Abstract
Purpose: To develop a model-based meta-anaysis (MBNMA) framework that allows for non-linear modelling of multi-parameter time-course functions for comparative effectiveness, and can account for residual correlation between observations using a multivariate likelihood.
Method(s): Model-based meta-analysis (MBMA) is a technique increasingly used in drug development for synthesising results from multiple studies, allowing pooling of information on treatment, dose-response and time-course characteristics, which are often non-linear. Such analyses are used in drug development to inform future trial designs. Network meta-analysis (NMA) is used frequently in Health Technology Appraisals and by reimbursement agencies for simultaneously comparing effects of multiple treatments. Recently, a framework for dose-response MBNMA has been proposed that draws strengths from both MBMA and NMA.
We expand this framework for modelling of time-course functions that allows for the inclusion of observations from multiple study time points. This methodology preserves randomisation by aggregating within-study relative effects and, by modelling consistency equations on the time-course parameters, it allows for testing of inconsistency between direct and indirect evidence. We demonstrate our modelling framework using an illustrative dataset of 24 trials investigating treatments for pain in osteoarthritis.
Result(s): For our dataset, we report results from 10 different models. An Emax function allowed for the greatest degree of flexibility, both in the time-course shape and in the specification of time-course parameters (Emax and ET50). Our final model had a posterior mean residual deviance of 291.4 (compared to 345 data points), indicating a good fit to the data. Some simplifying assumptions were needed to identify ET50, as studies contained few observations at earlier follow-up times. Treatment estimates were robust to the choice of likelihood (univariate/multivariate), suggesting that accounting for residual correlation between time points may not be essential if the time-course function has been appropriately modelled and the parameters of interest are summary treatment estimates.
Conclusion(s): Time-course MBNMA combines strengths from MBMA and NMA to allow inclusion of multiple study time points into analyses whilst preserving randomisation and allowing for testing of inconsistency. This has the potential to be used both in helping design and predict studies in drug development, as well as for decision-making by reimbursement agencies, and can act as a bridge between early phase clinical research and Health Technology Appraisal.
Method(s): Model-based meta-analysis (MBMA) is a technique increasingly used in drug development for synthesising results from multiple studies, allowing pooling of information on treatment, dose-response and time-course characteristics, which are often non-linear. Such analyses are used in drug development to inform future trial designs. Network meta-analysis (NMA) is used frequently in Health Technology Appraisals and by reimbursement agencies for simultaneously comparing effects of multiple treatments. Recently, a framework for dose-response MBNMA has been proposed that draws strengths from both MBMA and NMA.
We expand this framework for modelling of time-course functions that allows for the inclusion of observations from multiple study time points. This methodology preserves randomisation by aggregating within-study relative effects and, by modelling consistency equations on the time-course parameters, it allows for testing of inconsistency between direct and indirect evidence. We demonstrate our modelling framework using an illustrative dataset of 24 trials investigating treatments for pain in osteoarthritis.
Result(s): For our dataset, we report results from 10 different models. An Emax function allowed for the greatest degree of flexibility, both in the time-course shape and in the specification of time-course parameters (Emax and ET50). Our final model had a posterior mean residual deviance of 291.4 (compared to 345 data points), indicating a good fit to the data. Some simplifying assumptions were needed to identify ET50, as studies contained few observations at earlier follow-up times. Treatment estimates were robust to the choice of likelihood (univariate/multivariate), suggesting that accounting for residual correlation between time points may not be essential if the time-course function has been appropriately modelled and the parameters of interest are summary treatment estimates.
Conclusion(s): Time-course MBNMA combines strengths from MBMA and NMA to allow inclusion of multiple study time points into analyses whilst preserving randomisation and allowing for testing of inconsistency. This has the potential to be used both in helping design and predict studies in drug development, as well as for decision-making by reimbursement agencies, and can act as a bridge between early phase clinical research and Health Technology Appraisal.
| Original language | English |
|---|---|
| Publication status | Unpublished - Jun 2018 |
| Event | Society of Medical Decision Making - 17th Biennial European Conference - Leiden, Netherlands Duration: 10 Jun 2018 → 12 Jun 2018 Conference number: 17 https://smdm.org/meeting/17th-biennial-european-conference |
Conference
| Conference | Society of Medical Decision Making - 17th Biennial European Conference |
|---|---|
| Country/Territory | Netherlands |
| City | Leiden |
| Period | 10/06/18 → 12/06/18 |
| Internet address |