate distributions in the AgD studies to obtain the respective marginal likelihood contributions. Integration is performed numerically using quasi-Monte Carlo integration, allowing for general application regardless of model form or covariate distributions.
Results: Joint synthesis of ordered categorical outcomes lead to increased precision compared to separate models and avoided computational difficulties due to few events on higher outcomes. ML-NMR achieved similar fit to a random effects Non of effect estimates relevant to the decision target population.MA, but 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.
Conclusions: 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|Publication status||Unpublished - 17 Jul 2019|
|Event||40th Annual Conference of the International Society for Clinical Biostatistics - KU Leuven, Leuven, Belgium|
Duration: 14 Jul 2019 → 18 Jul 2019
Conference number: 40
|Conference||40th Annual Conference of the International Society for Clinical Biostatistics|
|Period||14/07/19 → 18/07/19|
FingerprintDive into the research topics of 'Synthesis of individual and aggregate level data using multilevel network meta-regression: extension to general likelihoods'. Together they form a unique fingerprint.
- 3 Finished
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
- 1 Participation in conference
David M Phillippo (Speaker)15 Jul 2019 → 17 Jul 2019
Activity: Participating in or organising an event types › Participation in conference