A Novel Semi-Automated Approach for Trial Identification and Evaluation of the Certainty of Evidence from Network Meta-Analyses

Simple item page
dc.contributor.advisorHazlewood, Glen
dc.contributor.advisorDeardon, Rob
dc.contributor.authorKamso, Mohammed Mujaab
dc.contributor.committeememberSajobi, Tolulope
dc.contributor.committeememberTomlinson, George
dc.date2024-11
dc.date.accessioned2024-09-11T14:01:38Z
dc.date.available2024-09-11T14:01:38Z
dc.date.issued2024-09-09
dc.description.abstractThis 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.
dc.identifier.citationKamso, 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.
dc.identifier.urihttps://hdl.handle.net/1880/119674
dc.language.isoen
dc.publisher.facultyGraduate Studies
dc.publisher.institutionUniversity of Calgary
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectRandomized controlled trials (RCTs)
dc.subjectCrowdsourcing
dc.subjectMachine learning
dc.subjectRheumatoid arthritis
dc.subjectSystematic reviews
dc.subjectLiving systematic reviews
dc.subjectAutomation
dc.subject.classificationBiostatistics
dc.subject.classificationStatistics
dc.subject.classificationEpidemiology
dc.titleA Novel Semi-Automated Approach for Trial Identification and Evaluation of the Certainty of Evidence from Network Meta-Analyses
dc.typedoctoral thesis
thesis.degree.disciplineMedicine – Community Health Sciences
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameDoctor of Philosophy (PhD)
ucalgary.thesis.accesssetbystudentI require a thesis withhold – I need to delay the release of my thesis due to a patent application, and other reasons outlined in the link above. I have/will need to submit a thesis withhold application.
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
ucalgary_2024_kamso_mohammed.pdf
Size:
4.83 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2.64 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Restricted Theses and Dissertations