Projects per year
Abstract
Background: Routinely collected healthcare data provides a rich environment for the investigation of drug performance in the general population, while also offering the possibility of assessing rare outcomes. The statistical analysis of this data poses a number of challenges. The data may be biased and lack the structure and balance provided by the drugs’ clinical trials. Outcomes are often modelled individually with an associated lack of control for multiple comparisons, as well as a difficulty in assessing multiple risks.
Methods: Bayesian models provide methods for analysing multiple clinical outcomes, using relationships between outcomes and handling the types of multiple comparison issues which may occur when using multiple single-variate approaches. Lack of balance within the data may be catered for by dividing the population into clusters with similar characteristics, allowing within cluster inferences to be made. A Bayesian hierarchical model for multiple outcomes is proposed and applied to data from a safety and effectiveness study of direct oral anticoagulants (DOACs) in Scotland 2009 – 2015.
Results: The Bayesian modelling results were comparable to the results from the original safety and effectiveness study, with the additional benefit of balancing patient clusters and controlling for relationships in the data.
Conclusion: Bayesian hierarchical models are a suitable approach for modelling routinely collected healthcare data. There is the possibility of moving to an integrated Bayesian approach, with the inclusion of treatment relationships; uncertainty regarding cluster membership; and treatment allocation in the model, eventually leading to more reliable treatment decisions.
Methods: Bayesian models provide methods for analysing multiple clinical outcomes, using relationships between outcomes and handling the types of multiple comparison issues which may occur when using multiple single-variate approaches. Lack of balance within the data may be catered for by dividing the population into clusters with similar characteristics, allowing within cluster inferences to be made. A Bayesian hierarchical model for multiple outcomes is proposed and applied to data from a safety and effectiveness study of direct oral anticoagulants (DOACs) in Scotland 2009 – 2015.
Results: The Bayesian modelling results were comparable to the results from the original safety and effectiveness study, with the additional benefit of balancing patient clusters and controlling for relationships in the data.
Conclusion: Bayesian hierarchical models are a suitable approach for modelling routinely collected healthcare data. There is the possibility of moving to an integrated Bayesian approach, with the inclusion of treatment relationships; uncertainty regarding cluster membership; and treatment allocation in the model, eventually leading to more reliable treatment decisions.
Original language | English |
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Number of pages | 1 |
Publication status | Published - 6 Mar 2020 |
Event | EuroDURG 2020 - Szeged, Hungary Duration: 3 Mar 2020 → 7 Mar 2020 |
Conference
Conference | EuroDURG 2020 |
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Country/Territory | Hungary |
City | Szeged |
Period | 3/03/20 → 7/03/20 |
Keywords
- Bayesian hierarchy
- safety
- direct oral anticoagulants (DOACs)
- stratification
- clusters
Fingerprint
Dive into the research topics of 'Bayesian hierarchical approaches for multiple outcomes in routinely collected healthcare data'. Together they form a unique fingerprint.Projects
- 1 Finished
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Rutherford Fund Fellowship at HDR UK: Risk Prediction in Pharmacoepidemiology
Bennie, M. (Co-investigator), Robertson, C. (Co-investigator) & Carragher, R. B. (Fellow)
MRC (Medical Research Council)
14/02/18 → 21/08/21
Project: Research Fellowship
Datasets
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Bayesian Hierarchical Possion Models for Mulitple Grouped Outcomes with Clustering - Simulation Study
Carragher, R. B. (Creator), University of Strathclyde, 12 May 2020
DOI: 10.15129/c8a0433e-042a-4088-9c4b-323943f14953
Dataset