Dynamic CER · Dynamic Comparative Effectiveness Research for health care interventions
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2021-11-01 → 2023-10-31
- EU contribution
- €184,708
- Participants
- 1
- Scheme
- MSCA-IF
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Results in brief
Dynamic Comparative Effectiveness Research for health care interventions
This fellowship was aimed to provide two main contributions to the current state-of-the-art in comparative effectiveness research (CER) of healthcare interventions, with a special focus on so-called complex interventions (typical examples of complex interventions include psychological interventions for mental illness or non-pharmacological interventions to support behavioural change in digital health). First, the fellowship investigated dynamic regimes of complex just-in-time adaptive interventions (JITAIs) in primary research. Specifically, novel use of data from JITAIs to dynamically evaluate response to treatment are proposed. Second, the fellowship contributed to the field of evidence synthesis by developing novel methodology for both complex and non-complex interventions, as well as software to dynamically update network meta-analyses results in a user-friendly and timely manner. All this addresses important limitations of current CER methodologies, which are to date not well developed to take into account the temporal evolution of treatment effects. In turn, this can also enable a more effective and precise health decision and policy making in the near future.
Data: CORDIS, © European Union
Project objective
Comparative effectiveness research (CER) has recently emerged as a key component of health care decision-making, specifically designed to provide evidence of the effectiveness of different health care treatments. The latter are becoming increasingly complex, and there is now intense interest in deploying advanced statistical tools to study and guide the development of such complex systems. Complex interventions might also involve interactions through time, where actions in the past affect the future decision making context. In this case, the temporal dimension should be taken into account into the statistical model, yielding in turn more precise guidelines for health care decision-making. However, current CER methodologies are not well-suited to understand such complex systems or characterise their future behaviour. Statistical methods based on dynamic modelling are therefore needed to advance progress of the state-of-the art of CER research. In particular, this fellowship will tackle the problem by developing novel statistical methodologies for the study of the temporal dynamics of complex health care interventions, both for primary research and evidence synthesis. For primary research, the fellowship will explore the emerging case of pervasive and technology-based interventions, which are by nature dynamic. For evidence synthesis - which is the procedure of summarising evidence from different primary studies of a specific health condition - the innovative tool of network meta-analysis will be deployed and an extension for dynamically monitoring and updating the results of existing network meta-analyses will be developed.
Original text from CORDIS.
Participants
- UNIVERSITE PARIS CITE · ParisCoordinatorFrance
Links
Data: CORDIS, © European Union