A Novel Semi-Automated Approach for Trial Identification and Evaluation of the Certainty of Evidence from Network Meta-Analyses
Date
2024-09-09
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Abstract
This thesis introduces an innovative approach for the rapid identification of randomized controlled trials (RCTs) and evaluation of the certainty of evidence within the context of a living systematic review and network meta-analysis. The first paper (Chapter 4) describes a living systematic review methodology that incorporates crowd-sourcing, machine learning and a web-based tool to streamline the identification and classification of RCTs, introducing a novel "studification" process to enhance review maintenance. The second paper (Chapter 5) presents a semi-automated method for evaluating the certainty of evidence derived from direct estimates within a Bayesian network meta-analysis framework, adhering to GRADE guidance. The study also addresses the assessment of indirectness at the study-specific level using online tools. The final paper (Chapter 6) extends this methodology to assess the certainty of evidence for indirect and mixed evidence separately. This is achieved through a semi-automated process that utilizes the concept of the contribution matrix to identify the first-order loop, highlighting the primary contributors to indirect estimate. Additionally, in accordance with GRADE recommendations, an automated approach for evaluating imprecision is developed. Overall, this thesis may enhance the efficiency of maintaining a living systematic review, offering a novel approach to semi-automate the evaluation of evidence certainty from Bayesian network meta-analysis models while adhering to GRADE guidelines. Applied to the context of early rheumatoid arthritis, the findings potentially have positive policy implications such as how fast reviews can be done for clinical practice guideline development.
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Randomized controlled trials (RCTs), Crowdsourcing, Machine learning, Rheumatoid arthritis, Systematic reviews, Living systematic reviews, Automation
Citation
Kamso, M. M. (2024). A novel semi-automated approach for trial identification and evaluation of the certainty of evidence from network meta-analyses (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.